Demand for Athena, the recently released voice-based OS, is confirmed to be strong:

Since Athena’s launch for enterprise customers 130 days ago, more than 40% of our super-scaled customers are already monthly users. And together, they’ve generated thousands of campaigns using Athena. And customers who comprehensively adopt our AI, using it for audience creation, activation and other means, contribute a disproportionate share of revenue. This shows up in several ways.

For those who have worked in a B2B software sales role, the extraordinary result of this will be clear. When you launch a new product, it normally takes months to get someone to buy in for a pilot, and it can take years before the related sales have a meaningful impact on revenue.

Nobody likes to be the first, unless the ROI is crystal clear and provable.

Zeta, in less than 6 months, has been able to get almost half of its biggest customers to try or use it.

Athena is having a great impact on engagement too:

Over the last 60 days, Athena’s voice users are interacting with the platform at a 500% higher level than non-Athena users.

This is great for two reasons. The first one is the obvious one: higher ROI, as more users are able, as explained by David several times, to use the platform better and deeper, getting more juice out of it. This ultimately turns into higher retention, more spending on utilization fees and eventually buying new features.

The less obvious one is that as the UI becomes less relevant, the value accrues more and more on the ROI which the system delivers, and for which Zeta is recognized as the leader in their domain.

This is also a further tailwind for Zeta’s M&A strategy: buying a new company and integrating it into the value proposition is easier and easier if the UI is not a thing.

Zeta Business Intelligence

But let’s go to the main point of my article, which is about Zeta Business Intelligence and its partnership with Palantir.

What was once called Business Intelligence is, in my opinion, where 90% of software value will accrue within the next 5 to 10 years.

Software, UI and UX, once the bulk of a software company’s value, will be worth almost nothing, as they can already be generated almost for free.

Ecosystems (customers, partners, distribution, ...) still matter a lot, I do agree on this. But let’s keep them aside for a moment.

What products should they be moving into retail? How do they use co-op dollars and marketing dollars to get better shelf space and how to create deeper and more meaningful relationships with the end customer? So it starts with the business intelligence around a retail use case, you then end up identifying meaningful opportunities for them to move inventory around geographically, which retailers they should be doing more with, maybe which retailers they should invest less with, and then we end up being able to market to the end user to drive them into the retailer on behalf of the product and vice versa.

These are the kind of questions which, if answered in an accurate, data-driven way, can really move the needle.

When a decision is strategic and not only tactical, the consequence of such a decision is spread across the organization and the supply chain, and the difference between making a good or a less good decision is measured in millions of dollars.

Zeta understood it for a long time, but what is changing now is that they are enlarging the pie, because it is becoming clear that the utility of ZETA’s marketing ontology is not limited to marketing. It can deliver tremendous value if paired with an ontology of the rest of the business, by answering questions which extend beyond the marketing domain but which require information from the Zeta domain to be answered.

This is David Steinberg giving an example during the call:

And if you look at this very large sports league that is using us, we were in the room with them and their CMO and they’re like, listen, how do we better negotiate our streaming rights with the two partnerships that are coming up for bid and how can we equate viewership and mind share for their—they own multiple products, multiple leagues that are owned by one holding corporation.

How do we help them to better show it’s not just who’s watching it in that moment, but it’s the halo effect that comes for the broadcaster by having them on? And we were able to put together a solution in hours that came back and they believe will result in millions of dollars of incremental revenue to them on the renegotiation of those streaming contracts. So it then becomes a flywheel because then we get part of the marketing for those streaming rights. So it really becomes a major flywheel in and around the company...

And what is the best way to accomplish an extended ontology, which is worth much more than the two single ontologies taken alone, if not joining Zeta’s ontology with the king of all ontologies?

And here we are, a partnership between Zeta and Palantir, which was announced in June.

As it should be clear to everyone by now, Palantir is becoming the new OS for businesses which aren’t joking, and Zeta understood it so clearly that it re-architected its Data Cloud on top of Palantir’s Foundry and adopted its ontology.

Our partnership with Palantir significantly expands Zeta’s enterprise opportunity. Palantir provides the ontology, governance, and enterprise AI infrastructure that large organizations require. Zeta contributes proprietary customer intelligence, proprietary data, identity, decisioning and activation. And we have reached two important milestones in this partnership already. Our data cloud was fully integrated with Foundry as of July 31. And we have already received multiple agreements for our initial combined sale with several other meaningful opportunities in flight.

said David Stainberg.

I believe that the ROI the two companies can provide to customers is not even comparable to a standard software company, and we are already starting to see the first proofs of it coming in. These are still two initial combined-sale engagements and test cases, so the sample is tiny:

The two deals we closed were at a 100% hit rate. Met with two, closed two.

A 100% hit rate even though on two opportunities is simply insane.

Conclusion

The surprises from Zeta are unlikely to end here. I expect more over the coming quarters.

On April 28 I opened a position in Zeta at the price of 18,26 $ per share, as I shared in real time in the Business Ontology community.

Hence, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since I opened my position.

+67% DDTD

If you want access to:

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• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

Here are the reasons why I think it just makes a lot of sense.

It is going to be the largest hyperscaler and it is cheap

Management is guiding to about $100B ARR (including Space, Connectivity and AI) by the end of the year, which at the price I bought is around 15x Price to ARR. For a company which can “easily” keep growing double digits for several years, it’s not a high multiple.

Elon is projecting compute capacity closer to 10 GW than 5 GW by the end of 2027. At pricing in the latest disclosed leases - about $50M per MW on the Anthropic/Colossus 1 deal, with Google paying $920M a month for a large GPU block — and assuming part of the capacity will be used internally (Grok training and inference), this can generate, together with the other businesses, $300B+ ARR by the end of 2027 (SemiAnalysis estimate, not company guidance). If that happens, at the price I bought, SpaceX stock will have a 5x Price to ARR multiple at the end of 2027. Pretty cheap.

Elon and the SpaceX team have already proved they can build and bring compute capacity online faster than anyone else, with 122 days to bring Colossus 1’s ~300MW online and Colossus 2 ramping even faster toward multi-hundred-MW and gigawatt-scale clusters. There are no reasons to believe they won’t keep delivering. The historical disadvantage compared to other hyperscalers was the smaller balance sheet, compared to the enormous AI capex bill.
Elon recently said they will build exclusively on Nvidia’s Vera Rubin stack, which I think may help with supply and partnership economics, with Nvidia financing part of the deal.

Tesla dilution

I own Tesla. It’s around 7% of the Lorenzo2cents portfolio at the time I write.

Tesla and SpaceX are more and more integrated and their destinies are tied. Think about Terafab, Grok for Tesla’s cars, SpaceX tech for Tesla Roadster, Tesla solar panels and batteries for SpaceX datacenters, Tesla’s Optimus for moon colonization, and more certainly to come.

I think they will merge, eventually. It would make any synergy easier, including for Elon’s governance.

One of the biggest concerns about this obvious merge is the potential dilution of Tesla shareholders, as Elon will want to maximize his control of the merged company and somehow will make a better deal for SpaceX rather than Tesla. Elon’s ownership of SpaceX is higher: he has voting control — roughly four-fifths of the votes through super-voting shares — while his Tesla stake is only around 15% of shares.

One way to mitigate this risk is to own both $SPCX and $TSLA, ideally in equal parts. This way, if you are going to be diluted on one side, you are the recipient of that benefit on the other side and the result is neutral. Of course, for this to make sense, you need to get SpaceX at a reasonable price before the merge becomes priced in, which I think I did.

Grok

Grok is basically valued at zero so far, and one could say fairly, as it has been far from frontier-model intelligence and far from capturing a meaningful market share among enterprises or consumers, with Anthropic and OpenAI hitting all the headlines and benchmarks, for good reasons.

I think this is going to change and Grok is now underestimated.

Grok 4.6, released on August 12, is back in the frontier conversation. It is competitive on some agent/coding and cost-performance comparisons, even if it is not clearly #1 versus Claude Opus 5 on the main public leaderboards. Grok 4.7 is expected next - Elon has pointed to roughly early September after the 4.6 launch - and he is promising another push toward the top of the frontier.

It will likely be leapfrogged by Anthropic after a few weeks or days, so likely xAI is still behind at least 3 months to the leading frontier lab.

But:

• Elon is now back in the race and he doesn’t like to be the second at anything, so I would not be surprised to see Grok taking the lead in a few months.

• xAI will likely have the largest compute available to any frontier lab for training new models, which is a big advantage

• Elon disclosed that xAI/SpaceX is putting decades of SpaceX engineering data into upcoming Grok training, which in my view can give it an unfair advantage against competition, positioning it to become the go-to model for specific engineering domains.

• I don’t see why, eventually, Tesla’s engineering data shouldn’t also be used to train Grok models, reinforcing its lead in the engineering domain

• X data were and will increasingly be an advantage for training new models as Grok catches up

• I expect Tesla cars and Optimus data will also be used to reinforce Grok’s understanding of the physical world

• Owned distribution channels for Grok, beyond Grok apps: Cursor, X, Tesla’s cars, Optimus (forward looking).

All in all, I just see it as likely for Grok to become one of the leading models, and with Anthropic and OpenAI already valued near or targeting the trillion-dollar tier, this is not priced in yet.

Space and Connectivity

The revenue generated by Space (launches and satellite services) and Connectivity (Starlink) is clearly already priced in, including the discounting of future growth.

But the value of these two assets resides elsewhere, beyond the revenue.

These two capabilities are highly synergic with everything else Elon is doing and are basically positioning him to have an unfair advantage in the long term. Here are some examples:

• Elon/Tesla are integrating Starlink into Cybercab (the purpose-built robotaxi). Tesla has already shown Starlink V5 built into the design, which should help connectivity and fleet operations even if autonomy itself still runs on the car. It will likely do the same with Optimus and then with all Tesla’s cars. Can competitors do the same? Sure, but for now they need to rely on Starlink to accomplish that. Even when competition catches up, they will still be paying a fee to whoever supplies the service.

• Orbital datacenters may be 5 or 10 years away, or more, before becoming anything meaningful, but yet, I think they will happen. I consider this an upside, but it alone could be worth more than any other company’s product or service in the world right now.

• SpaceX is the gatekeeper to space, which I believe will become, together with AI and longevity, the biggest industry in the world. Even when Rocket Lab, the only credible competitor (which by the way I own), successfully scales Neutron, the competitor to Falcon 9, SpaceX will still be shipping most of the mass to orbit with Starship and will be the only one able to enable specific use cases like orbital data centers.

Conclusion

I believe $SPCX can be a $10 trillion company in 5 to 10 years, and I believe its downside is limited at this valuation, while it also hedges my $TSLA position. This makes it a good fit for the Lorenzo2cents portfolio.

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

Before doing that, let me highlight one positive insight from the first release of the new AI features to some cohorts: AI is making the service more engaging and improving retention. This is excellent news because it derisks the whole thesis. One of the most critical factors for Hims’ success is increasing subscribers and scale. Once it has scale and organic growth, almost everything else follows as a consequence.

… we spoke around how with a more immersive experience through the next evolution of the app and some of the AI tools as well as physical tools like the scale of the consumers are getting, that enables more connectivity with their providers. And ultimately, we’re already seeing signs of lower cancellation rates for the cohorts of users that are receiving that. And as we look to continue invest in the experience, and we see opportunities as we gain more efficiencies to make price points more affordable, we see an already solid retention rate getting stronger and stronger.

On the same topic:

Patients are interacting and messaging with their care teams 3x as much with the new care operating plan and operating system that we’ve rolled out. And at the same time, the actual tasks for humans has dropped 50% when you’re talking about nonclinical tasks

Hims Is Planning to Become the Backbone of the Healthcare Industry

We are excited to bring the experience to more of our customers across the world, and we think we are moving closer to a future where the model we are building becomes the backbone for health platforms beyond Hims & Hers.

Mohamed ElShenawy, the CTO recently hired to build the Hims AI infrastructure, explained this during his AI update.

This is not a random comment that can be dismissed as buzzwords or hype to reinforce the AI thesis. This is Hims explaining in plain English what the company’s ultimate goal is as it works to accomplish its mission.

Becoming the backbone of the healthcare industry.

As I have envisioned since my first Hims write-up more than one year ago, and reiterated in my Q1 update, Hims is an AI company, and its value is in the closed loop. I will not describe it again; just read the paragraph “Hims is an AI company and providers are training the model” in my previous article.

Hims is actually pushing it even further, trying not only to create the only vertical ontology for healthcare, but also to create the layer where all healthcare actors will operate.

Andrew Dudum elaborated on it better later in the call:

…building the operating system and kind of this AI platform that can connect to pharmacists, a doctor, care coaches, agents all together in that environment, we thought was the best place to start

Having the best longevity and healthcare AI model in the world - which they will, if they can keep executing for a few years - is already a strong incentive, but it is not enough. Big Pharma is not moved by good intentions. It is moved by making more and more money, whatever the outcome for patients.

Andrew understood this, of course, and is trying to make them happy enough to join the system:

• Hims’ platform becomes a distribution layer for Big Pharma (see the Novo partnership). The bigger it gets, the higher the FOMO to get in.

• Hims does not compete with Big Pharma because it quit mass compounding.

The next obvious step, in my view, will be for Hims to sell anonymized data to Big Pharma and biotech companies for their R&D.

• What are the most common and painful problems suffered by patients?

• How much are patients willing to spend on them?

• What are the most likely solutions to such problems—APIs, proteins, molecules, and so on?

• What is the addressable market?

Such information is worth billions.

Here is Andrew telling you in plain English during the call, when addressing the Novo partnership:

I think there’s an increasing amount of collaboration around key strategic markets that are valuable to both businesses and thinking about how to collaborate into expanding the market and getting people more access I think there is increasing ability to share data with regard to what’s working for patients, adherence benefits, what we’re seeing with regard to side effects or how to mitigate that in different dosing regimens.

So I think there’s a really powerful flywheel here when you start getting the leading drug manufacturers actually closer to the data on the ground of the consumers in a way that can not only help when it comes to commercializing therapies but also thinking through how we bring new therapies to market. And so I think there’s a growing set of opportunities that I think both of the teams are very excited by.

The Freemium Model

I think this also sets up a really powerful freemium offering for Hims & Hers, where patients could just have the opportunity to come leverage these tools at Hims & Hers in a free manner to just get benefits of the 360 view of their health, a unified platform with an ecosystem, all powered by AI on the bottom layer of actual doctors, actual trained nutritionalists as well as agents and care pilots. And so when you really step back, like what is this that we’re building is currently available. This is what most would consider concierge care today. And it costs anywhere from $50,000 to $150,000 annually. I think in the simplest way, my vision is to make that same level of proactive, preventative, always-on care affordable to everybody globally for a price that is universally accessible.

Andrew is basically thinking of delivering a Duolingo-like model, where anyone can join for free, dump all their health problems into the app - every person on Earth has a lot of them - chat with AI about them, and actually get contextualized answers instead of only a recommendation to consult with a doctor.

You may be able to use the Hims app for free to track your diet progress and blood analysis, and get useful insights and recommendations.

In other words, a no-brainer.

From there to buying a product or service, the step is very short.

Combined with the already good marketing ROI Hims is delivering, this could be a total game changer for Hims’ top of the funnel.

Conclusion

As I correctly predicted two quarters ago, growth is reaccelerating. After a beat and raise in Q2, I expect another beat and raise in Q3.

Why do I say this? Because I noticed some patterns in how management presented the guidance. I will point you to my post on X for more details.

The only thing I did not like about Q2 was management not being fully transparent about guidance and subscriber numbers excluding Eucalyptus. This is not to diminish the importance of the Eucalyptus acquisition or to say that it was not a good thing. It was. But it makes it impossible to track organic growth and retention, which is crucial for a company like Hims that is betting the house on its ability to acquire new customers and retain them.

Besides that, I think we will have a good end to the year. Peptides are coming, the Novo partnership is bringing a lot of new business, and the international business is growing fast. We could not ask for better.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the February 8th 2025, when the stock price was $42.54.

-36% DDTD

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

This is my opinion, of course, and I can be wrong, so do not take this as financial advice.

Developing a thesis about an investment, owning it and monitoring it is tougher than one might think. That’s why I have developed my method, which I call the Business Ontology Framework.

It helps me think from first principles and identify the relevant metrics to monitor that are, as much as possible, leading indicators of how the thesis is unfolding.

I am going to explain in this short write-up what I think matters and why. First, my thesis: Lemonade is going to become an autonomous company, and this will allow a winner-takes-all scenario.

I’ll keep it simple and short here, as you can find a lot more details by checking my past articles on the matter.

Follow me in my first-principles thinking here. To become an autonomous company and thrive in the insurance market, Lemonade needs to:

1. Make human employees more and more productive over time, let’s say at least 2 times more productive than the most productive competitor

2. Be able to acquire customers profitably for decades

3. Be able to retain customers

4. Keep risks under control

That’s it. Really, that’s it.

If Lemonade keeps doing these four things better than others, it will become a $100B company in less than ten years, and a $1T company in less than 15 years.

Everything else doesn’t really matter. It’s noise.

Let’s now look at how to assess if Lemonade is on track to deliver in Q2 and onward.

Make human employees more and more productive over time

There is really one simple metric here that tells us if Lemonade is on track, and it is

In Force Premium (IFP)/Employee

Here is how it printed in Q2 and its trend over the last quarters.

It must keep going up. Anything else doesn’t matter. Period.

You may see headcount or salaries increasing; it doesn’t really matter. In the long run, what matters to validate the autonomous company thesis is how productive employees are.

How much they are leveraged by technology.

Financials will inevitably follow, eventually.

Now, the IFP/employee trend is for sure a leading indicator, but if we want an even better one, it is probably the LAE ratio. Even though it is less complete, as it covers only part of the business, it is a useful proxy for claims-handling efficiency. Lemonade says the improvement is being driven by the growing use of its OS and AI across claims operations, so I see it as evidence that automation is increasingly supporting the business.

If Lemonade is achieving this trend in claims management, we can reasonably assume that something similar is happening in other areas of the business, such as risk management and customer acquisition.

This is how the LAE ratio trended over time and printed in Q2.

Of course, the lower the better. Currently, it is already one of the best in the industry, if not the best.

Be able to acquire customers profitably for decades

This is crucial, even more so for a D2C business, because if Lemonade were to lose the ability to acquire new customers profitably, it would be a survival issue. Lemonade would likely need to change its go-to-market, starting to rely on human agents, the same as its competitors, which of course would invalidate the thesis.

Just think about what happened to Oddity Tech ($ODD). I recommend every Lemonade investor learn about it, as it is a good case study to understand how important this ability is for Lemonade.

The best indicator of this ability is

Lifetime Value/Customer Acquisition Cost (LTV/CAC)

This value has consistently been held at approximately 3x, which is a healthy level, as reported by management.

I see two positive trends here, both driven by scale.

As Lemonade becomes bigger and operates in more geographies and with more products, its ability to cross-sell improves. This means the same customers will buy more products, which means a higher LTV.

If we look at the denominator, CAC, the same driver helps. More customers, more products and more geographies mean more data and more targets, and therefore a higher probability of finding highly rewarding and efficient campaigns to run. To understand this point, you can read how they deal with marketing campaigns here (from an old report):

Going deeper, the second quarter saw the launch of our 6th generation Lifetime Value machine learning model (”LTV6”). This is our most advanced, credible, nuanced and precise LTV prediction model to date (a synthesis of several models, to be precise).

For each customer we on board, LTV6 offers a prediction of their likelihood to churn, claim, and cross sell - and from these it derives a predicted lifetime value. This is the projected net stream of dollars, discounted for the time value of money, from each customer (i.e., all premiums, minus all claims, adjusted for net present value).

Once they reach statistical significance, each product, each market, and each campaign is likewise scored using LTV6. After adding the customer acquisition cost (CAC) into the formula, the resultant LTV/CAC, together with the CAC Payback Period, determine which product, market, and campaign receives the incremental dollar spend.

This is what Shai said about it during the last earnings call:

We seek to increase LTV through sustained momentum in cross-sells, which can drive gains in retention. And we seek to improve CAC efficiency through more granular AI-driven pricing, which can provide a tailwind to conversion rates. We continue to focus on these key drivers that we believe can drive sustainable profitable growth.

Being able to retain customers

This metric is strictly correlated with the previous one, as it is the other side of the coin. Indeed, every customer retained is worth the same as a customer acquired (or even more as it does not require CAC). In other words, 1 is worth 1: either you gain it or you lose it. And better retention has a positive impact on LTV and vice versa.

Annual Dollar Retention (ADR) is the king metric here.

It held stable sequentially at 85%,

…continuing to reflect the impact of our prior clean the book actions within our homeowners product line. And as a reminder, ADR is measured relative to prior year’s IFP. So, while those portfolio actions are now largely behind us, they will continue to impact the reported ADR metric for the next couple of quarters before rolling out of the comparison period.

Management is basically saying that we will see ADR improving in a couple of quarters.

Where can it go?

Its peak of 88% was reached in Q1 2024, and the drop was due to the “clean the book” actions, based on what we know from management. This makes me think it can go back to that level, but even higher given that Lemonade now has more products to sell (it added Car), which of course is an incentive for customers to remain loyal. So I wouldn’t be surprised to see an ADR of 90% or more in 2027.

ADR is a lagging indicator though, meaning that when you see it changing, it could be too late. As a leading indicator, I instead like to use customers reviews. The rationale is that if customers are satisfied, they will stay. If not, they will change.

As long as Lemonade is rated 4 or above, I feel pretty comfortable with the expected ADR.

Keeping risks under control

This is the essence of an insurer: being able to accurately predict the risk of having to pay a claim and run the business accordingly.

The loss ratio is the king metric here. But as the loss ratio has to be seen as a trade-off between bearing a reasonable risk and keeping prices attractive enough to grow, I like to represent it with my proprietary adjusted rule of 40:

The beauty of this graph speaks for itself.

Given that for Car, the last product introduced, the loss ratio is following a similar path, I consider this part of the equation solved for Lemonade.

Profitability

Although important, of course, being obsessed with profitability is a waste of time and focus. Reasoning from first principles, if Lemonade keeps delivering on the four points that I have outlined, it will eventually become the most profitable insurer in the world.

If you are a long-term investor, you may actually want Lemonade to be as unprofitable as possible for a long time, as long as the cash is spent to grow faster.

Anyway, for the fetishists of profitability, I have at least two pieces of good news for you too.

Lemonade has two further important levers for profitability: reinsurance and Synthetic Agents.

During the quarter, we also completed our annual reinsurance renewal as well as the extension of our synthetic agents program with important upgrades to each. As it relates to reinsurance, the renewed program modestly increases the share of premiums that we retain while meaningfully strengthening catastrophe protection, including named storm coverage that was largely absent under the expiring structure.

Reinsurance is a way to lower risk and use less capital. As Lemonade is already doing, it will likely reduce the reinsured portion as it scales and has more capital available. Or maybe it will prefer (which is what I hope) to reinvest the capital to grow faster and keep the reinsurance share higher. The point is that once it has a meaningful capital surplus every quarter, being more or less profitable by retaining a higher or lower portion of the premium will be just a strategic decision.

Even more compelling is the argument for the Synthetic Agent program.

The agreement related to our synthetic agent extension provides $0.25 billion in growth financing at roughly 9.8% cost and applies to growth spend in ‘27 and ‘28. This amounts to more than 6 percentage points improvement in our cost of capital, materially lowering expected interest expense on a go-forward basis.

Lemonade secured a future reduction of more than six percentage points during Q2, which will be reflected in the financials of 2027.

As above, once enough cash comes in every quarter, Lemonade may decide to quit the program and bring the interest paid on growth to zero.

Conclusion

I’ll say it again: if Lemonade delivers on the four outlined points, it will be the most profitable insurer in the world.

As always, here is the “Deep Dive To Date” (DDTD), which shows how the stock is performing since my initial deep dive on September 24th 2024, when the stock price was $17.23.

2.8x DDTD

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

From a RAISE Summit discussion, here is a quote from Nikita Vdovushkin (Product Director, Token Factory at Nebius):

And we do see that like there is like some kind of a maturity curve where people start from using closed-source models like OpenAI and Anthropic or whatever. ... When they start getting traction, the cost basically soar in the the sky and they want to have and they start to get basically just two things. The first one, they do understand their customers better and due to that they have more data on that. And the second thing, they do understand the economics and how the product works better. And they want to apply these two things to make everything like work. And obviously, for that they start to fine-tune, post-train, do something with the model. And yeah, like most of the models that run on top of Token Factory, they still, like, some versions of the open source, but basically they’re somehow customized by the customer.

This makes perfect sense. As a company, you start by testing and may even launch your AI product, or AI-powered product, with closed models: it is faster, convenient, more intelligent and it scales nicely at the beginning. But as you validate your product and make it ready for production, it doesn’t make much sense to keep using closed models. You want it to be as cheap as possible, you want control and, once you have valuable data, you want to fine-tune it. All of these needs are met by open models and a powerful and reliable token factory, which is what Nebius offers and has been investing in from the beginning.

If you have ever worked in a manufacturing company, you may find the following analogy useful. Think about R&D and Production. For the first, you want the smartest and most capable people, as you need to come up with the best possible product. But for production, you want the best trade-off between people who are smart and capable enough and the lowest possible cost. You are willing to spend whatever is needed on individual salaries to create the product, but once the product is created, you need to optimize your manufacturing process to scale in the best possible way, while maintaining decent product quality.

According to research I ran with Grok 4.5, global spending on manufacturing salaries is from 2 to 4 times greater than global spending on R&D salaries. This data doesn’t need to be exact; being directionally correct is enough to prove my point about where the biggest opportunity is.

This doesn’t necessarily mean that global spending on open models will be greater than global spending on closed models. In fact, we have seen the opposite trend over the last 6 months: while tokens on open models are gaining share of total token usage versus closed models, in terms of spending the opposite is happening, and closed models are gaining revenue share (note that this data comes from a Vercel report, and open models are likely underrepresented here because fully self-hosted and direct-provider traffic is missing, but that only strengthens the observed direction: even in this gateway-heavy slice of the market, open models are already rapidly gaining token share while closed models continue to capture the vast majority of spend).

This actually makes perfect sense and supports my point.

More and more tasks will be accomplished by open models. But as you always need to create the best product and do better than the competition in coming up with new products and services, you are incentivized to use the best frontier model for some more strategic tasks. You cannot afford not to use the best intelligence available if you don’t want to be outcompeted.
So companies (and states too—think of departments of defence) will always be willing to pay a premium for specific critical use cases. And as we are still super early in the AI journey, it makes total sense that companies are still increasing their spending on closed models.

Quality of revenue and switching costs

Two main factors make the revenue of open-model providers which will be able to build a moat around software tools (like Nebius) better in quality:

• A company using closed models to experiment, test, build a product, discover stuff or do science is actually quite likely to fail, especially if it is a start-up developing and launching a new product. In that case, its revenue is only temporary for the frontier lab. A company running its workload on Nebius has likely already validated and market-proven its product using closed models, meaning that the chances of that revenue being permanent and growing are much higher.

• Although frontier labs are trying to build stickier and stickier tools to allow their customers to build agents and harness the raw model for a specific use case, if (or I should say when) their frontier model is leapfrogged by a competitor lab, the incentive for their customers to change provider could become compelling, as the strongest value delivered is the intelligence. On the contrary, if you have built on Nebius Token Factory, you have probably invested a lot of effort and money to fine-tune a model for your specific use case, and the incentive to go back to closed models is basically zero.

The arguments in favour of open models, and in particular of a provider like Nebius that is building a moat on what I have just explained above, are compelling and proven at this point.

This is not financial advice, remember that I have a position in Nebius.

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But manufacturing and launching satellites is not the same as operating a global constellation service.

Rocket Lab is buying what cannot be easily built.

Iridium brings:

• scarce, globally coordinated spectrum;

• a constellation operating continuously across the entire planet;

• government, aviation and maritime certifications;

• more than 500 commercial and technology partners;

• billing, provisioning and customer-support infrastructure;

• decades of experience managing a mission-critical satellite network;

• recurring cash flow that can help finance the next constellation and reduce future dilution.

These capabilities would take Rocket Lab many years to reproduce—and some, particularly the spectrum and regulatory rights, might be impossible to replicate.

This is what Rocket Lab is really buying.

In this article, I’ll share my first thoughts and considerations following Rocket Lab’s latest acquisition.

The Business Opportunity for Iridium’s Constellation

These are the numbers for Iridium’s current business:

• Total Revenue (Q1 2026): $219.1 million (+2% YoY)

• Service Revenue (recurring, 72% of total): $158.0 million (+2% YoY)

And these are the lines of business that make up its revenue:

Commercial Service $130.4 million +2% ~60% Largest segment. IoT is the main growth driver (+5%). Voice & Data stable with pricing gains. Government Service $27.6 million +3% ~13% Stable, driven by EMSS contract with U.S. DoD. Equipment Sales $20.2 million -13% ~9% Down due to timing; expected flat for full year.

Engineering & Support Services $40.8 million +9% ~19% Growing, mostly U.S. government work.

Most of the current business comes from legacy areas, such as commercial voice and data and commercial broadband, which will likely shrink over time or grow only moderately.

The most promising area may be commercial IoT data. One might think that, with the explosion of drones and autonomous vehicles more broadly, this business could see strong growth. Every autonomous object will need to be tracked, and Iridium’s PNT (Positioning, Navigation and Timing) service is state of the art in terms of reliability. Thanks to its LEO positioning and Iridium’s unique spectrum, its signal is stronger, more robust and more reliable than GPS, although less accurate. It is normally used together with GPS in missions where PNT complements and protects GPS and other GNSS-reliant systems. In some situations, these systems can be put out of service by bad weather or hostile interference—in war and defense missions, for instance. In those cases, having a more reliable, although less accurate, positioning system becomes crucial.

Digging further into this topic, I discovered that even drones currently used in defense are not generally equipped with Iridium PNT or any equivalent service. When GPS is unavailable, they rely only on vision to navigate. This makes me less certain about the strategic importance and utility of Iridium PNT for most autonomous vehicles, and therefore less bullish about possible future hypergrowth in this line of business. However, the miniature application-specific integrated circuit (ASIC) Iridium is about to launch will allow partners to integrate its PNT services more cost-effectively, using less power and space.

That could make the service more compelling for autonomous vehicles.

The most promising business appears to be commercial IoT. Once Iridium launches its Direct-to-Device IoT connectivity later this year, every phone could potentially connect to Iridium satellites and use their services without needing additional hardware. It will also become easier and cheaper to integrate Iridium services into any IoT object, simply by using a small commercial modem. This will certainly boost IoT segment revenue. Until now, Iridium IoT required a dedicated receiver, making it complex and expensive—and, in some cases, simply not feasible.

Yet I am not convinced that this will be a game changer capable of turning Iridium into a hypergrowth story. For mobile phones, Iridium is a good solution for providing connectivity globally and in almost any weather condition. In that respect, it is more reliable than Starlink, which can struggle in bad weather despite offering broadband internet speeds, while Iridium can provide only the lower speeds needed for messages and phone calls. The question is: how large is this market? Even assuming that every phone maker in the world adopts it across its devices, the ARPU would probably remain tiny, although the service itself would still be compelling.

My conclusion—possibly wrong, since I have only just started analysing the topic and there is still a lot I do not know—is that current’s Iridium constellation is a stable, recurring-revenue business heavily weighted toward commercial IoT and government contracts. Growth is modest, in the low single digits, but reliable, with upside from new services such as D2D, PNT and aviation. It is not a high-growth company yet, but the Rocket Lab acquisition could accelerate its future expansion.

The biggest opportunity is still defense

If we look at who is launching things into space as a proxy of the space TAM share, there are four meaningful demand categories:

1. Communications constellations — largest volume, but overwhelmingly Starlink.

2. Defense and sovereign infrastructure — broadest and most dependable launch market.

3. Earth-observation intelligence — smaller satellites, recurring replenishment and growing defense demand.

4. Science, exploration and human spaceflight — valuable but government-funded and relatively slow-growing.

If we exclude Starlink, the most meaningful missions are funded directly or indirectly by governments.

For me, defense remains for Rocket Lab the clearest space hypergrowth opportunity for at least the next five years.

The US and its allies are moving from a few expensive satellites toward proliferated constellations for missile tracking, secure communications, intelligence, resilient navigation and space-domain awareness.

Rocket Lab was already moving in this direction. Its Space Development Agency contracts proved that it can compete for awards historically reserved for legacy aerospace primes.

Iridium reinforces the thesis.

The combined company can potentially offer a government the complete architecture:

Design the satellites. Build the payloads. Launch the constellation. Operate the network. Deliver the service.

That is a much stronger proposition than being only a launch provider or component supplier. Rocket Lab can take responsibility for the entire mission—and capture more of its economics.

Iridium also gives Rocket Lab something especially valuable in defense: operational credibility. It has already demonstrated that it can run a secure, resilient global constellation relied upon by governments and safety-critical industries.

Becoming a Platform: The AWS of Space

A customer planning a new constellation does not only need spacecraft. It needs network architecture, spectrum strategy, ground infrastructure, fleet management, continuous operations and distribution.

After Iridium, Rocket Lab can credibly sell a much broader outcome:

Rocket Lab will design, manufacture, launch and operate your constellation.

That positions Rocket Lab to compete as a prime contractor for commercial deals, while creating more opportunities to sell its own solar arrays, star trackers, reaction wheels, flight software, payloads and launch services.

Once a space company owns the complete stack, I would argue that it becomes a platform.

If you need to send assets into space and operate them there—especially as part of a constellation—you will probably standardize your payloads around Rocket Lab or SpaceX. Once you know how to design and build them for, say, Rocket Lab, you can standardize dimensions, processes, communication protocols and operations around the same provider.

This is what happened with cloud providers such as AWS. Once it became clear that running software in the cloud was cheaper, more reliable and more convenient than hosting it on-premise, software vendors and end-users began standardizing their operations around AWS, Azure or Google Cloud. In space, this dynamic could be even stronger because there is no real “on-premise” option. The choice is between rebuilding the entire stack from scratch every time or standardizing around one of the only two or three end-to-end space companies.

With this acquisition, Rocket Lab has everything required to become a platform—the same concept I described in my original deep dive. I think we are getting closer to seeing it become a reality.

The optionality is real—but not yet the thesis

Iridium mentioned several possible new services in its 2025 annual report, including aviation data through Aireon and Project Authentic, which aims to use Iridium’s PNT signal to authenticate the physical location of devices involved in sensitive transactions.

Project Authentic is interesting because it could move Iridium from backup navigation into cybersecurity. But it is still early: no named platform partners or commercial contracts have been disclosed.

I would treat these services as optionality, not as justification for paying $8 billion today.

Rocket Lab next constellation

As a simple way to see it, most launches today and in the future will be about one of three things:

1. Connecting things (internet, phones, IoT, drones)

2. Watching things (Earth, weather, military targets)

3. Enabling future ambitions (Moon bases, Mars, space industry)

SpaceX is capturing most of the connectivity business through Starlink, while IoT remains a smaller and slower-growing segment.

When it comes to future ambitions, I think Rocket Lab will play an important role in orbital data centers, even though this opportunity may not become viable or meaningful for at least another five years.

Looking at the short term—say, three to five years—I believe Rocket Lab could launch its own Earth-observation constellation. That seems achievable.

Over a ten-year period, Rocket Lab should undoubtedly aim to operate its own orbital data-center service.

What could Rocket Lab buy next?

Rocket Lab has already acquired Mynaric, adding laser terminals for high-bandwidth links between satellites.

The next logical gap is the other side of that network: space-to-Earth optical communications and optical ground infrastructure.

A future acquisition could add optical ground stations, atmospheric and cloud mitigation, network-routing software and hybrid laser/RF connectivity.

That would matter for defense networks, Earth observation and, eventually, space-based data centers. Rocket Lab would control the satellites, power systems, optical links, launch and ground connection.

Strategically, this is the missing capability I would watch.

My conclusion

Iridium’s existing constellation will not support Rocket Lab hypergrow by itself. I would be very surprised to see its business growing by more than 15-20% YoY over the next five years. Its existing business is profitable and defensible, but mostly mature. It will certainly strengthen Rocket Lab’s financial position by adding a meaningful, cash-generating revenue stream that could even make the company profitable as early as 2027, when Neutron CAPEX begins to slow down.

The acquisition works if Rocket Lab uses Iridium to become something much larger: an end-to-end prime contractor capable of building and operating complete defense and commercial space infrastructure.

In terms of a proprietary Rocket Lab constellation, beyond what Iridium already has in operation, I struggle to see anything generating meaningful revenue before five years from now.

That is the opportunity.

Rocket Lab is not simply buying a satellite operator. It is buying the final operating layer it needs to compete for the largest contracts in space.

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I have a position in Rocket Lab, and please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

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Lorenzo

In terms of timeline, I starterd in 2024 and I believe the $1 million target is achievable by 2032, assuming no major black swan events or asset bubbles burst. In such scenarios, it would naturally take longer.

Target and KPIs

Every meaningful goal requires a clear path and process, which is why the portfolio’s success is evaluated on a rolling five-year average basis using two key performance indicators (KPIs):

1. Compounding at a minimum of 30% year-over-year (YoY)

2. Outperforming the NASDAQ Composite benchmark by at least 10%

A consistent 30% YoY compounding rate won’t quite deliver $1 million by 2032, but it represents a realistic yet ambitious target—one that would likely surpass the performance of most institutional and retail investors alike.

L2C Portfolio Performance

Now, turning to the H1 2026 (First half of 2026) performance.

Following a stellar 2024, and a good 2025, here’s how H1 2026 stacked up:

• Lorenzo2cents Portfolio YTD: +19.0%

• NASDAQ Composite: +11.5%

• S&P 500: +9.1%

While the YTD performance remains strong compared to the benchmark and is well on track to meet my goals, I am a bit disappointed about the significant drawdowns experienced in late February and late March. During that period, my portfolio declined by an average of 25%, while the Nasdaq fell only 2% to 10%.

The underperformance was primarily driven by idiosyncratic events affecting two holdings: Oddity and Hims. Oddity suffered a severe setback in customer acquisition (which has not yet been resolved), causing its stock to drop ~70% since the start of the year. Hims was hit hard too, falling 55% after the Novo lawsuit.

Events like these are an inherent part of the portfolio’s risk profile. That said, the probability of two out of nine stocks experiencing major negative market-moving event at the same time is quite low — even though it unfortunately happened in this case.

Let’s now review the success KPIs since inception (January 1, 2024) through July 2, 2026:

1. L2C Portfolio CAGR: 58% — exceeding the minimum goal of 30%

2. L2C vs. NASDAQ Composite (IXIC) CAGR: 58% vs. 24,2% — surpassing the minimum goal of outperforming the market by 10% (overperformed by 33,8%)

L2C Portfolio Composition (as of July 2, 2026)

The following positions constitute the portfolio:

1. $LMND: 25.6% Allocation - Reinventing the insurance sector.

2. $NBIS: 22.5% Allocation - The Application Software’s Universal Substrate for Agentic Computation

3. $RKLB: 17.7% Allocation - The leading public space company.

4. $HIMS: 14.9% Allocation – Redefining healthcare by making it accessible and affordable.

5. $TSLA: 6.8% Allocation - The ultimate leader in transportation autonomy, sustainable energy, and humanoid robotics.

6. $MELI: 5.9% Allocation - The backbone of Latin America’s digital economy, poised to benefit from continent-level network effect.

7. $ZETA: 4,3% Allocation - The Marketing Vertical Ontology

8. $ODD: 3,2% Allocation - Revolutionizing the beauty industry.

9. $DUOL: 1.9% Allocation - Transforming education by making learning both fun and effective.

10. Liquidity: (3.6%)

Key Portfolio Moves

• Sold $META - While I still believe META is unlocking AI’s future with an unparalleled dataset, I decided to allocate the capital to more asymmetric bets.

• Opened a $ZETA position - Incredibly undervalued with an exceptional management team.

Top Performers and Top Losers (+-20% movements)

• $NBIS +157%

• $RKLB +44%

• $ODD -55%

• $DUOL -28%

Stay tuned

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YouTube Channel

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To read my deep analysis

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Conclusion

I’m quite pleased with H1 2026’s performance and anticipate an even more exhilarating second half of 2026, if the market doesn’t crush. The Lorenzo2Cents project is growing steadily and is poised to deliver even greater value to those willing to engage with it. Stay tuned!

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

Revenue grew 49% year over year to $8.8 billion, the fastest pace in almost four years. GMV grew 42% to $19.0 billion. TPV grew 50% to $87.2 billion. Fintech monthly active users reached 82,9 million, up 29% year over year. The credit portfolio reached $14.6 billion, up 87% year over year.

And yet the stock sold off because operating margin fell to 6.9%, down 600 basis points year over year.

But the headline is not that MercadoLibre is suddenly less profitable. The headline is that MercadoLibre is choosing to invest aggressively because the opportunity in front of it is still enormous, and those investments are already producing faster growth, stronger network effects, and better scale economics.

Here is how Ariel Szarfsztejn, CEO of Mercado Libre, described competition in Brazil.

Brazil is one of the most attractive e-commerce markets in the world. So it’s natural that it’s getting more and more intense and as that has been the case for many years... competitive intensity is also having a positive impact in the market as a whole by bringing new consumers from the offline world into the online world, and we feel we are very much equipped to offer all those consumers the opportunity to buy in MercadoLibre. So the pie is increasing at a faster pace than it was before, and we are taking an even larger slice of that pie.

This sounds almost like a Jevons paradox.

In the original Jevons paradox, when something becomes more efficient, people do not necessarily consume less of it. They often consume more because the lower cost expands the total market.

Something similar is happening in Latin American e-commerce.

Competition, free shipping, better logistics, lower friction, better search, and better prices are not simply taking share from MercadoLibre. They are accelerating the migration from offline retail to online retail. And when more consumers move online, MercadoLibre is structurally advantaged because it has the largest marketplace, the strongest logistics network, the most trusted payments layer, and the best data loop in the region.

In other words, competition is not necessarily a zero-sum game yet.

Latin America is still too early.

Management reminded investors that the average American makes 41 online purchases per year, while the average Latin American makes just 7. MercadoLibre buyers average 11. That gap is the whole thesis.

If the market grows faster because consumers are being pulled online more aggressively, the largest platform can benefit even if the environment looks more competitive on the surface.

That is exactly what Q1 showed.

Brazil: The Flywheel Is Working

Brazil was the standout market again.

MercadoLibre lowered the free shipping threshold in Brazil in 2025, and the market initially interpreted it as a margin-negative competitive response. But the data keeps pointing in the opposite direction: it is a long-term investment that is expanding the market, increasing frequency, and improving the utilization of MercadoLibre’s infrastructure.

Management put it clearly:

By bringing more buyers into the ecosystem, we’re strengthening network effects with higher purchase frequency, broader assortment and a logistics network that becomes more efficient with every incremental package. As a result, Brazil delivered another standout quarter for commerce. GMV grew 38% year-over-year as items sold growth accelerated to 56%. This is more than double the quarterly growth rate prior to lowering the free shipping threshold.

Free shipping penetration reached a new record and unit economics continue to improve with cost per shipment down 17% year-over-year in local currency. In other words, higher demand is driving lower costs.

This is the core point.

More demand is not just more revenue. More demand means more packages through the same network. More packages improve route density, fulfillment utilization, line-haul efficiency, and last-mile economics. MercadoLibre already has the most advanced logistics infrastructure in the region, so incremental volume should improve the economics of the network over time.

This is why I think the market is misreading the margin compression.

If the lower free shipping threshold were simply a price cut with no strategic benefit, I would be worried. But if it brings more buyers into the ecosystem, increases frequency, expands category breadth, improves logistics density, and strengthens the marketplace, then the short-term margin hit is an investment.

And the investment is clearly working.

Brazil GMV grew 38% FX-neutral. Items sold grew 56%. Unique buyer growth accelerated to 32%, the fastest pace in five years. Unit shipping costs in Brazil fell 17% year over year in local currency.

The market sees lower margins. I see MercadoLibre pulling more of Latin America into its ecosystem.

The impact of AI Is Still Underappreciated

Another part of the quarter that I think is still underappreciated is AI.

Most investors do not think of MercadoLibre as an AI beneficiary. They think about marketplace, logistics, payments, credit, and maybe ads. But AI directly improves the economics of all of these layers.

Management said:

We deployed LLMs in search in commerce for the first time this quarter. And basically, that is live in Brazil, Mexico and Argentina. So now we are using this technology to better understand users’ intent, combining both knowledge on the user behind the query and better interpretation of the query itself. And the impact is basically very visible across the funnel. We have higher conversions as buyers find what they are looking for much faster.

We have better ad returns as our search also improves the quality of the results that our ad tech stack is generating. We have higher -- stronger engagement from our users as the discovery experience actually improves. And clearly, this is one of the contributors to the great performance we had this quarter.

This matters because MercadoLibre has one of the richest first-party datasets in Latin America: shopping intent, payment behavior, merchant data, credit performance, logistics data, search data, and advertising performance.

LLMs improve the search layer, but the impact does not stop there.

Better search means higher conversion. Higher conversion means more GMV. More GMV means more payment volume. More payment volume means more data. More data improves underwriting, ads targeting, and personalization. Better personalization increases engagement. Higher engagement increases frequency.

That is the ecosystem loop.

The low-hanging fruit is advertising. If AI improves search relevance and sponsored listing performance, ad ROI improves. When ad ROI improves, sellers can spend more. MercadoLibre said ads revenue grew 73% in USD and 63% FX-neutral in Q1.

MELI does not disclose Mercado Ads revenue as a separate line item, but based on prior disclosed ads penetration and Q1’26 growth, Mercado Ads was likely running at roughly $2B annualized run-rate.

This is still a small line relative to the full business, but it is a structurally high-margin opportunity. It also reinforces the marketplace, because better ads help sellers sell more and help buyers discover more relevant products.

But I think the bigger AI opportunity is not only ads. It is the improvement of the entire consumer experience. Search becomes more natural. Discovery becomes better. Credit decisions become more precise. Customer support becomes cheaper. Logistics workflows become more productive. Seller tools become more useful.

This is not an AI story in the hype sense. It is an AI story because MercadoLibre has scale, proprietary data, distribution, and real use cases.

The Margin Compression Is Not What It Looks Like

The main reason the stock is trading around 2.5x LTM revenue is that investors are worried about competition and margin pressure.

I understand the concern, but I think the conclusion is wrong.

MercadoLibre is not losing control of its business. It is deliberately choosing to invest because the returns on those investments are attractive.

Martin de los Santos, CFO at Mercado Libre, said it directly:

If we wanted to improve margins in the short term, it will be fairly easy for us to slow down certain investments, but we don’t think it’s the right way to go given the large opportunity that we have in front of us for both commerce and fintech.

This is not a management team trying to explain away a broken model. This is a management team saying: we can harvest, but harvesting would be the wrong capital allocation decision.

The biggest distortion is in Mercado Pago.

The credit book grew 87% year over year, much faster than consolidated revenue growth of 49%. That creates accounting margin pressure because MercadoLibre has to provision expected losses upfront when it originates new credit.

Management explained the mechanics:

The fact that our credit book grows at a faster pace than revenues, the credit book grows at 87% year-on-year and our revenues for MercadoLibre growth at 49%, that generates margin compression.

And the reason for that is because as we issue any new loan, we have to provision for the full amount of the expected loss of that loan. And when we accelerate growth, we need to provision more.

So 2/3 of the margin compression comes from that.

This is crucial.

If the credit book is growing profitably, then near-term provisions are not the same thing as structural margin deterioration. They are the accounting cost of growth.

MercadoLibre’s Q1 NIMAL was 17.8%, down from 22.7% a year ago, mainly because of the higher mix of lower-spread credit cards and some spread compression in Brazil. That decline is worth watching. But 17.8% NIMAL is still an extremely attractive return profile, especially if the credit book is expanding and asset quality remains controlled.

The 15-90 day NPL ratio was 8.0%, broadly stable year over year, according to the company. Management also said that each of its consumer and merchant loan portfolios remains highly profitable.

So I do not think the right conclusion is “Mercado Pago is destroying margins.”

The right conclusion is: Mercado Pago is growing so fast that the accounting cost of new credit is temporarily compressing margins, while the future earnings power of the credit book is being built.

FCF: Do Not Treat Loan Growth Like a Cost

This is also why I think the way we calculate free cash flow matters.

If we subtract all cash used to grow the loan book as if it were an operating cost, as per standard account practice and as often done by investors, we risk dramatically understating the value of MercadoLibre.

That cash is not being burned. It is being converted into financial assets that generate yield.

Using the standard free cash flow definition:

Free Cash Flow = Cash From Operations - Capex

MercadoLibre generated roughly $11.8 billion of LTM free cash flow through Q1 2026 (LTM):

That is the number I care about for the main valuation framework.

Yes, the credit book requires capital. Yes, provisions and funding costs matter. Yes, NPLs must be watched carefully. But deducting loan originations 1:1 from FCF can make the business look much less valuable than it is, because it treats a productive asset like a cost.

The right way to think about it, in my opinion, is to keep reported FCF as the main line and analyze credit-book growth separately: NIMAL, NPLs, funding costs, portfolio mix, and return on the capital deployed.

If those metrics deteriorate, then the thesis changes. But as of Q1, I do not see evidence that MercadoLibre is simply buying growth with bad credit.

I see a company investing aggressively into a credit opportunity where it has unique data, distribution, and ecosystem advantages.

Mercado Pago vs Nubank: The Business Ontology Test

One of the most important data points I am watching for my Business Ontology is the performance of Mercado Pago versus Nubank.

Nubank is probably the cleanest benchmark for Latin American fintech at scale. It is an incredible business. It has more than 135 million customers, a monthly activity rate of 83%, and a credit portfolio of $37.2 billion, up 40% year over year FXN.

But Mercado Pago is becoming harder to ignore.

Mercado Pago reached 82,9 million monthly active users, up 29% year over year.

Its credit portfolio reached $14.6 billion, up 87% year over year. AUM reached almost $20 billion, up 77% year over year.

The active-user comparison is also interesting. Nubank had more than 135 million customers in Q1 2026 and an 83% monthly activity rate, which implies roughly 112 million active customers. That is still larger than Mercado Pago, but Nubank’s active base grew from 98.7 million monthly active users in Q1 2025 to roughly 112 million in Q1 2026, or about 14%. Mercado Pago’s active user base grew 29% over the same period.

The profitability of credit also tells a more nuanced story. Nubank’s risk-adjusted NIM was 9.5% in Q1 2026, down from 10.5% in Q4 2025. Mercado Pago’s NIMAL was 17.8%, down from 22.7% a year ago. These are not perfectly identical metrics, so I would not overstate the comparison. But directionally, Mercado Pago is scaling credit faster, with a higher reported credit spread, while Nubank remains the larger and more mature fintech machine.

So Nubank is still much larger in credit portfolio and active customer scale. But Mercado Pago is growing active users and credit faster, and it is doing it from inside the MercadoLibre ecosystem.

This is exactly my thesis.

The combination of commerce + fintech should create advantages that a standalone digital bank does not fully replicate.

MercadoLibre knows what consumers buy, what merchants sell, how often they transact, what categories they participate in, how payments flow, how logistics behave, and how users interact across the platform. This gives Mercado Pago a different underwriting and distribution advantage.

A standalone fintech can have an excellent app and an excellent brand. Mercado Pago has that too, but it also has the marketplace.

MercadoLibre is not just a marketplace with a payment app. It is a commerce and financial infrastructure layer for Latin America.

What Could Re-rate the Stock?

I do not think a re-rate is imminent. The market is clearly skeptical, and investors usually do not reward margin compression until they can see stabilization.

But a few catalysts could change the narrative.

First, a stock split. Economically, it changes nothing. But MELI’s share price is high, and a split could renew retail investor interest, especially if the fundamental story is already improving.

Second, NIMAL stabilization. The decline from 22.7% to 17.8% is one of the concerns. If NIMAL stabilizes while the credit book keeps growing, the market may start seeing the credit investment as value creation rather than margin risk.

Third, margin stabilization. Management does not need to maximize margins today, but investors need evidence that margins are being compressed by choice, not by necessity.

Fourth, continued FCF growth. If free cash flow per share keeps rising while the company reinvests aggressively, the market will eventually have to reconcile the narrative of margin pressure with the reality of cash generation.

Fifth, capital rotation from AI stocks. MercadoLibre is not an obvious AI trade, but it is one of the few large-cap businesses where AI can improve an already dominant marketplace/fintech ecosystem. If investors start looking for non-obvious AI beneficiaries with real earnings and real data advantages, MELI fits that bucket.

L2C take aways and performance

MercadoLibre is being priced like a company whose margins are under attack.

I think it is a company choosing to reinvest at high rates because the opportunity is still underpenetrated, and because its ecosystem advantages are getting stronger.

Q1 2026 was not a clean quarter if you only care about near-term EPS. Operating income declined, net income declined, and margins compressed.

But if you care about long-term value creation, the quarter was extremely strong.

Revenue growth accelerated. Brazil commerce accelerated. Items sold exploded. Logistics unit costs improved. Ads grew rapidly. Fintech MAUs grew. AUM grew. The credit portfolio expanded. AI started to show real business impact. Free cash flow remains very strong on an LTM basis.

The market sees lower margins.

I see a dominant platform with a huge volume of high quality first-party data investing to make the market bigger, take a larger slice of it, and turn that scale into long-term cash flow.

At roughly 2.5x LTM revenue, the asymmetry is becoming more and more interesting.

I do not know when the market will re-rate MELI. It may take time. But even without multiple expansion, a business compounding revenue and cash flow at these rates can still create significant value.

And if the market eventually understands that the margin compression is mostly a reinvestment cycle rather than structural damage, the upside could be much larger.

I am willing to wait.

I will soon publish a video where I explain my updated Rocket Lab 4D valuation model and the assumptions behind it.

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the May 18th 2025, when the stock price was $2585. As proved by my post on X, where I share real time updates, I opened a position a few days later.

- 37% DDTD

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

Demand is clearly not the problem.

Execution is not the problem.

The company is doing almost everything bulls wanted to see: revenue is growing, backlog is expanding, Electron is still proving its cadence, HASTE is becoming increasingly relevant for defense, and Neutron is attracting customers before its first launch.

On the business side, this was probably one of the strongest updates Rocket Lab has ever delivered.

But that is not the only question that matters for investors.

The real question is different:

How much of the future is already priced into the stock?

And after updating my model, my answer is less comfortable than it used to be.

Rocket Lab remains an exceptional company. I still think it is one of the most strategically important public companies in the space economy. But at the current valuation, the stock no longer looks like the obvious asymmetric setup it was before.

The company may execute beautifully.

The stock may still do well.

But the margin of safety is much thinner.

Q1 was a very strong quarter

Rocket Lab reported record Q1 2026 revenue of $200.3 million, up 63.5% year over year. GAAP gross margin reached a record 38.2%, and non-GAAP gross margin was 43.0%.

Backlog reached $2.2 billion, up 20.2% sequentially and more than double year over year.

That backlog number matters more than a normal quarterly revenue print, because Rocket Lab is not a simple transactional business. It is becoming a long-duration execution machine across launch, spacecraft, components, defense programs, and eventually Neutron.

Backlog is not revenue.

But in space and defense, backlog is the closest thing we get to forward visibility.

And the direction is very clear.

The backlog-to-revenue ratio increased sequentially again. Using Q1 revenue of $200.3 million and backlog of $2.2 billion, Rocket Lab is sitting at roughly 11x quarterly revenue in backlog.

That means demand is not just growing in absolute terms; the forward pipeline is growing faster than current recognized revenue.

That is exactly what you want to see from a company that is supposed to accelerate over time.

Peter Beck summarized the demand picture very well:

With the 31 Electron and HASTE launches and 5 Neutron contracts combined, we booked more launches in the first 3 months of 2026 than we did for all of last year.

That sentence is the bull case in one line.

Rocket Lab is not begging the market for demand.

The market is asking Rocket Lab for capacity.

Launch demand is inflecting

The most important signal in Q1 was not just revenue.

It was bookings.

Rocket Lab signed 31 new Electron and HASTE contracts in Q1, plus five dedicated Neutron launches.

This matters for two reasons.

First, Electron is still alive and very relevant.

Many people underestimate Electron because it is a small rocket, but that misses the point. Electron has cadence, reliability, and customer trust. In launch, those things matter more than PowerPoint capacity.

Second, Neutron is getting commercial validation before first flight.

That is important.

Neutron is still the biggest execution risk in the Rocket Lab story. Until it flies successfully, it is still a promise. But customers are already reserving capacity through the end of the decade, which tells you there is real market appetite for another reliable medium-lift provider.

The industry does not want only SpaceX.

Governments do not want only SpaceX.

Commercial constellation operators do not want only SpaceX.

The world needs more launch capacity, and it needs it from operators that can actually deliver.

That is where Rocket Lab has a shot.

Neutron still looks on track

The other key update is that Neutron is still expected to launch later this year.

No new delay was signaled in the Q1 update.

This is important because a large part of the Rocket Lab thesis now depends on Neutron. Electron is a great business, but it is not enough to justify the full long-term vision by itself. Space Systems is growing and becoming strategically important, but Neutron is what can turn Rocket Lab from a strong niche player into a true end-to-end space infrastructure company.

Neutron changes the size of the opportunity.

It opens the medium-lift market.

It gives Rocket Lab more control over its own space systems deployment.

It creates the platform that could eventually enable the services layer Peter Beck has talked about for years.

And that last point is key.

Because if Rocket Lab ever becomes more than launch + components + spacecraft manufacturing, if it eventually owns and operates services from space, the upside can be much larger than what a normal aerospace model captures.

But timing matters.

Peter Beck has been clear that services from space come after Neutron is established. That makes sense. You need the transportation layer before you can efficiently deploy and replenish your own infrastructure.

So even in an optimistic scenario, meaningful revenue from a services business is probably still three to five years away.

That does not mean it has no value.

The valuation is rich.

This is the uncomfortable part.

When I update my model, the conclusion is not that Rocket Lab is overhyped as a company.

The conclusion is that the stock price is already discounting a huge amount of success.

At the current valuation, the market is not pricing Rocket Lab like a speculative small launch company anymore. It is already pricing in a version of the future where:

• Electron continues to grow and maintain strong cadence.

• Space Systems compounds for many years.

• Gross margins keep expanding.

• Neutron launches successfully.

• Neutron scales without major delays.

• Rocket Lab captures a meaningful share of medium-lift demand.

• Defense and national security demand remain strong.

• The company keeps using its balance sheet well for M&A and vertical integration.

That is a lot of good news already embedded in the stock.

To be clear, none of these assumptions are crazy.

Actually, many of them are exactly what I want Rocket Lab to do.

The issue is not that the story is fake.

The issue is that the market now believes a large part of the story.

And when the market already believes, the asymmetry changes.

A year ago, the market was still treating Rocket Lab like a risky space stock with a small rocket and a dream.

Today, the market is closer to pricing it like a future space prime with a successful medium-lift rocket, a growing defense business, expanding margins, and long-duration backlog.

That is a very different setup.

What is not priced in?

In my view, the part that is not fully priced in — or at least not priced in enough — is the long-term services layer.

This is the part of the thesis where Rocket Lab stops being only a provider of launch and space systems, and starts monetizing infrastructure in orbit.

That could be constellations.

It could be data.

It could be communications.

It could be defense-related services.

It could be something we cannot precisely model yet.

This is why I have always liked Rocket Lab. Peter Beck is not building a small rocket company. He is building an end-to-end space company.

Launch is the access layer.

Space Systems is the manufacturing layer.

Services would be the monetization layer.

If Rocket Lab gets there, the upside can still be very significant.

But this is also the most uncertain part of the thesis.

It is far away.

It depends on Neutron.

It depends on execution.

L2C take aways and performance

Rocket Lab is still a great company. But the stock is no longer an obvious asymmetric bet at this valuation.

I remain invested.

But I am watching closely, and I am ready to take action if the risk/reward keeps deteriorating versus other opportunities in the portfolio.

I will soon publish a video where I explain my updated Rocket Lab 4D valuation model and the assumptions behind it.

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive. For RocketLab stock ($RKLB), the price on September 17th, 2024, when I published my analysis, was $7.19. As of this update, the price stands at $102, reflecting a

+14x DDTD

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

I won’t repeat the full thesis here. If you haven’t read it, start there.

This is just a short update after Q1.

Sales declined 26% year over year, slightly better than management’s previous expectation of about -30%, but directionally nothing changed. The damage is real.

Management also said they “remain hopeful” that the business will return to normalization in the second half. That word matters. “Hopeful” tells me this is still not under their control.

There was one encouraging data point: IL Makiage CPA improved in May, declining an estimated 28% from April after multiple months of increases with the same advertising partner.

That could be the first sign of stabilization. But it also shows how serious the problem was. CPA kept getting worse for months, even after management knew what was happening and started taking corrective action.

May could be the turning point. Or not.

The other important update is that Oddity has already shifted 40% of acquisition revenue out of Try Before You Buy and into the standard Buy model.

That is a big deal. TBYB was part of the IL Makiage edge. I don’t know exactly how much of the historical advantage came from it, but it clearly mattered to some degree. If they can move away from it with “no impact on unit economics”, great. But I want to see more proof.

The most concerning part of the call was how much power Meta has over Oddity, and how little accountability Meta has when something breaks.

Oddity was damaged by a Meta algorithm change. Meta is not accountable for that damage. And now Oddity still has to keep spending money on Meta to understand the problem, test fixes, and try to recover.

Without spending, they cannot identify what works. But because the efficiency of that spend is broken, they also cannot simply scale it back up.

That is a very uncomfortable position.

Management also made clear that there is no real alternative at the same scale. Yes, Oddity advertises on other platforms, but their largest ad partner is by far the biggest acquisition channel in U.S. beauty, with more than 50% market share.

In practice, Oddity has no real alternative to Meta for new customer acquisition. They are still completely dependent on it.

This is the core lesson for me.

Oddity built a very powerful D2C engine, but it was more fragile than I appreciated. The company is not broken, and the thesis is not dead, but the acquisition model depends too much on one platform.

Meta changed the rules. Oddity took the damage. Now Oddity has to spend money to figure out how to adapt.

That is not a position of strength.

IlMakiage.com traffic also continued to fall, which confirms why I wanted to monitor it in the first place. It is not perfect, but it looks like a decent proxy for the state of the recovery.

For now, I am leaving my position untouched.

I will keep monitoring web traffic, CPA commentary, and any sign that the business is normalizing. If I make any trade, I will update Business Ontology members first.

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the July 16th 2024, when the price was $44.24.

-77%% DDTD

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

I think that call was right.

Q1 revenue grew only 4% year over year, gross margin compressed, and GAAP net income flipped to a loss. The quarter that Hims just reported is backward-looking.

The more important information came from the guidance, the Novo Nordisk pivot, the Eucalyptus deal, and especially what management said about AI.

And I think very few people have understood where Hims is going.

Management guided Q2 revenue to $680–700 million and raised full-year 2026 revenue guidance to $2.8–3.0 billion.

Yemi Okupe, Hims’ CFO, basically told investors that demand in weight loss is exploding again:

Within weeks of this launch, we are on track to add north of 100,000 new subscribers per month within our weight loss specialty. Early signs point toward a high level of subscriber engagement with nearly 90% of these users downloading the app and the average subscriber of these products interacting with the provider 3 times in the first month.

In Q4 2025, subscriber growth was almost irrelevant. In Q1 2026, Hims added roughly 100k subscribers in the quarter, or around 33k per month average.

Now management is talking about north of 100k new weight-loss subscribers per month.

That is roughly 3x the Q1 pace.

And this is happening after the strategic shift toward a broader assortment of FDA-approved GLP-1s through Novo Nordisk.

Hims announced in March that Novo Nordisk’s FDA-approved GLP-1 medications were available through the platform, including Wegovy options and Ozempic for eligible patients where clinically appropriate.

This matters because the market narrative on Hims became too simplistic.

The bear case was: “The compounded GLP-1 opportunity was temporary, so the business is broken.”

My view is different.

The compounded GLP-1 opportunity was the ignition. The real asset is the consumer healthcare distribution layer.

Hims can take demand, route it through a regulated medical workflow, connect it to providers, manage the customer relationship, and increasingly personalize the journey.

That is not easy to replicate.

Andrew Dudum also said demand is now stronger than what they saw during major seasonal campaigns:

We see it in the scale of new customers coming in the door today, which is larger than, as Yemi said, any spikes we saw even during the most important seasonal campaigns such as New Year’s and Super Bowl campaigns.

That is why I do not think Q1 is the quarter to obsess over.

The important signal is what happens from Q2 onward.

Hims is sandbagging

Management guided Q2 revenue to $680–700 million.

At the midpoint, that is around 13.5% sequential growth from Q1.

At the high end, it is around 15% sequential growth.

That already tells you the business is reaccelerating.

Now add the second part of Yemi’s comment:

...with record-level users coming to the platform, the focus really is on continuing to expand share, expand our reach and expand the overall subscriber base. And so with that, I think we would expect to see accelerating growth via revenue over the back half of the year.

For a subscription business, I read “accelerating growth” primarily as sequential acceleration.

If Hims does $700 million in Q2 and hits the high end of full-year guidance at $3.0 billion, then Q3 and Q4 together need to contribute about $1.692 billion.

One simple version could be:

• Q1: $608 million actual

• Q2: $700 million high-end guidance

• Q3: $800 million

• Q4: $892 million

• FY 2026: $3.0 billion

The problem is that this would not really show clean acceleration into Q4. It would imply Q3 growing about 14% sequentially and Q4 growing about 11% sequentially.

So if management is serious that the back half should accelerate, and if monthly subscriber additions really are running north of 100k in weight loss alone, then the full-year guide looks conservative to me.

This is not a precise model. I am not pretending to know Q3 and Q4.

But the direction is clear.

Hims is guiding to a year where growth reaccelerates, while also telling us that the biggest category is seeing record-level demand. That combination does not sound like a business in structural decline.

It sounds like a business sandbagging a little.

And this excludes Eucalyptus.

Hims valuation in 2027

The Eucalyptus acquisition is also important.

Hims announced the deal in February. Eucalyptus is a consumer healthcare platform with operations in Australia, the UK, Germany, Japan, and Canada. Hims disclosed that Eucalyptus had an annual revenue run-rate north of $450 million and delivered triple-digit year-over-year ARR growth in every quarter of 2025, while operating within line of sight of profitability.

The transaction is expected to close around the middle of 2026, subject to approvals, and Hims explicitly excluded any Eucalyptus contribution from its Q2 and full-year 2026 guidance.

This means the reported guidance does not fully capture the new international scale.

If Hims exits 2026 near a $3 billion revenue run-rate and then layers in Eucalyptus, which was already north of $450 million ARR and growing very fast, the combined business could be much larger than what the current headline guidance implies, probably around $4 billion revenue run-rate in 2027.

At around a $6 billion market cap, it sounds a great deal to me.

No financial advice, of course. Do your own work.

Hims is an AI company and providers are training the model

Now we get to the part that I think almost everyone is missing.

Hims is an AI company.

I know this sounds like a cliché because every company now says it is an AI company.

But with Hims, the claim is not marketing.

It is structural.

In my first Hims deep dive in February 2025, I framed one of the key points as: a critical factor in leveraging the quantum leap in AI technology.

The idea was simple.

Healthcare AI is only as good as the feedback loop behind it.

You need consumer intent. You need symptoms. You need diagnosis. You need provider decisions. You need treatment plans. You need side effects. You need outcomes. You need longitudinal data. And you need all of that in one system.

Most healthcare companies do not have this.

Hims does.

Mohamed ElShenawy, Hims’ CTO, explained it clearly on the call:

We’ve embedded intelligence at every step of the care journey, while keeping independent providers responsible for all clinical decision-making. For example, we currently have an AI copilot live on the provider side of the platform that drafts contextual responses on behalf of our care coaches, which are then reviewed by the care coaches before being sent.

This is exactly the loop.

AI drafts. Humans review. Humans correct. The system learns.

And then he made it even more explicit:

We currently support tens of millions of customer touch points annually and independent providers view, correct and approve AI inputs that make up our platform design. This means we are generating clinician-verified training signals for our models. This is the highest quality label in AI any company can hope for, and it can’t be acquired.

That sentence is the whole thesis.

Clinician-verified training signals.

This is not generic web data. This is not scraped text. This is not a chatbot guessing from public medical information.

This is real healthcare workflow data, corrected by providers, attached to actual patients and outcomes.

That is very hard to buy.

It has to be earned through volume, trust, workflows, regulation, and time.

And Hims has been building it for years.

I want to be much more direct here.

Providers are training the Hims AI model.

Yes, today providers remain responsible for clinical decisions. They have to. Healthcare is regulated, sensitive, and high trust.

But look at the architecture.

Hims is not simply adding ChatGPT on top of a telehealth workflow. Hims is building the workflow so the human provider corrects, approves, rejects, escalates, and labels what the AI does. That is exactly the feedback loop you need if your goal is to train proprietary healthcare models.

Here is the important part: providers are training the system that could automate a large part of provider work over time: AI handles the repeatable part, guardrails decide what can be done automatically, escalation rules decide what needs a clinician, and the clinician keeps improving the system every time he or she intervenes.

This is why I think Hims will eventually train at least two kinds of proprietary models.

First, a model to predict disease risk or likely conditions from symptoms, clinical history, labs, wearables, medication response, side effects, and outcomes. This may not even be a classic LLM. It could be a more specialized model, maybe an encoder-style system, because the task is not “write me a nice paragraph.” The task is: given this patient profile, what is probably going on?

Second, a model to recommend the next best treatment path from symptoms, history, predicted disease, prior responses, contraindications, and outcomes. In other words: if this is the likely problem, what should the platform suggest, prescribe, monitor, or escalate?

There can still be a frontier model as the orchestrator. There can still be a 24/7 companion talking to the subscriber. But the real IP, in my opinion, is not the chat interface. The real IP is the clinical prediction and treatment-decision layer trained on Hims’ own loop.

Management is already pointing in this direction.

Labs AI explains biomarker results in the unique context of each customer, flags what matters, and knows when to recommend escalation to a provider.

The AI weight-loss companion will proactively support customers, reach out at the right moment, and prompt clinician engagement when needed.

Providers are in the loop more to review and validate what AI does or would do, rather then the other way around. That is the architecture of an AI-native healthcare company.

Hims is not selling prescriptions online.

Hims is building an intelligent care layer between the consumer, provider, medication, lab result, wearable, side effect, and outcome.

That is a completely different business.

Switching cost as a function of time

I have a personal example I want to share.

I built a system based on Hermes Agent, which I call Max, to track what affects my health.

Max lives on a Mac mini. He has access to my Obsidian notes and my Google Sheets database. I speak to him on Telegram. He knows my diet, my habits, my baseline routine, and every day he asks me if I have anything to declare that deviates from normal.

It can be food, training, symptoms, sleep, supplements, anything worth registering.

I send a voice note and he logs it.

Every six months, I do blood work. I send Max a picture of the results and he helps me record the markers in the database.

The goal is simple: correlate what I do with how my biomarkers change, and then improve the markers over time.

For example, from October 2025 to May 2026, my testosterone increased from 3.89 to 5.1, probably helped by keto/carnivore, more sun, and heavy lifting.

This is useful.

But 99.9% of people will never build something like this.

They do not want to manage notes, spreadsheets, automations, reminders, and lab data.

They want the outcome.

If Hims can make this kind of proactive health companion easy, cheap, regulated, and personalized, then the value proposition becomes much stronger than “buy this treatment online.”

It becomes: stay on the platform because the platform knows you better every month.

The longer you stay, the more data you accumulate.

The more data you accumulate, the more useful the recommendations become.

The more useful the recommendations become, the harder it is to leave.

This is switching cost as a function of time.

And it is also a network effect, because every patient interaction, provider correction, treatment journey, and outcome can improve the system.

That is why Hims could become much more defensible than people think.

Hims building a moat

Management basically described the same flywheel:

With this infrastructure, we are strengthening the closed-loop proprietary data flywheel that competitors cannot recreate. We are one of the only platforms in healthcare where consumer intake and diagnosis, treatment journey, provider decisions and eventual outcomes all live in a single stack, and we’ve designed our platform to get stronger as these insights grow.

This is exactly what I want to own.

Not just a digital clinic.

A full-stack healthcare data loop.

Intent → intake → diagnosis → treatment → titration → side effects → outcomes → better models → better care → more customers → more data.

That is the flywheel.

And because Hims is consumer-first, the company is not waiting for employers, insurers, hospitals, or legacy systems to move.

It goes directly to the person who wants to feel better.

This matters.

The traditional healthcare system is not designed around consumer delight. It is slow, fragmented, and reactive.

Hims is trying to make healthcare feel more like a high-frequency consumer product.

That is why I think the company can win share in markets like weight loss, sexual health, dermatology, mental health, longevity, labs, and eventually broader preventive care.

The more categories they add, the more complete the customer profile becomes.

The more complete the profile becomes, the more valuable the intelligent layer becomes.

This is why the Eucalyptus acquisition also matters strategically, not just financially.

Different geographies mean different populations, disease profiles, regulations, medications, and care patterns.

That can make the model better.

Hims said it directly:

We believe our global scale will act as a force multiplier with models reflecting the populations, disease profiles and care patterns of multiple geographies, fundamentally improving the intelligence we are building.

This is the part I think investors are underestimating.

The data advantage is not just US GLP-1 demand.

It is global, multi-category, longitudinal consumer health data inside a regulated care workflow.

Lorenzo2cents (L2C) take aways and performance

I have never been more excited about Hims.

The vision and the catalysts are finally coming together at the same time.

GLP-1 demand is reaccelerating. Novo gives Hims a cleaner regulatory path. Eucalyptus adds international scale. Guidance looks too low. The AI/data flywheel is no longer hidden in the background. Labs, wearables, biomarkers, companions, and provider-verified training signals are turning Hims into something much bigger than a telehealth app.

This is the strongest version of the Hims thesis I have seen so far.

My position is fully built at around 10.3% of my portfolio.

But if the market gives me another dislocation, I could still use options to take advantage of it.

I do not own Hims because of a temporary GLP-1 trade.

I own Hims because I think it can become the default consumer healthcare platform for people who want to feel good, perform, lose weight, optimize biomarkers, and live longer.

That is a massive market.

And if Hims is right on the AI layer, this can become a winner-takes-most business.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the February 8th 2025, when the stock price was $42.54.

-39% DDTD

If you want access to:

• My portfolio

• all my trades in real time

• my price targets and how I calculate them

• Business Ontology content

Join the Business Ontology Community

My price targets will be shared in the Telegram Group and explained on my Youtube channel.

I share my thoughts on X.

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

Marc Boroditsky, CRO at Nebius, put it clearly:

The referenced pipeline growth of 3.5x a quarter, which -- 3.5x quarter-over-quarter, which we’re very proud of, is for our AI cloud business, and it does not include any strategic hyperscaler deals like the Meta deal. It does include qualified opportunities across our core AI cloud and Token Factory products as well as across all of our key customer segments, including AI natives, software vendors and enterprises.

What we can share about conversion is that we have maintained our solid win rates at the same time as we’ve accelerated our sales cycles and increased our average selling prices. And you can see this with some of the strong wins that we have, such as Sword Health in healthcare, life sciences and Rhoda and 1X in physical AI and core automation in one of our AI-native model builders as well as Revolut and monday.com, new customer wins for Token Factory.

What we are doing is enabling our go-to-market teams to have a consultative conversation with our customers about their plans and for current and future workloads, including, as an example, what they’re thinking about with regard to Vera Rubin. We’re also focusing and scaling our go-to-market and success teams to help customers to realize their plans, which turns into durable revenue for us.

This is exactly what I wanted to see.

Nebius is not simply selling GPUs into a hot market. It is starting to convert GPU scarcity into a broader AI infrastructure relationship. That is a very different business.

The company’s ability to deliver both high-performance, large-scale clusters and smaller-scale, on-demand compute is becoming a real advantage. In Q1, that advantage translated into a sequential re-acceleration in revenue. The momentum was broad-based and compounding:

• Pipeline generated in the quarter reached a new record, increasing approximately 3.5x quarter over quarter.

• Pricing continued to rise for new-generation GPUs.

• Older-generation chips also saw strong pricing support.

• Average deal sizes increased across both new and existing customers, driven by pricing, GPU commitments, and longer contract durations.

• Demand widened across verticals, model builders, enterprises, software vendors, AI-native companies, and inference workloads.

The most important detail is that the 3.5x pipeline growth excludes strategic hyperscaler deals like Meta. In other words, this is not a single large customer distorting the picture. This is the core AI cloud and Token Factory business accelerating.

Contracted capacity already exceeds 3.5 GW, far surpassing the 3 GW goal Nebius had set for the end of the year. Because of that execution, management raised its contracted power guidance to more than 4 GW by year-end.

This matters because, in AI infrastructure, demand is not enough. Everyone has demand. The question is who can convert demand into capacity, capacity into revenue, and revenue into a deeper customer relationship.

Nebius is starting to show that it can.

The World’s Biggest Factory Will Produce Tokens

My core thesis is simple: the biggest factory of the next decade will not produce cars, steel, semiconductors, or oil.

It will produce tokens.

My thesis is that AI can dramatically expand economic output over the next few years. But I also believe that, relative to the size of the economy, there will be fewer and fewer winners. This is because the companies that successfully build AI-powered flywheels will often capture entire market segments.

Once the data/AI flywheel is built, working, and spinning, it becomes extremely difficult to stop. The company gains more users, which creates more data, which improves the product, which attracts more users, which creates more data, which improves the product again.

The flywheel spins faster and faster.

And then another force kicks in: talent.

To make the data/AI flywheel spin, you need exceptional talent. Talent is naturally limited. And the best talent will increasingly concentrate inside companies where the flywheel is already spinning, because those companies will have more ambition, more resources, better data, better compensation, and a higher probability of winning.

So the companies that reach the “spinning flywheel” stage early will attract more talent. The companies that do not reach that stage soon enough will be left behind.

This is why I believe the ingredients of an AI flywheel are:

1. Visionary and brave management.

2. Data.

3. Talent.

But the first factor is the trigger.

Visionary management starts the process. It chooses the right market, takes the hard decisions, collects the right data, attracts the right people, and pushes the organization through the painful transition before competitors understand what is happening.

Once the flywheel spins for a few years, the company becomes almost impossible to catch.

I also believe that most of these AI flywheels will be built by AI-native companies, not incumbents. Most incumbents carry too much legacy. Their culture, incentives, shareholders, technical debt, and profit pools make internal disruption almost impossible.

The exceptions will be founder-led incumbents where the founder still has enough power, hunger, and vision to take painful decisions against the will of shareholders focused on short-term profit and risk avoidance.

$META is one example. Mark Zuckerberg has been systematically throwing billions at new bets and restructuring the company to take full advantage of the AI revolution.

Alphabet is another example. It has been willing to put at risk its highly profitable Search advertising business in order to push the AI-based business.

But most winners will be AI native.

And many of those AI-native companies will build on Nebius Token Factory.

Why?

Because Nebius is trying to offer something more valuable than raw GPU access. It is building an environment where AI-native companies can build with ownership, access the tools they need, and generate tokens at the lowest possible cost.

That is the key.

In an AI-native economy, tokens are not just a technical output. Tokens are the cost of doing business. They are the new unit of production.

Routing data from OpenRouter suggests proprietary models still dominate usage, with proprietary non-Chinese models averaging around 70% of token volume in 2025. But the direction of travel is clear: open models are gaining share as performance improves. My view is that this mix can flip over the next two to three years.

For now, open models are not good enough for many use cases. But if they reach something close to GPT-5.5 / Opus 4.7 level, everything changes. I think that could happen sooner than most investors expect, but this remains a forward-looking bet, not a fact.

There will always be specific use cases where organizations prefer frontier closed models. But over time, that percentage should decline. Most workflows will not need the absolute best model in the world. They will need the best cost/performance model for that specific job, integrated into a reliable workflow, running at scale.

This is where Nebius Token Factory becomes interesting.

My view is that Token Factory has the potential to become the preferred environment where companies build their AI-powered workflows. From there, they can still call external frontier models for specific tasks when needed. But the main workflow, the infrastructure layer, the orchestration layer, and the economics sit inside Nebius.

That would make Token Factory a gatekeeper.

And as human labor is increasingly leveraged or replaced by AI, a larger and larger percentage of every company’s cost structure will be related to token generation.

That is why I believe Nebius Token Factory has the potential to become the world’s biggest factory.

Not because it will produce the most GPUs.

Because it can become one of the places where the new economy produces work.

Significant Token Factory customer wins in Q1 include:

• Revolut, which is developing a platform to simplify finance for businesses and consumers, was able to remove human intervention from 80% of support chats and handle 1.2M chat tickets per month on Token Factory.

• monday.com selected Token Factory to support its AI work platform that helps manage, orchestrate, and execute workflows.

These customers show why Nebius is not just another GPU reseller. The company is moving up the stack, closer to where the customer’s AI workload is actually built and scaled.

Roman Chernin explained the logic very well:

First of all, I want to say that we are super excited that these 2 incredible teams of talented people from Eigen and Clarifai will join us. And to deep dive into the rationale, let’s start from foundations.

Our view is that we should own the compute stack. That is where our vertical integration, our supply chain depth and our hardware engineering generate advantage and it’s also the layer that drives the bulk of our economics. But the compute stack, we built the full cloud solution and software plays the role of enabler. By the way, we partner where partnership is the right path. And as you see now, we use M&A selectively where it accelerates our road map, brings in proven developer adoption or as capabilities complementary to what we are building.

Acceleration is the key lens we apply to every potential transaction where we can find rare talent or proven adoption. This is, by the way, the example of Tavily that has incredible developer adoption that would, in general, take us meaningfully longer to build organically, acquisition is the fastest path. And we evaluate every potential deal against the clear criteria. Does it deepen customer engagement, increase lifetime value, unlock the new category of customers or use cases we can address and in general, strengthen our position as a full stack AI cloud.

This is exactly the right strategy.

Nebius does not need to become a traditional software company. It does not need software as a separate revenue stream. It needs software as an enabler of compute consumption, customer stickiness, workload expansion, and lifetime value.

Roman made this even clearer:

As I said, we look at the software as an enabler. So it’s not that we build the software to generate a separate revenue stream. The software first of all plays the role of unlocking the new capabilities for us, unlocking the new opportunities in the types of the workloads that are growing on the market and the types of customers that we can address.

Software changes the shape of the customer relationship. Every layer of software unlocks another group of users and customers. We want to meet customers where they need us and let them consume our vertically integrated solution in the way that they need, and it might be a different way for different types of customers.

Customers come to our platform for different needs. In essence, they all need to run AI at scale — which means they need compute. But for example, people who use our multi-tenant cloud, they -- to a big extent come for large training jobs. People who -- and these are like research-driven, data scientist-driven workloads. People who come to Token Factory, they build vertically integrated vertical AI products or apply AI in their enterprises. And they come for the tokens. And moving forward, we’ll see new ways to consume infrastructure at scale that will be the agentic -- end-to-end agentic workloads.

This is the point most investors still miss.

If you only look at Nebius as a GPU cloud, you compare it with every other company that has land, power, and Nvidia chips.

But if you understand Token Factory, the comparison changes.

Nebius is trying to become the vertically integrated infrastructure layer where AI-native companies generate tokens, build workflows, deploy agents, scale inference, and eventually run entire AI operating systems.

That is a much bigger opportunity than renting GPUs.

Why Nebius is well positioned to win the AI supply chain bottleneck race

The most common argument I hear from investors and finfluencers is that power supply is the new bottleneck for AI infrastructure. Therefore, companies like IREN, which already own a decent amount of connected power, will win the race against companies like Nebius, which supposedly will not build enough capacity on time to serve demand.

Supporters of this thesis even claim that companies like IREN are delaying deals on purpose, waiting for the moment when the supply/demand imbalance is at its peak, so they can extract better terms because they own the scarcest resource.

I recognize the bottleneck. It is real.

Power matters. Land matters. Copper matters. Grid connection matters. Speed of execution matters.

But I think this thesis is still too shallow.

It can work in the short term. In a very tight market, the owner of the scarce physical resource can capture a lot of value.

But over the medium to long term, let’s say beyond three years, the winner will be the company that delivers the highest value per token.

And to deliver the highest value per token, you need to deliver now.

A few months of delay can mean losing the race. In AI, timing is not a detail. Timing is strategy.

The AI race is not just about land and copper. That is the physical layer. It matters, but it is not the full game.

The AI race is about spinning the AI flywheel faster than anyone else in your domain.

We are already seeing this with models.

OpenAI has more compute available than Anthropic. Yet Anthropic has been gaining a lot of ground, especially in coding and enterprise. Why? Because Anthropic found a way to spin the AI flywheel faster in a specific domain.

It focused on coding and enterprise instead of trying to win every consumer use case at the same time. The iteration pace in that domain is faster. The feedback loops are clearer. The willingness to pay is higher. And once your LLM can improve the workflows of the people building the next LLM, the flywheel becomes extremely powerful.

At that point, the chance for a competitor to catch up becomes much lower, unless the leading company hits a technological limit soon enough for laggards to recover.

The obvious objection is:

What about compute? Anthropic is constrained. OpenAI will catch up.

But Anthropic just got helped by Elon.

And that is exactly my point.

If you have the talent, the demand, and the best product, you will find the resources.

Resources are attracted to the winning player.

As a supplier of power, land, compute, capital, or any constrained resource, who would you rather sell to? The company with the strongest product and fastest flywheel, or the company with idle capacity and no clear edge?

This is the same dynamic we see with talent. Talent flows to the winning player. Capital flows to the winning player. Suppliers flow to the winning player. Partners flow to the winning player.

This is why I do not believe the AI infrastructure race will be won simply by whoever has the most connected power today.

Connected power is valuable.

But connected power without a differentiated customer relationship becomes a commodity.

Actually, I think the market is framing the bottleneck in the wrong unit.

The scarce resource is not power in isolation.

The scarce resource is useful tokens per watt.

This is where software becomes more important than power.

SemiAnalysis InferenceX data shows how dramatic the difference can be. According to NVIDIA, recent SemiAnalysis InferenceX data shows that NVIDIA software optimizations and Blackwell Ultra GB300 NVL72 systems can deliver up to 50x higher throughput per megawatt and 35x lower token cost than Hopper for DeepSeek-R1.

Think about what that means.

If one operator owns 1 GW of connected power but converts that power into tokens inefficiently, and another operator owns less power but converts every megawatt into dramatically more useful inference, the second operator may generate far more economic output.

The market sees megawatts.

Customers see latency, reliability, model quality, throughput, cost per token, developer experience, and time to production.

Capital sees revenue per megawatt.

That is why the most important metric in AI infrastructure will not be only connected power. It will be revenue per megawatt.

Power is the input. Tokens are the output. Software determines how efficiently one becomes the other. That is why “I own power” is not enough as a long-term thesis. Power without software optimization is like owning oil without a refinery: valuable, but not the highest-value part of the chain.

The best AI infrastructure companies will not only secure electricity. They will continuously improve scheduling, orchestration, utilization, inference serving, model optimization, networking, cooling, workload routing, reliability, and customer workflow integration.

That is exactly where Nebius is trying to go with Token Factory.

The real question is: who can turn power into tokens, tokens into workflows, workflows into customer lock-in, and customer lock-in into a compounding data/software/infrastructure flywheel?

That is where Nebius is better positioned than the market understands.

Nebius is not just trying to build capacity. It is trying to build the AI supply chain layer where customers come not only for compute, but for tokens, tools, workflows, and eventually agentic infrastructure.

That is a much more durable position.

Why Nvidia Has Every Reason to Back Nebius

The second key piece of the thesis is Nvidia.

As hyperscalers increasingly diversify chips and invest more in proprietary silicon, they are becoming, de facto, competitors to Nvidia.

Google has TPUs. Amazon has Trainium and Inferentia. Microsoft is working on its own chips. Meta is developing more internal AI silicon. Tesla and xAI may eventually go deeper into custom silicon as well.

This does not mean Nvidia is weak. Nvidia’s moat is still extremely strong.

But it does mean this will not be a winner-takes-all chip market forever.

Nvidia needs to defend its ecosystem, keep customers locked into CUDA, maximize distribution, and avoid becoming overly dependent on hyperscalers that are increasingly incentivized to reduce Nvidia dependence over time.

This is why neoclouds matter.

Recent news about Google and Blackstone creating a new AI cloud company confirms the direction. For Google, it is not enough to use TPUs internally and inside Google Cloud. It is building an external, neocloud-like distribution surface for its chips.

The probability that Amazon eventually does something similar is high. Elon could follow in a few years. AMD could do the same. Chinese players may eventually enter the American and European markets as well.

The implication is clear: chip companies need distribution.

And Nvidia has every incentive to support strong independent distribution partners more than ever.

Neoclouds like Nebius, CoreWeave, and IREN will be key to that strategy. Over time, they can become one of Nvidia’s most important go-to-market surfaces, especially for AI-native companies, model builders, enterprises, and software vendors that do not want to depend entirely on hyperscalers.

But not all neoclouds are equal.

The winner will not simply be the one that buys the most GPUs. The winner will be the one that creates the most customer stickiness.

Stickiness is not built by selling bare metal.

That is why I think IREN’s long-term upside depends on whether it can move beyond scarce power and build real software/workload stickiness. If it remains mostly a capacity seller, I think it will be structurally disadvantaged versus Nebius and CoreWeave.

IREN now seems to be trying to pivot toward a Nebius-like model, more focused on software edge and more targeted to end users instead of only hyperscalers. But in my view, the burden of proof is now on IREN to show it can build that ecosystem before Nebius and CoreWeave compound their lead.

The real race is probably between CoreWeave and Nebius.

CoreWeave has strong execution, scale, and market credibility. But Nebius is more clearly focused on building a lasting moat around the Token Factory ecosystem.

That is what makes Nebius different.

It is not only trying to be a GPU landlord. It is trying to become the place where AI-native companies build, deploy, and scale their token production.

This is, in my view, why Jensen has been so vocal about Nebius lately.

He understands that Nebius can become one of Nvidia’s most important distribution channels. Not because Nebius is the biggest today, but because Nebius is building the kind of customer relationship Nvidia needs in the next phase of the AI infrastructure race.

Nvidia also understands the quantitative side better than anyone.

If SemiAnalysis data shows that software optimizations and new Nvidia systems can create up to 50x more throughput per megawatt and 35x lower token cost than the previous generation for a relevant inference workload, then Nvidia’s objective is obvious: put those systems in the hands of operators that can actually turn them into scaled token factories.

Not every owner of power can do that.

The best partner for Nvidia is not the company with the loudest announcement about megawatts. It is the company that can convert Nvidia’s full-stack platform into customer workloads, high utilization, developer adoption, and recurring token demand.

This is why Nebius matters strategically: it gives Nvidia a partner that can turn full-stack hardware and software into recurring token demand, not just one-off GPU capacity.

That is why Q1 mattered.

Not because one quarter changes the thesis.

But because the quarter showed that the thesis is starting to play out:

• Demand is accelerating.

• Pipeline is exploding.

• Contracted power is already ahead of plan.

• Token Factory is landing real customers.

• Software is being used as an enabler of compute consumption and customer lock-in.

• Management understands the strategic importance of owning the compute stack while moving high enough in the stack to shape customer behavior.

• Nvidia has every reason to support the strongest independent AI cloud distribution partners.

The market still wants to reduce Nebius to a simple AI infrastructure story.

I think that is wrong.

Nebius is not just building data centers.

Nebius is trying to build the token factory layer of the AI economy.

And if the economy really becomes increasingly AI-native, the biggest factory in the world will not be measured in square meters, machines, or human workers.

It will be measured in tokens.

Lorenzo2cents (L2C) take aways and performance

My core takeaway from this quarter is simple:

1. Demand is outpacing supply.

2. Nebius is scaling capacity fast.

3. AI-native customers are becoming enterprises.

4. Nebius is building a software moat.

5. And open-source models are an underrated tailwind because they accelerate tokenization and commoditize the model layer, pushing value into delivery.

If Nebius becomes the best “token factory” for robust performance at the cheapest price, then it is not “a neocloud”.

It is an emerging hyperscaler.

And that is exactly why I’m here.

As always, here’s the Deep Dive To Date (DDTD): Stock performance since my first deep dive and when I bought in on July 28, 2025, at $51.63.

4x DDTD

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Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

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Ciao

Lorenzo

I still believe that.

The app is getting better. Speaking practice is becoming more central. AI is helping them create content at a speed that would have been impossible a few years ago. The long-term thesis, in my view, is still the same: Duolingo is not just a language app. It is trying to become the learning ontology. The place where AI is not just a chatbot, but a teacher.

That was the core of my last $DUOL update: Duolingo’s AI Pivot: The Teacher Model Thesis.

But Q1 2026 also exposed the other side of the story.

Duolingo is becoming a better teacher, but it may have a problem finding enough new students.

And for a company valued on years of user growth, that matters.

Q1 2026: good numbers, but the mix matters

The headline numbers are not bad.

In Q1 2026, Duolingo reported:

• DAUs of 56.5 million, up 21% YoY.

• MAUs of 137.8 million, up only 6% YoY.

• Paid subscribers of 12.5 million, up 21% YoY.

• Revenue of $292.0 million, up 27% YoY.

• Total bookings of $308.5 million, up 14% YoY.

• Free cash flow of $147.8 million, a 50.6% FCF margin.

So this is not a broken business.

Actually, the opposite. Duolingo is still growing revenue at 27%, printing free cash flow, and improving monetization. Paid subscriber penetration increased to 9.4% from 8,9% in Q1 2025 and 9,3% in Q4 2025.

The company is clearly getting better at farming the existing base.

The problem is the top of the funnel.

MAUs grew only 6% YoY and 3,5% QoQ, while DAUs grew 21% YoY and 7% QoQ.

Being MAU a leading indicator for DAU, and being the former in deceleration, the latter will likely follow soon.

That means engagement is improving, but the pool of users is not expanding nearly as fast as before.

DAU/MAU went from 35.8% to 41.0% YoY.

That is impressive product execution.

But it also tells you where the growth is coming from: retention, habit, conversion, monetization.

Not enough from new user acquisition.

The bull case is still alive: AI is making Duolingo a better product

The best part of the quarter was product velocity.

Duolingo is attacking the biggest weakness of the app: speaking.

Management wrote in the shareholder letter:

Speaking practice, historically the biggest gap for learners on Duolingo, is now a core part of the product.

They added that Video Call remains one of their most impactful paid features and that, over the last year, they have “more than doubled the average number of words spoken per user in Video Call.”

This matters because Duolingo’s historical weakness was obvious.

It was great at making you show up every day. It was great at teaching vocabulary and grammar in small chunks. It was not great at making you speak.

That gap is now closing.

They introduced spoken tokens, flashcards, and Speaking Adventures. Management described spoken tokens as a change that “enables almost every exercise in Duolingo to become speaking practice.”

This is exactly what I want to see.

If Duolingo becomes the place where a beginner can go from zero to real conversation, the product becomes much more valuable. Not because it adds another feature, but because it moves closer to the real job to be done: teach me until I can actually use the language.

AI is also changing the content creation curve.

This was the strongest quote of the quarter:

In Q1 2026 alone, we published 20,500 course units across our language courses, up from 7,100 per quarter in 2025 and 1,800 per quarter in 2024.

Management added that automating more of the content creation process lets them push changes across many courses at once and improve quality faster.

This is the AI tailwind.

Most consumer AI stories are vague. Duolingo’s is very concrete: more content, faster iteration, more speaking, more personalization, lower content bottlenecks.

This supports my teacher model thesis.

A generic LLM can translate, explain grammar, or roleplay a conversation. But Duolingo has the structured curriculum, the user behavior data, the retention loops, the A/B testing engine, and the feedback loop on what actually helps people learn.

In my view, that is the moat.

The raw model is not the product. The learning system around the model is the product.

But the funnel is the problem

Here is the part I do not like.

On the call, they were asked directly about MAU growth and top of funnel. Luis von Ahn answered:

The reality is that top of funnel has been about flat for certainly for this quarter, and we would like to accelerate it.

He then explained that the historical growth driver has been word of mouth:

Again, the main thing that has been responsible for top of funnel historically has been word-of-mouth. And word-of-mouth is this interesting thing that is beautiful because it is free, but we do not have that much control over it in terms of being able to measure it the same way that we can measure retention.

That is the key issue.

Duolingo has world-class retention mechanics. They can measure them. They can A/B test them. They can improve them.

But the main top-of-funnel engine is word of mouth, and management openly admits they do not have the same level of control there.

The Google Trends screenshot makes the same point visually.

Duolingo reached peak search interest last year around the Dead Duo campaign. That campaign was genius: almost free, impossible to ignore, and very Duolingo. But that is exactly the point. You cannot schedule virality every quarter.

After Dead Duo, interest did not keep compounding. It got worse. That does not mean the brand is dead. It means word of mouth is not a controllable machine. It is a powerful engine when it works, but it is not the same as paid distribution, network effects, or owned social graphs.

This is not fatal.

But it is a different type of risk.

If MAU growth keeps slowing, DAU growth will eventually follow. DAU/MAU cannot expand forever. At some point, the ratio saturates. When that happens, the business needs new users again.

This is why I am more cautious after Q1.

The market is not wrong to ask whether Duolingo’s growth is structurally slowing. The question is whether this is a temporary transition year, while management invests in teaching better and user growth, or whether the core organic acquisition engine is reaching saturation in the most mature markets.

The D2C lesson: distribution is everything

The more I follow companies like Duolingo, Oddity, Hims and Lemonade, the more I think the same lesson keeps coming back.

D2C is brutally hard.

When it works, it looks magical. You own the customer. You own the data. You own the product loop. You can compound without paying rent to a platform forever.

But distribution is still the bottleneck.

Meta sits on a goldmine because it owns attention and distribution. Most D2C companies have to fight for it every day.

Duolingo’s advantage was that it escaped the usual D2C trap. It did not need to spend heavily on performance marketing because the product itself created word of mouth. The owl became the distribution channel.

That was a superpower.

Now it may also be the constraint.

Luis was very honest about performance marketing:

Performance marketing at Duolingo has been this interesting thing that because our free version is so good, it has not been easy for us to do profitable performance marketing because what happens is we acquire people and then they are super happy as free users.

He added:

At the moment, in some geographies, we have profitable performance marketing, but in many geographies, we do not.

This is almost an Innovator’s Dilemma.

Duolingo’s free tier is the reason the company became huge. It creates trust, habit, brand love, and scale. But the same free tier makes paid acquisition harder because many acquired users land in the free product and stay there.

If they make the free tier worse, they damage the product and the brand.

If they keep it too good, performance marketing ROI is limited.

So the business has to thread a very narrow needle: keep the free product excellent enough to drive word of mouth, while making the paid product valuable enough to justify acquisition spend.

That is not easy.

Asia and China may be the answer

There is one important counterpoint.

The problem is not equally bad everywhere.

Luis said Asia is the fastest-growing region, and China is particularly interesting. On the call, he said:

One of the things that is interesting about Asia is in a number of large Asian markets we can do profitable performance marketing

He also said: “We are able to acquire profitably” in China, where the users are English learners.

That matters.

If Duolingo can use performance marketing profitably in underpenetrated Asian markets, then the top-of-funnel issue may be more of a mature-market problem than a global problem.

The China brand partnerships are also interesting.

Luis mentioned Luckin Coffee and a coming McDonald’s China partnership, and said:

We have had incredible brand partnerships in China.

But he also made the more important point:

China is not just growing fast because of the great partnerships. I think it is kind of the other way around. I think the great partnerships are coming in part because we are growing fast and because we are seen as a very cool brand.

This is the bull version of the story.

Duolingo may not need to solve paid acquisition everywhere. It may need to professionalize it where it works, use partnerships where the brand has pull, and keep improving the product so word of mouth reaccelerates.

That is possible.

But it is not yet proven.

The other answer: make learning multiplayer

There is another path I would like to see more aggressively.

Duolingo should take the chess playbook and apply it to languages, math, music, and every course where it makes sense.

In Q3 2025, management wrote that chess had become Duolingo’s fastest-growing subject. In the same shareholder letter, Luis also said they had introduced player-vs-player on the chess course.

That is the most interesting user acquisition clue, in my view.

Multiplayer changes the growth equation.

A solo lesson is retention.

A multiplayer lesson can become distribution.

If I challenge a friend to a chess puzzle, a vocabulary duel, a speaking battle, a grammar race, or a math challenge, I am not just using the product. I am pulling someone else into the product.

That is how word of mouth becomes less random.

Today, Duolingo’s social graph is still relatively weak compared to what it could be. Streaks, leagues and leaderboards are powerful, but they mostly make the individual user come back. Multiplayer can make users bring other users back.

This is where the product can create its own acquisition loop.

Imagine language learning with real-time speaking battles. One user learning Spanish, another learning English. Short, structured, gamified conversations where both users improve and both users have a reason to invite friends.

That would be much closer to a true learning network.

And it fits Duolingo perfectly because the company already knows how to make hard things feel like a game.

The 2026 strategy sounds like “improve the product and pray”

I am exaggerating a little, but not much.

Management’s strategy for user growth is basically:

1. Teach better.

2. Improve speaking.

3. Make the free tier more engaging.

4. Invest more in marketing, especially in underpenetrated regions.

5. Hope better learning outcomes create more word of mouth.

This can work.

Actually, it is probably the right strategy for a product like Duolingo. You do not want management to ruin the product with aggressive monetization. You do not want them to turn the owl into a CAC machine.

But as an investor, I also have to be honest: this is less controllable than retention optimization.

Luis said it himself. Word of mouth is beautiful because it is free, but they do not have the same measurement and control they have with retention.

That is the risk.

L2C take aways and performance

I am still bullish on Duolingo long term.

The product is improving. AI is a real tailwind. The company is highly profitable. The balance sheet is strong. The learning ontology thesis is intact.

But Q1 made the main risk clearer.

The question is no longer: “Can Duolingo use AI to make the product better?”

I think the answer is yes.

The question is: “Can a better product restart top-of-funnel growth?”

That is much harder.

If the answer is yes, then $DUOL remains one of the most interesting consumer AI companies in the market. It would mean the company can use AI to improve teaching quality, increase engagement, expand monetization, and eventually reaccelerate user growth.

If the answer is no, then Duolingo may still be a very good business, but the multiple has to come down because the market will stop underwriting hypergrowth.

For now, I see Q1 as a yellow flag, not a red flag.

The business is not deteriorating. It is evolving.

Duolingo is becoming a much better teacher.

Now it needs to prove it can still find new students.

Looking at my Business Ontology visual, while Duolingo is performing well on the first two pillars (“Engagement” and “Paid Subscription Penetration”), it needs to improve a lot on User Growth.

As always, here is the “Deep Dive To Date” (DDTD), that is how the DUOL stock is performing since my initial deep dive on the January 11th 2025, when the price was $318.15.

-67% DDTD

I remain long .

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My price targets will be shared in the Telegram Group and explained on my Youtube channel.

Please note that:

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

So what becomes scarce?

The domain map.

The workflow.

The data.

The ability to turn AI into action inside a specific industry.

That is what I call a vertical ontology.

$LMND is building it in insurance.

Customers, policies, claims, risk, fraud, pricing, and service interactions. Insurance is a perfect domain for an ontology because the business is basically a machine for pricing uncertainty. The better the object graph, the better the underwriting. The better the claims loop, the better the data. The better the data, the better the model.

That is the flywheel.

I have been very vocal about this, but let me say it again.

Lemonade is becoming a vertical ontology because it was built around the idea of becoming an autonomous organization. Its IT systems and business processes were designed for that from day 1. And it runs mostly a D2C model, which gives it direct access to first party customer data. That data, plugged into those systems, makes Lemonade a vertical ontology that keeps learning in a reinforcing cycle.

No incumbent will ever achieve it. I think it is almost impossible because of the innovator’s dilemma, which I have already explained multiple times.

Now, on to Q1 earnings, which were stellar. Probably the best in the company’s history.

• Topline growth acceleration: At $1.33 billion, In Force Premium grew 32% – extending their streak of IFP growth rate acceleration to ten consecutive quarters.

• Revenue grew faster still, by 71% to $258 million, reflecting the impact of recent reinsurance transition and higher premium retention.

• Gross Profit: Increased 159% YoY to $100 million

• Q4 2026 EBITDA positive guidance confirmed.

So it is time for an update of my Business Ontology for Lemonade. If you want to know more about what a business ontology is, let me know in the comments and I will write more about it.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up. I break them down to their basic parts, then piece together a real thesis on what drives them. Think of it as a map of a company’s soul. It is a tight set of core indicators, tailored to each business, that shows whether it is heading in the right direction, no matter what the stock price says. These are not basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They are deeper, sharper, and linked directly to the thesis I have built on how the company creates value and competes in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks whether the company is worth buying into (or holding).

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own decisions on how much to allocate.

Now, let’s dive into Lemonade and this quarter’s update.

Business Ontology

Here’s a quick visual on the Business Ontology for Lemonade.

Annual Dollar Retention

Annual Dollar Retention has been stable at 85% for a few quarters, after trending down from 88% in Q1 2024.

Management said the decline is primarily due to the continuing impact of their “clean the book efforts” in the home business. It should start rising again soon. There is no reason to doubt it after looking at the leading indicators:

App Store and Trustpilot rankings are above 4, which is extraordinarily strong compared to peers.

Happy Customers keep using the product.

IFP Per Employee

IFP per Employee keeps rising and is about to overtake ROOT Insurance. For context,

Lemonade Premium per Customer is $424. ROOT Premium per Policy is $1423.

I could not find a perfectly comparable metric, but if you compare apples to apples the difference must be even more staggering (since a customer can have more policies).

The point is that ROOT should be much more efficient than Lemonade because any acquired or managed policy is worth on average 3 or 4 times more than Lemonade.

This is only a benchmark, but the takeaway is that Lemonade’s AI engine is firing on all cylinders, and my original thesis is slowly coming true.

The LAE ratio confirms the same point. At 6%, it is among the lowest in the industry.

Loss ratio

Loss ratio is at a healthy level: 62%. I do not expect it to go much lower than this, because Lemonade, as said multiple times, is optimizing the business to maximize gross profit, not to minimize loss ratio. The end goal is to make more money, not to have the lowest loss ratio in the industry.

To get a feel for how the company manages the trade-off between loss ratio and growth (lower loss ratio usually means higher prices, which usually means lower growth, and vice versa), let’s check my proprietary metric: Adjusted Rule of 40.

The higher, the better. Lemonade printed 69 in Q1, which is an excellent result that, if maintained, would drive outstanding gross profit growth.

As management expects the car business to drive most growth in the near future, let’s see how the same metric is performing for the car segment.

The car business is growing healthy and strong, as shown by the positive trend in this metric. It prints even better than the overall business, even though it is not perfectly comparable. The car business is still tiny in absolute terms, so it benefits from “easy” hyper-growth that inflates the metric.

If you want access to the source of all the data I track, including what I used to update my business ontology, subscribe to Lorenzo2cents.

If you are already subscribed and you still do not have access to the Business Ontology Knowledge base, join the Business Ontology Community (Telegram channel) here.

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4D Valuation Model

If you would like access to my 4D valuation model, including my price target, let me know in the comments of this post or in the Telegram channel. I will share it only if there is interest from enough people.

Lorenzo2cents (L2C) take aways and performance

My thesis on Lemonade—growth with near-zero marginal costs—is fully unfolding. As of now, there is not a single reason to doubt that 30% IFP growth for the foreseeable future, as guided by management, is achievable.

Even more, Lemonade is positioning itself to conquer a totally new market: autonomous agents and objects, which will be even bigger than the current insurance industry. If Lemonade captures a 5% market share of the US Property & Casualty (P&C), Auto Insurance, and autonomous objects market in 10 years, it will likely be a $1 trillion company.

As always, here is the “Deep Dive To Date” (DDTD), which shows how the stock is performing since my initial deep dive on September 24th 2024, when the stock price was $17.23.

3.3x DDTD

Lemonade (LMND) remains my highest-conviction holding and largest portfolio position.

Unlike other names (e.g., HIMS), Lemonade faces no acute, company-specific legal or regulatory headwinds that would derail its trajectory. Standard industry risks (e.g., compliance, reinsurance, AI model scrutiny) exist, but nothing idiosyncratic suggests near-term impairment.

The position is fully built. I have no plans to sell or add, except that I may use options or other trades to take advantage of market dislocations from real value, as I did in the recent drop. If you want to follow my moves in real time, join the Business Ontology Community on Telegram.

Join the Business Ontology Community

There you will also find the link to access my portfolio.

Here is an example of a message I sent on Monday, when I swapped my Meta shares for ZETA Global shares.

I expect Lemonade to be the primary driver of portfolio returns over the next 5+ years.

I can be and will be (hopefully not often) WRONG. This is just my personal strategy—NOT FINANCIAL ADVICE. I don’t know your financial or life situation well enough to give any recommendations. Please do your own due diligence and research. Don’t be LAZY.

Be the architect of your own destiny.

Ciao

Lorenzo

• Writing copy gets cheaper.

• Designing landing pages gets cheaper.

• Building software gets cheaper.

• Producing content (video, images, ads) gets cheaper.

• Soon, with humanoid robots, even physical goods and services will get cheaper to produce and deliver.

When production becomes commoditized, differentiation shifts to distribution.

In practice, that means a higher % of enterprise revenue will go to:

• demand generation

• retention and lifecycle marketing

• personalization and automation

• measurement and attribution (or better: outcome-based ROI)

Marketing turns into the main “battlefield” because every product starts to look similar, faster.

I bet that the following downtrend is going to reverse soon.

As the ad market consolidates into “walled gardens” (Meta, Google/YouTube, Amazon, TikTok), a larger share of every incremental marketing dollar gets spent inside closed ecosystems where the platform owns the identity, the inventory, and—crucially—the measurement.

In that world, the “tax” advertisers pay is not only higher CPMs; it’s also the inefficiency created by siloed data and last-click-style reporting. That’s where Zeta becomes a commission to pay on walled gardens: a layer that sits on top, resolves identity, and optimizes spend allocation based on outcomes—not on whatever each garden claims. Zeta’s data cloud is built around a durable person-level graph (hundreds of millions of opted-in people, with thousands of attributes per profile) and its first-party pixel footprint across massive web surfaces, which gives it an independent signal set to judge incrementality and ROI across channels.

The Meta partnership is a concrete example of why this matters: David Steinberg has said Zeta has “a matching between the Zeta ID and the Meta ID,” which lets Zeta connect exposures in Meta to downstream actions and then orchestrate the next best message/channel (e.g., follow with CTV, email, or another paid impression) based on the highest expected ROI. Put differently, if walled gardens are where the budget must live, Zeta is the vertical “marketing ontology” that makes that budget work harder—because it understands who the person is (identity), what to do next (propensity), and where to spend (allocation) in one closed-loop system.

If you have not read it, check my ZETA deep dive part 1:

If marketing is the battlefield, ROAS is the weapon

In a world where everyone can build, the winners are the companies that can sell.

For an enterprise buyer, the “best marketing cloud” is not the one with the most features. It’s the one that:

• gets the highest ROAS (return on ad spend)

• makes ROAS more predictable

• scales ROAS across channels

• proves it financially (CFO-grade reporting)

That’s why the category is structurally winner-takes-most.

If Vendor A gives you 2x ROAS vs Vendor B, you don’t “diversify vendors”. You reallocate budget.

In the last earnings call, David Steinberg (CEO) framed it very directly:

• The marketing ecosystem grew about ~10%, while Zeta grew ~30% — and he explicitly said that this is Zeta “taking meaningful market share”.

• On wallet share: he said Zeta’s 603 global enterprise clients spent about $100B in marketing last year and Zeta captured ~1.3% of that spend (~$1.3B revenue). (Math check: 1.3% × $100B = $1.3B.) He also shared a long-term goal of 10% wallet share.

• Retention backs it up: net revenue retention hit a record 120% in 2025 (up from 114% in 2024).

• He also explained the basic mechanism: “the higher we drive the return on investment, the greater the percentage of budget that our clients move to us.”

This is the “budget gravity” thesis:

1. deliver superior ROAS

2. get more wallet share

3. invest more in product + data

4. deliver even better ROAS

Flywheel.

Zeta is stealing share to Salesforce, for comparison.

If Zeta grows ~30% while the overall ecosystem grows ~10%, somebody has to be losing share.

Salesforce is a natural place to look because it’s one of the biggest incumbents in enterprise marketing.

One public data point that supports the narrative:

• Salesforce’s Marketing + Commerce segment declined ~1% YoY in Q4 FY26 (constant currency), according to Salesforce’s quarterly breakdown.

Why I think Salesforce (and other incumbents) can’t easily “catch up”:

• Legacy architecture + latency: many suites were assembled via acquisitions; stitching data + channels together often adds delay and complexity. In a ROAS world, milliseconds matter.

• Different center of gravity: incumbents are optimized around being the system of record (CRM). Zeta is optimized around being the outcome engine (identity + activation + measurement). Those are different products, cultures, and data assumptions.

• Signal disadvantage: Zeta’s data cloud/pixel footprint means more third-party-like signals feeding optimization loops. Incumbents can buy AI, but it’s harder to buy continuously-updating proprietary signals at that scale.

• Enterprise switching dynamics: when a platform shows provably higher ROI, budgets migrate faster than core CRMs do — so challengers can win share even if the incumbent “stays installed”.

This is not “proof” by itself — but it’s exactly the type of footprint you expect when:

• legacy stacks slow down

• budgets get reallocated toward higher-ROI platforms

• enterprises separate “CRM system of record” from “marketing outcome engine”

Zeta is trying to be that outcome engine.

The data moat is the product

Zeta’s moat is not “AI”. AI will commoditize.

Zeta’s moat is data + identity + signals at scale, and the ability to activate those signals across channels.

Steinberg gave the most important numbers in one quote:

“We have 552 million active people who are opted in our data cloud today with an average of 5,000 to 7,000 data elements per person. Our first-party tracking pixel sits on 1 trillion pages of content and ingest trillions and trillions of proprietary marketing signals…”

Those numbers matter because they describe:

• scale (552M people)

• depth (5k–7k data points per person)

• freshness (signals ingesting continuously)

• distribution surface (pixel on 1T pages)

That is the raw material to train models, build propensities, measure incrementality, and optimize campaigns in near real-time.

Why this matters now more than ever?

Steinberg also made an important observation about the current shift in traffic:

• Google now answers 60%+ of queries on its own platform (down from the old world where “90%+” of queries were sent out to publishers).[1]

The conclusion is clear:

When “free clicks” disappear, marketing becomes harder and more expensive.

And when marketing becomes more expensive, the value of a platform that can:

• find the right audience

• personalize the message

• measure outcomes

• reallocate budget dynamically

…goes up.

The “ROAS compounding” piece: why Athena matters

Steinberg used an analogy I love:

Zeta built an “F-22 fighter jet”, but clients were flying it like a “Cessna”. Athena becomes the copilot that lets customers use more of the machine.

This matters because it points to a second order effect:

• If Athena makes customers more effective, their ROAS goes up

• If ROAS goes up, their budget moves to Zeta

• If budget moves to Zeta, Zeta gets more signals

• If Zeta gets more signals, the models get better

It’s not “AI feature”. It’s distribution + data + ROI loop.

Key risks

• Regulation / privacy: Zeta’s advantage is signals + identity (including its pixel footprint). Any tightening around consent, tracking, data sharing, or cross-site identifiers could reduce signal quality or raise compliance cost.

• Data-source scrutiny: even if data is “opt-in”, the market is increasingly skeptical about where signals come from and how durable the permissioning really is.

• Steinberg reputation / past controversies: the CEO’s history (and periodic short-seller narratives) can raise the bar for trust and keep the valuation multiple suppressed even when fundamentals are strong.

My take

The Zeta thesis is not “marketing grows”.

It’s: marketing becomes the profit engine in a commoditized world.

And in that world, the company that can prove higher ROAS will win share, even against the biggest incumbents.

Zeta is explicitly telling you this is already happening — and the growth delta (30% vs ~10% ecosystem) plus the early cracks in legacy segments support it.

Potential catalysts from here:

• Athena going GA (announced March 24, 2026) could increase adoption inside existing accounts and drive faster wallet-share expansion.

• Next earnings is imminent (Q1 2026 on April 30, 2026). Any beat/raise + continued strong NRR can change the narrative quickly.

• Dilution is coming down: dilution used to be much higher (~15% in FY2024), then management guided 4%–6% for FY2025 and is targeting 3%–4% net dilution in FY2026.

  • In the latest disclosures, management said they ended 2025 at the low end of their range with total net dilution of 4.3% (or 2.2% excluding Marigold) and improved stock-based comp from 19% of revenue in 2024 to 14% in 2025.

• Valuation reset: the stock still trades like a “question mark”, not like a compounding 30% grower — which is exactly why the upside (and the risk) can be asymmetric.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up—breaking them down to their basic parts and piecing together a real thesis on what drives them. Think of it as a map of a company’s soul. It’s a tight set of core indicators—tailored to each business—that show if it’s heading the right way, no matter what the stock price says. These aren’t your basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They’re deeper, sharper, and linked straight to the thesis I’ve cooked up on how the company makes value and fights in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks if the company’s worth buying into or hanging onto.

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own calls on how much to put in (allocation).

Now, let’s dive into it.

Read more

HIMS business is going through two temporary headwinds, and both get better as 2026 progresses:

1. The book cleaning: the company cleaning up lower-quality / on-demand behavior

2. GLP-1 uncertainty (regulatory/legal noise around compounding)

On the GLP-1 side, Andrew Dudum (co-founder & CEO) was explicit that compounded GLP-1s are not the company:

the amount of patients that actually are on the compounded GLP-1s is actually quite a small minority of the aggregate subscriber base.

And in the prepared remarks, the company framed GLP-1s as a case study of a bigger shift:

Healthcare must evolve towards that same consumer-oriented distribution model.

So my bet is simple:

As GLP-1 distribution normalizes (especially with big pharma partnerships) and the book cleaning slows, the underlying platform growth shows up again.

One more thing: your highest-quality users are the ones on a “monthly membership” behavior and when multi 30-day subscribers hit an all-time high (~96%), that’s a huge signal.

It means the base is increasingly made of loyal subscribers (not one-off buyers).

Table of Contents

• Q4 2025 Update

• L2C take aways and performance

• Business Ontology Framework by L2C

  • Business Ontology

  • 4D Valuation Model

• L2C portfolio strategy

Q4 2025 Update

Scale + distribution: this is the real moat

This is the quote that matters most (from the earnings call).

It explains the “compounding” effect of scale:

To give you an idea of how our speed to market has changed, historically, we targeted launching one new specialty every year… Longer tenured offerings like sexual health and hair loss… took three and sometimes even four years to eclipse $100 million in annual revenue… our weight loss offering reached $100 million revenue run rate in less than seven months after launch…

Translation in plain English:

Once you have distribution, new specialties are not “new businesses”.

They are new entry points into the same machine.

It’s like Amazon.

First: books.

Then: everything.

Same logistics.

Same flywheel.

Labs is a Trojan horse

Labs matters because it creates:

• a new entry point

• higher engagement

• better personalization

• a reason to stay for years

Andrew Dudum (co-founder & CEO) described the direction very clearly:

as we’re able to get testing at home for cheaper, cheaper, cheaper cost… you as a member of Hims & Hers can be getting access to this type of data collection on a really frequent basis.

And Labs is already priced like a low-friction membership add-on, not like a luxury service.

In their Labs launch release, Hims offered:

• Base plan: $199/year (50 biomarkers)

• Advanced plan: $499/year (120+ biomarkers)

That’s absurdly cheap compared to what healthcare normally charges.

And it’s exactly why I call it a Trojan horse. It’s a no brainer.

If you acquire a customer with a $199/year bloodwork plan, you can later monetize with higher-value treatments where clinically appropriate.

Plus, in the prepared remarks, the company highlighted cross-sell potential:

we’ve identified that over 70% of lab customers may be eligible for treatment plans offered through the platform

Once you have the data, you have everything.

International: they’re telling you they will run near breakeven (on purpose)

Another important line from the call:

Across the majority of international markets, we expect to take a growth-oriented approach… even if that means running certain markets at or near breakeven on an adjusted EBITDA margin basis.

This is what good capital allocators say.

They are basically telling you:

“We will buy market share first, then optimize margins later.”

My take: the bottom is in

My thesis is simple:

• GLP-1s demand is likely back

• Book cleaning is easing

• Hims is building the consumer health distribution layer

• Labs increases engagement and personalization

• GLP-1s are a catalyst, not the business

• International is a long runway

The market tends to overreact to bad news, such as Novo Nordisk’s lawsuit.

But the platform keeps compounding.

This is why I’m calling the bottom, in terms of users and revenue growth.

Peptides: the business case

The key idea: peptides are likely to be another category expansion moment.

They fit perfectly with what Hims sells:

• recurring relationships

• ongoing clinical oversight

• personalization

• distribution at scale

Andrew Dudum (co-founder & CEO) literally put peptides in the roadmap:

…work on innovation and R&D for future categories like peptides which we’re working on right now.

On the regulatory side, Reuters reported the FDA was expected to lift restrictions on certain peptides used by compounding pharmacies (this is about compounding policy, not full FDA approvals for new drugs).

Example: BPC-157 (how it’s used, how recurring it could be, and a realistic serviceable market)

BPC-157 is probably the most famous “wellness peptide” right now.

What it is (in plain English):

• OPSS (a U.S. Department of Defense program) describes BPC-157 as a lab-made synthetic peptide, often promoted to speed healing of wounds and injuries (skin, tendon, muscle, ligament, bone), and also marketed for gut/joint health and inflammation.

• The FDA has listed BPC-157 among bulk substances that may present significant safety risks for compounding (Category 2 for 503A), citing limited safety information for proposed routes of administration and potential risks related to immunogenicity/impurities.

• A systematic review (orthopaedic sports medicine) notes BPC-157 is not FDA-approved, but is still being used; the review found mostly animal evidence and very limited human evidence (one small retrospective human dataset was mentioned).

How it tends to be used today (the “workflow”):

1. Trigger: injury, pain, or “recovery optimization” (tendon/ligament issues, joint pain, post-surgery recovery, etc.)

2. Channel: cash-pay clinics / concierge medicine / compounding + wellness

3. Format: commonly discussed as injectable (subcutaneous / sometimes localized injections), and sometimes oral for “gut health” claims

4. Pattern: typically sold as a cycle (weeks), not as a forever drug

This is not a GLP-1-type subscription where someone stays on it for years.

It’s closer to:

• a repeatable “course” product (like antibiotics, physio, or an MRI)

• or a seasonal / episodic product (you come back when you’re injured again)

How recurring could it be:

There are probably 3 recurring paths Hims could realistically build around a peptide like BPC-157:

A) Injury recurrence = repeat cycles

If someone gets 1–2 injuries per year (or has chronic tendon issues), they may do 1–2 cycles per year.

That’s recurring, but episodic.

B) Subscription wrapper (the Hims way)

Even if the peptide is episodic, Hims could monetize a recurring relationship via:

• ongoing “injury prevention + recovery” membership

• access to providers

• labs, biomarker tracking, or imaging referrals

• adjunct products (sleep, pain, inflammation, PT guidance, supplements)

C) Bundled category expansion

BPC-157 is not the endgame.

The endgame is: “Hims becomes the consumer distribution layer for performance + recovery + longevity.”

BPC-157 is just one SKU in that shelf.

Potential market for Hims right now (based on their subscriber base)

Instead of guessing a top-down TAM, here’s the clean way to think about BPC-157 as a product inside the current Hims subscriber base.

Known input:

• Subscribers at end of 2025: 2.5M+

Define the variables:

• N = subscribers (use 2.5M)

• a = adoption rate (share of subscribers who do at least one BPC-157 cycle)

• f = cycles per year per adopting subscriber

  • if “every x months”, then f = 12 / x

• P = price per cycle (cash-pay; depends on dosing + provider visit + fulfillment)

Then the annual revenue opportunity (gross) is:

Annual Revenue = N × a × f × P

Now plug in scenarios (these are assumptions, not facts):

Scenario A (conservative)

• a = 1% of subscribers

• x = 12 months → f = 1

• P = $250 per cycle

Revenue = 2,500,000 × 0.01 × 1 × $250 = $6.25M/year

Scenario B (base case)

• a = 3% of subscribers

• x = 6 months → f = 2

• P = $300 per cycle

Revenue = 2,500,000 × 0.03 × 2 × $300 = $45M/year

Scenario C (aggressive)

• a = 5% of subscribers

• x = 4 months → f = 3

• P = $400 per cycle

Revenue = 2,500,000 × 0.05 × 3 × $400 = $150M/year

Even at modest penetration, this can become a meaningful incremental line item, but it’s not a GLP-1-sized engine.

And the real strategic value is still the same:

If a peptide brings people into the platform, Hims monetizes the relationship over time (labs, follow-ups, other categories), not only the peptide itself.

Why this matters for Hims:

If Hims can make any peptide category:

• safer

• more compliant

• more standardized

• and more subscription-wrapped

…they can turn a messy, episodic, cash-pay trend into a real platform business.

And importantly: BPC-157 is only one example.

Over the next few years, there are likely to be tens of peptides that get allowed / re-allowed in some form.

So the real opportunity is not “BPC-157 revenue”.

It’s Hims building the distribution + care layer that can keep adding new peptide SKUs as the menu expands.

L2C take aways and performance

The Hims thesis is more alive than ever, and the headwinds are slowly dissipating. Starting next quarter, I expect user growth to resume, with revenue following soon after.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the February 8th 2025, when the stock price was $42.54.

-55% DDTD

From here on, the content is restricted to L2C Premium Members, folks who’ve chosen to unlock this toolkit and support my independent research:

• Business Ontology Framework by L2C

  • Business Ontology: My core blueprint for modeling and tracking company performance at every level.

  • 4D Valuation Model: the valuation tool I use to value all my investments (Fair price is useless)

• L2C Portfolio Strategy: My portfolio allocation and strategy in details

• L2C Portfolio access & trades alerts: Real-time views into my holdings, plus instant notifications on buys, sells, and shifts.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up—breaking them down to their basic parts and piecing together a real thesis on what drives them. Think of it as a map of a company’s soul. It’s a tight set of core indicators—tailored to each business—that show if it’s heading the right way, no matter what the stock price says. These aren’t your basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They’re deeper, sharper, and linked straight to the thesis I’ve cooked up on how the company makes value and fights in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks if the company’s worth buying into or hanging onto.

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own calls on how much to put in (allocation).

Now, let’s dive into Hims and this quarter’s update.

Read more

Duolingo just closed a strong FY 2025: revenue grew 39% YoY to $1B+, but user growth is clearly slowing, which will likely slow revenue growth next. QoQ MAU is negative, and the average over the last three quarters is in the low single digits, which suggests DAU will follow, and revenue right after. This is exactly what management guided: 10–12% bookings growth for 2026.

This was mostly expected, at least to me. The more interesting news is that Luis von Ahn, CEO and co-founder, announced a strategic fork in the road, aimed to reach 100M DAU in 2028.

He basically said:

AI is reshaping learning, so in 2026 we will deliberately prioritize user growth and teaching better, even if that moderates near-term financial growth.

But I want to go one step further.

My thesis: the next obvious move for Duolingo is to train a great open LLM using their unique data, and turn it into the best teacher in the world.

Not a chatbot. A teacher.

And I think the timing is perfect: the next 12 months are when open models will likely become “smart enough” to deliver consistent teaching quality at low cost. Let’s get into it.

Table of Contents

• Q4 2025 Update

• L2C take aways and performance

• Business Ontology Framework by L2C

  • Business Ontology

  • 4D Valuation Model

• L2C portfolio strategy

Q4 2025 Update

Duolingo is quietly building the most valuable dataset in education

The reason Duolingo is special is not “language learning”.

It’s this:

• They have a massive funnel (free product).

• They have daily repetition behavior (habit).

• They have outcomes that can be measured (did you answer correctly? how fast? how many retries? do you remember it next week?).

That is the dream dataset for training a teaching model.

Think of it like self-driving:

• The car company that collects the most real-world driving data wins.

Education is similar:

• The platform that collects the most real-world learning data wins.

The real moat is not the content library.

It’s the feedback loop.

And Duolingo’s feedback loop is insane.

They already use AI… but the current approach is limited

Duolingo has used ML for years (e.g. personalization models like Birdbrain, and AI-assisted content generation).

More recently, they productized GenAI in Duolingo Max: Roleplay and Video Call with Lily, powered by modern LLMs.

And if you read how Video Call works, you realize they’re already doing “teacher engineering”:

• They use the transcript to extract a “list of facts” about the user

• Then they feed it back as system context to make future sessions more personal

• They carefully prompt the model to ask the right question at the right level

This is not a toy.

It’s the early version of a learning agent.

But today there’s a problem:

• The best teaching experience is gated behind expensive closed models (or expensive inference).

• That makes it hard to scale “teacher quality” to hundreds of millions of people.

So you get a dilemma:

• Either keep AI features premium-only (good for ARPU, bad for data + adoption)

• Or democratize them (good for growth, expensive, margin pressure)

Management is basically choosing the second path in 2026: grow the user base and improve teaching quality, even if it hurts near-term numbers.

That choice makes perfect sense if they’re playing a longer game.

The longer game: train an open teacher model

Here’s the hot take.

Duolingo should fine tune and optimize an open model for one thing:

Teaching.

Not general chat.

Teaching.

Why open?

Because the economics of education are brutal.

If you want to serve 100M+ daily users with interactive speaking, explanations, coaching, and personalization, you need:

• Low inference cost

• High controllability

• Strong safety / guardrails

• Ability to run custom fine-tunes + retrieval

• A model you can iterate on every week

Owning your own model is the only way to make that sustainable.

And “open” is the fastest path to reach that point, because:

• Open models are improving at an insane pace

• The ecosystem is global (research + tooling + fine-tuning stacks)

• You can start from a very strong base model and then specialize

The missing ingredient is high-quality domain data.

Duolingo has it.

What data do they have that others don’t?

Most LLMs learn language from the internet.

Duolingo has something different:

• Paired learning data: a prompt, an answer, and whether it was correct

• Difficulty labels (implicitly via user performance)

• “confusion patterns” (what people get wrong, systematically)

• longitudinal memory: did you retain it after 7 days / 30 days?

• speaking practice data (especially as Video Call expands)

This is a goldmine.

If you train a model with that feedback loop, you don’t just get a model that “speaks Spanish”.

You get a model that knows how to teach Spanish.

That’s a completely different product.

The KPI nobody tracks: words spoken per user

Management actually called this out on the Q4 2025 earnings call. Luis said that if you look at the “graph of words spoken per user on Video Call”, it’s been “a monotonically increasing graph over time.”

That’s not a random metric.

And the analogy is straight from Elon.

Tesla didn’t win autonomy by optimizing for “time spent in the car”. They obsessed over miles driven, because miles driven = real-world training data. More miles means faster learning loops, edge cases, and better models.

Duolingo’s version of “miles driven” is words spoken.

Because the endgame is not “minutes spent”.

It’s “practice delivered”.

If Duolingo becomes the best teacher in the world, the biggest unlock is speaking.

Speaking is where:

• learners freeze

• tutors are expensive

• scheduling is annoying

• and progress is slow

An AI teacher solves this by making speaking practice:

• instant

• infinite

• personalized

• cheap

So I expect the Duolingo product roadmap to push more and more toward:

• conversation

• pronunciation

• adaptive feedback

• “live” tutoring

Video Call with Lily is the prototype.

The next step is to make that prototype scale to everyone.

Why I think this happens in the next 12 months

Two reasons:

1. Open models are getting good enough

The last 24 months were about “wow, this can talk.”

The next 12 months are about “wow, this can reliably follow instructions, be consistent, and do narrow jobs extremely well.”

That’s when a specialized teaching model becomes viable.

2. Duolingo is explicitly shifting to a growth + teaching year

They are telling you: we are choosing the long-term user base over near-term monetization.

This is exactly what you do when you’re about to unlock a new growth engine.

What does Duolingo become if this works?

If you believe this path, Duolingo stops being “a language app”.

It becomes:

• the default interface between humans and learning

Like Google is the interface between humans and information.

Or like Spotify is the interface between humans and music.

The big idea is simple:

• if you own the best teacher, you own the learning time

• and learning time expands with AI (because the cost goes down)

This is why I think management’s 100M DAU ambition is not crazy — it’s a stepping stone.

L2C take aways and performance

Q4 numbers were weak — but the story is bigger than a quarter.

Duolingo is positioning itself for the next era of learning, where:

• teaching quality is AI

• practice is unlimited

• cost is low

• and the best teacher wins

The obvious next step is to stop renting intelligence… and start owning it.

Train the teacher model.

Make it the best in the world.

If they execute, Duolingo won’t just be an edtech company.

It will be an AI company whose product is human progress.

As always, here is the “Deep Dive To Date” (DDTD), that is how the DUOL stock is performing since my initial deep dive on the January 11th 2025, when the price was $318.15.

-70% DDTD

From here on, the content is restricted to L2C Premium Members, folks who’ve chosen to unlock this toolkit and support my independent research:

• Business Ontology Framework by L2C

  • Business Ontology: My core blueprint for modeling and tracking company performance at every level.

  • 4D Valuation Model: the valuation tool I use to value all my investments (Fair price is useless)

• L2C Portfolio Strategy: My portfolio allocation and strategy in details

• L2C Portfolio access & trades alerts: Real-time views into my holdings, plus instant notifications on buys, sells, and shifts.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up—breaking them down to their basic parts and piecing together a real thesis on what drives them. Think of it as a map of a company’s soul. It’s a tight set of core indicators—tailored to each business—that show if it’s heading the right way, no matter what the stock price says. These aren’t your basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They’re deeper, sharper, and linked straight to the thesis I’ve cooked up on how the company makes value and fights in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks if the company’s worth buying into or hanging onto.

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own calls on how much to put in (allocation).

Now, let’s dive into Duolingo and this quarter’s update.

Read more

Most people still think Rocket Lab = “that small satellite launch company”.

That’s the mistake.

My core thesis on Rocket Lab hasn’t changed, but only reinforced: RKLB is building an end-to-end space company, and the total addressable market in front of them is not “launch”. It’s the full space value chain, and it keeps expanding as the world gets more tense and more digital.

In 2026 and beyond, two forces dominate everything:

1. AI

2. Space

And they are starting to reinforce each other. If you want a simple mental model: AI is the brain, space is the high ground. You don’t want your brain to depend on someone else’s high ground.

That’s why I say Rocket Lab is going after an “infinite TAM”. Because Space is infinite, if compared to earth.

Table of Contents

• Q4 2025 Update

• L2C take aways and performance

• Business Ontology Framework by L2C

  • Business Ontology

  • 4D Valuation Model

• L2C portfolio strategy

Q4 2025 Update

The new arms race: not tanks, but tests and cadence

When geopolitics heats up, governments don’t buy ideas. They buy capability.

And capability is not a PowerPoint. It’s:

• frequency (how often can you deliver)

• reliability (does it work every time)

• speed (can you do it now, not in 5 years)

Management has been very explicit on this point:

Faster and more frequent hypersonic testing is an urgent need and a national priority.

And they also framed the moat in plain English:

Rocket Lab is the only credible provider that has demonstrated the ability to deliver this capability right now, not years into the future… This kind of cadence and reliability positions us well for programs like Golden Dome.

Right after came the market proof: this week Rocket Lab signed a $190M contract for a block buy of 20 HASTE hypersonic test flights over the next four years — the single largest launch agreement in the company’s history.

In defense, cadence becomes a moat.

It’s like Amazon Prime in e-commerce. Everyone can build a website. The killer feature is the logistics machine that delivers fast, consistently, at scale.

In space/defense, Rocket Lab is trying to become that logistics machine.

Rocket Lab goes “prime”: the $816M proof point

One of the most important signals in the latest update is the Space Development Agency award.

SDA awarded Rocket Lab an $816 million contract to build an advanced constellation of 18 spacecraft, the largest single contract in the company’s history.

Why does “prime” matter?

Because being a prime means you’re not just a supplier. You’re the one accountable. You become part of the inner circle of who the government trusts to deliver critical systems.

Historically, that club was basically legacy aerospace. Slow, expensive, protected.

Now we’re watching the “defense prime” world changing, similar to how software ate the world:

• Anduril in defense

• Palantir in data

• and Rocket Lab trying to do the same in space manufacturing + launch + payloads

And this is where vertical integration stops being a buzzword. They explicitly said:

It’s important to point out that the acquisition of Geost played a significant role in securing this award.

Vertical integration: the flywheel nobody can copy overnight

Rocket Lab’s strategy is simple and brutal:

• build rockets

• build spacecraft

• build payloads

• build the manufacturing engine to ship all of it

Most competitors are single-product companies.

Rocket Lab is building a system.

And systems compound.

A good analogy is Apple:

• Hardware + software + supply chain

• not because Apple likes complexity, but because control creates margin, speed, and quality

In defense/space, control is even more important because timelines and reliability are everything.

And there’s a “hidden” upside that many investors ignore: once you’re in the flow of these programs, you can capture value not only as a prime, but also as a merchant supplier. Management pointed at it: as a merchant supplier into other primes, there are “additional subsystem opportunities… that could add a total capture value to approximately $1 billion” across payloads, solar power, reaction wheels, star trackers, software, and more.

“Datacenters in space” sounds crazy, but if it happens...

Now the spicy part: space-based data centers.

At first glance it sounds like science fiction.

But the logic chain is not stupid:

• AI demand explodes

• data centers on Earth face limits (power, cooling, regulation, land, grid constraints)

• in orbit you have cold environment + potentially massive solar power

Will this happen fast? I don’t know. Probably it will take 5 to 10 years.

But I like how Rocket Lab is positioning itself for that world without betting the company on a single moonshot.

For example, the solar piece is not a detail — it’s the bottleneck.

If you want to put serious compute in orbit, you need industrial power generation. And management basically said the same thing: if space-based data centers become real, “rapid market growth… will be hampered if traditional solar cells are the only option.”

That’s why I find Rocket Lab’s solar push so interesting. They are not just selling panels, they are trying to unlock the cost curve.

They introduced a space-optimized silicon solar array: silicon historically had “low radiation tolerance and very low life expectancy” in space, so it was not considered viable. Their claim is they solved that and can now deliver “a really low cost per watt at industrial scale,” enabling “gigawatt class power generation” and “kilometer size scale” systems.

And the hybrid approach (high-efficiency cells + silicon) is smart engineering: use premium cells when power density matters, use silicon when cost and scale matter, and mix them when you need to optimize the trade-off.

If “compute in orbit” ever becomes a real market, whoever controls power + thermal + manufacturing at scale wins. Solar is step one.

L2C take aways and performance

Rocket Lab is a manufacturing + execution + national security story, with optionality on the next wave (proliferated constellations, payload verticalization, maybe even compute-in-orbit).

Because the real product is not the rocket.

The real product is: delivery of critical capability, at scale, repeatedly.

And that’s an infinite TAM world.

Before the conclusion, it’s important to mention that Neutron’s first launch has been delayed to Q4 2026 due to an issue with its fuel tank. Compared to the original guidance from early 2025, Neutron is now running roughly one year late.

This can be read in two ways.

• The bad news: delays push out revenue, slow down capability development, and give customers one more reason to stick with the incumbent.

• The bullish interpretation: if Rocket Lab — the company that built, owns, and operates one of the most reliable small launch vehicles in the world — needs this much time to industrialize a bigger rocket, it’s reasonable to expect competitors will need even longer.

In other words: the difficulty of scaling up launch is exactly why the medium-lift market is likely to stay a duopoly (SpaceX + Rocket Lab) for long enough to let Rocket Lab compound into a much bigger company.

If you ask me what’s the best way to value Rocket Lab right now, I think it’s to monitor a simple ratio: Price-to-Backlog.

Backlog is not revenue, but in defense/space it’s the closest thing you get to forward visibility. And RKLB is increasingly behaving like a prime contractor, where the pipeline of awards matters as much as quarterly numbers.

Today, with backlog now above $2B, the Price-to-Backlog ratio is roughly ~20.

Here is how the backlog trend looked like in Q4 (You need to add the $190M of HASTE contract to get a more updated number).

My bet is that as Rocket Lab consolidates as a prime supplier, it will keep landing bigger and bigger contracts, and the backlog will stay full (or even expand).

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive. For RocketLab stock ($RKLB), the price on September 17th, 2024, when I published my analysis, was $7.19. As of this update, the price stands at $67, reflecting a

+9,3x DDTD

From here on, the content is restricted to L2C Premium Members, folks who’ve chosen to unlock this toolkit and support my independent research:

• Business Ontology Framework by L2C

  • Business Ontology: My core blueprint for modeling and tracking company performance at every level.

  • 4D Valuation Model: the valuation tool I use to value all my investments (Fair price is useless)

• L2C Portfolio Strategy: My portfolio allocation and strategy in details

• L2C Portfolio access & trades alerts: Real-time views into my holdings, plus instant notifications on buys, sells, and shifts.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up—breaking them down to their basic parts and piecing together a real thesis on what drives them. Think of it as a map of a company’s soul. It’s a tight set of core indicators—tailored to each business—that show if it’s heading the right way, no matter what the stock price says. These aren’t your basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They’re deeper, sharper, and linked straight to the thesis I’ve cooked up on how the company makes value and fights in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks if the company’s worth buying into or hanging onto.

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own calls on how much to put in (allocation).

Now, let’s dive into Rocket Lab and this quarter’s update.

Business Ontology

Here’s a quick visual on the Business Ontology for Rocket Lab.

Read more

$MELI is, in my opinion, one of the most misunderstood large caps in the market right now.

It’s growing revenue ~40% YoY, it’s scaling logistics + fintech in a way no one else in LatAm can replicate, and yet it’s priced like a business that is about to die.

That mismatch is my thesis.

Table of Contents

• Q4 2025 Update

• Lorenzo2cents (L2C) take aways and performance

• Business Ontology Framework by L2C

  • Business Ontology

  • 4D Valuation Model

• L2C portfolio strategy

Q4 2025 Update

I get why Mr. Market is nervous:

• LatAm macro always feels “fragile” (FX, inflation, politics)

• Competition is real (especially in Brazil)

• When MELI invests aggressively, margins look worse in the short term

But here’s the key: these worries don’t explain the price.

If you price a company like it’s ex-growth, you better have evidence that the machine is breaking.

So let’s look at the machine.

Brazil: “go beast mode” worked

A few months ago MELI turned aggressive in Brazil. More subsidies, lower free shipping threshold, better value proposition.

This is the type of move that scares Wall Street, because it looks like “profitability sacrifice”.

But it’s not charity. It’s a land grab.

And it’s working.

Here’s how management framed it: the acceleration

is the result of our strategic investments to enhance the value proposition, most notably the decision to lower the free shipping threshold.

More free shipping

is driving higher purchase frequency and bringing new buyers into the ecosystem.

Then the key line:

This volume is translating directly into efficiency. Our logistics network absorbed the increase in volumes while driving productivity gains, proving our ability to scale effectively.

In numbers, that means:

• GMV in Brazil +35% YoY

• Sold items +45%

• Logistics absorbed the volume while improving productivity

This is important: they didn’t just buy growth with dumb spending. They scaled.

When you can scale logistics efficiently, you build a moat.

Argentina: same playbook, bigger check

Now they’re doing something similar in Argentina.

On March 11, 2026, CEO Ariel Szarfsztejn announced a plan to invest $3.4B in Argentina in 2026 (roughly +30% vs 2025).

Where is the money going?

• Logistics network (distribution centers)

• E-commerce platform + tech

• Fintech ecosystem (Mercado Pago)

Plus ~1,900–2,000 new hires.

Again: short term pain, long term dominance.

Think about it like this.

If you want to build the “Amazon + PayPal” of an entire continent, you don’t optimize for quarterly margins. You optimize for being unavoidable.

The profitability fear is overplayed

A lot of the bear case is basically: “competition will force higher marketing spend, and MELI will lose unit economics.”

But when you actually do the math, the opposite shows up.

If you approximate CAC payback as marketing spend divided by incremental gross profit (a rough but useful sanity check), it improved from ~13.2 months (2024) to ~11.8 months (2025).

Translation: MELI’s customer acquisition machine became more efficient, not less.

In a tougher environment.

That’s not what a company under structural pressure looks like.

The proof that matters: FCF/share is compounding

At the end of the day, I don’t care about “narratives”. I care about whether the business is turning into cash for shareholders.

If MELI’s investments were reckless, you would see it in the per-share economics.

Instead, what you see (zoom out) is a pretty clean story: FCF/share keeps going up.

This is the single best rebuttal to the “they’re buying growth at any cost” argument.

The best time to buy is when price goes down while value goes up

$MELI is down ~17% YTD.

But the business is not deteriorating.

It’s strengthening.

This is a classic pattern:

• management invests for long-term growth

• margins compress a bit

• Wall Street panics

• stock sells off

• long-term investors get a gift

I love when this happens, because it’s one of the few moments where you can buy quality at a discount.

“We’re not trying to optimize short-term margin” (and that’s exactly the point)

The CFO said it clearly during the earnings call:

• their focus is capturing commerce, fintech, ads opportunities

• they will invest even if it pressures margin short-term

• they manage for the long term

If you’re an investor, that should not scare you.

It should make you ask a better question:

“What do they become if this strategy works?”

The next big bet: advertising

The next leg of the story is ads.

MELI sits on something extremely valuable: first-party data on real commerce behavior.

Not “likes” or “views”.

Actual intent.

If you believe in a world of agentic commerce (AI agents shopping for you), this becomes even more interesting.

Because:

• shopping moves faster from offline to online

• discovery becomes algorithmic

• whoever owns the transaction graph owns the monetization

MELI is building its own agentic experience inside the platform (search, recommendations, discovery).

But there’s a second angle: off-platform advertising services, attribution, tech stack for third parties.

If commerce runs at low single-digit margins to win the market, fintech + ads can print money.

That’s the shape of the long-term MELI.

Lorenzo2cents (L2C) take aways and performance

The stock is priced like a problem.

The business looks like a compounding machine.

That’s why I think the best time to buy is when the market is focused on short-term margin noise, while MELI is building long-term dominance.

DYODD. I’m long-term bullish on $MELI.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the May 18th 2025, when the stock price was $2585. As proved by my post on X, where I share real time updates, I opened a position a few days later.

- 35% DDTD

From here on, the content is restricted to L2C Premium Members, folks who’ve chosen to unlock this toolkit and support my independent research:

• Business Ontology Framework by L2C

  • Business Ontology: My core blueprint for modeling and tracking company performance at every level.

  • 4D Valuation Model: the valuation tool I use to value all my investments (Fair price is useless)

• L2C Portfolio Strategy: My portfolio allocation and strategy in details

• L2C Portfolio access & trades alerts: Real-time views into my holdings, plus instant notifications on buys, sells, and shifts.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up—breaking them down to their basic parts and piecing together a real thesis on what drives them. Think of it as a map of a company’s soul. It’s a tight set of core indicators—tailored to each business—that show if it’s heading the right way, no matter what the stock price says. These aren’t your basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They’re deeper, sharper, and linked straight to the thesis I’ve cooked up on how the company makes value and fights in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks if the company’s worth buying into or hanging onto.

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own calls on how much to put in (allocation).

Now, let’s dive into MELI and this quarter’s update.

Business Ontology

Here’s a quick visual on the Business Ontology for MELI.

Read more

Oddity fell off a cliff in Q4. On February 25, 2026, they announced record 2025 results—revenue up 25% to $810M with strong profitability—but warned of a sharp disruption in ad costs from an algorithm change at their largest advertising partner (likely Meta). This caused unprofitably high customer acquisition costs, leading to an expected ~30% revenue drop in Q1 2026, no full-year guidance, and the stock plummeting more than 50%.

Permabears couldn’t have asked for a better opportunity, and the narrative that Oddity is a scam came back stronger than ever.

Table of Contents

• Q4 2025 Update

• Lorenzo2cents (L2C) take aways and performance

• Business Ontology Framework by L2C

  • Business Ontology

  • 4D Valuation Model

• L2C portfolio strategy

Q4 2025 Update

At the current share price, the company is a bargain unless you think it’s going out of business.

There are two valid arguments supporting the “out of business” thesis:

1 — Loss of trust in management, which isn’t telling the truth about what happened and is probably hiding something even worse than what they’ve shared

2 — The company can’t return to its previous growth rate and will actually lose market share as revenue continues to fall

My thoughts on point one are simple: I can’t see what could be worse than this. Oran (the CEO) presented the worst possible situation, saying they don’t even know how to fix it yet. What could be worse? He could have sugar-coated it, claiming they have a clear recovery plan and will return to growth quickly. In my view, he was fully transparent.

To the second point: Let’s dig deeper into what the issue is likely about.

The Issue

The issue Oddity faced stemmed from their unique “Try Before You Buy” (TBYB) model on IL Makiage (and SpoiledChild), which lets customers pay only shipping upfront for a trial, then keep/pay or return the product.

This model boosts conversions and reduces buyer risk (mimicking in-store trying), driving strong growth and high repeat rates (~70% of revenue from repeats). However, it has inherently higher return rates compared to standard e-commerce beauty purchases.

The company’s largest advertising partner (widely interpreted as Meta—Facebook/Instagram, based on analyst notes and context) recently updated its algorithms. The new algo began interpreting TBYB-related signals (especially elevated returns) negatively:

• It viewed these as lower-quality or riskier signals (e.g., higher post-purchase dissatisfaction indicators).

• This diverted Oddity’s ad campaigns into lower-quality auctions with abnormally inflated costs (disconnected from market norms).

• Result: Sharp spike in new user acquisition costs (CAC/CPA), making first-order profitability unsustainable at scale.

Oddity described this as an “unprecedented dislocation” and an “edge case” because TBYB is rare in beauty (most competitors avoid it due to logistics/complexity). They first noticed issues in H2 2025 (mentioned in prior calls), but it worsened sharply into early 2026.

They believe it’s fixable without ditching TBYB—through adjustments like infrastructure tweaks, better signal optimization, or platform workarounds—expecting meaningful progress in Q2 2026 and normalization in H2. They didn’t abandon the model, viewing it as a core pro-consumer differentiator.

In short: The algo change penalized their high-return TBYB setup → CAC explosion → scaled-back ad spend → ~30% Q1 2026 revenue drop expected.

Will Oddity Go Out of Business?

The question is: How long will it take the company to return to growth, and will 20%+ growth be restored or lost forever?

Nobody knows, of course, but here’s my speculation. I believe 2026 is a lost year, likely ending with 0% revenue growth or slightly negative YoY. The worst will be over in one month from now. The worst-case scenario is that they’ll need to ditch TBYB advertising, which will surely impact growth and/or profitability (to grow at the same rate, they’ll bear higher CAC).

In 2027, Oddity will resume double-digit growth. It’s unclear whether they’ll return to 20%+.

Lorenzo2cents (L2C) take aways and performance

Oddity is clearly fragile right now, depending on Meta for survival, which isn’t sustainable long-term. I’d argue this event will end up making them stronger, forcing them to diversify ad partners and channels. In the long term, this event could even be considered positive.

What happened helped me better understand how hard it is to build a Direct-to-Consumer business and the level of moat that D2C companies like Oddity, Lemonade, Duolingo and Hims have built by reaching scale and profitability.

Their race is against incumbents, not new entrants, and it’s unlikely any new player can achieve anything similar.

This also highlights how social network companies like Meta act as formidable gatekeepers, effectively collecting a commission on revenue from any company selling to consumers.

My Thesis

If the company doesn’t go out of business—which I don’t think it will—my thesis is unchanged, though it will likely take a bit longer to materialize.

The early signals for the newly launched brand, Methodiq, are encouraging as shared by management, backed up by TrustPilot and App Store reviews—4.6 and 4.2 respectively, with already 101 reviews on the former.

Oran mentioned that they’ve expanded their capabilities in peptides:

We recently expanded our capabilities into peptides to add to our small molecule foundations, and are working on peptide solutions in areas like acne and aging. This expansion into peptides gives us flexibility to identify the right modality to address an individual biological target.

At the same time, they’re working in parallel to improve topical delivery of different actives to ensure they reach the relevant areas in the skin and maximize the biological effect. They expect to have 8 products in market in 2026 made with ODDITY Labs molecules, including molecules that cover key categories like acne, eczema, and hyperpigmentation.

And more to come in the future that we are bullish about.

The Smartest Trade of All Time (NO FINANCIAL ADVICE)

Looking at the web traffic of IlMakiage.com—the website of the brand that accounts for 70% of revenue—we can see a sharp decline starting in December, approximately when the issue became serious. The drop is about 30% from the high in September.

Web traffic is a proxy, and it likely lags the performance of ads by a few days or weeks, as it relates to organic traffic and (I don’t think) accounts for Meta ads direct traffic. Still, it should indirectly give a good indication of whether the situation has normalized. My guess is that web traffic will trend up before management announces normalization, as they’ll take their time to ensure it’s stable.

Since the company is basically trading at a ridiculous share price, any outcome other than bankruptcy will reprice it meaningfully higher. Being able to anticipate management’s announcement—even by one day—means we have a meaningful advantage.

As always, here is the “Deep Dive To Date” (DDTD), that is how the stock is performing since my initial deep dive on the July 16th 2024, when the price was $44.24.

-72%% DDTD

From here on, the content is restricted to L2C Premium Members, folks who’ve chosen to unlock this toolkit and support my independent research:

• Business Ontology Framework by L2C

  • Business Ontology: My core blueprint for modeling and tracking company performance at every level.

  • 4D Valuation Model: the valuation tool I use to value all my investments (Fair price is useless)

• L2C Portfolio Strategy: My portfolio allocation and strategy in details

• L2C Portfolio access & trades alerts: Real-time views into my holdings, plus instant notifications on buys, sells, and shifts.

Business Ontology Framework by L2C

The Business Ontology is a framework I built after tearing apart several tech companies from the ground up—breaking them down to their basic parts and piecing together a real thesis on what drives them. Think of it as a map of a company’s soul. It’s a tight set of core indicators—tailored to each business—that show if it’s heading the right way, no matter what the stock price says. These aren’t your basic stats like P/E ratios or revenue bumps you grab from Yahoo Finance. They’re deeper, sharper, and linked straight to the thesis I’ve cooked up on how the company makes value and fights in its market.

The framework boils down to two big pieces:

1. The Business Ontology—This checks if the company’s worth buying into or hanging onto.

2. The 4D Valuation Model—This gives you a 4D roadmap to guide your own calls on how much to put in (allocation).

Now, let’s dive into Lemonade and this quarter’s update.

Read more