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

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.

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 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 .

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.

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.

Join the Business Ontology Community

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

I read the transcript rather than listening to the call, so I can’t be certain of Daniel Schreiber’s tone. But to me, this opening statement was clearly directed at the perma-bears who have insisted since day one that Lemonade will never be profitable:

Somewhat unusually, insurance is a business that tends to turn cash flow positive before GAAP accounting positive. The one almost inevitably follows the other. This then is as good a spot as I need to reiterate a long-standing expectation that we will be EBITDA profitable in Q4 of this year and EBITDA positive for the full year of 2027.

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

By any measure, Q4 2025 was Lemonade’s strongest quarter ever. It was characterized by growth acceleration, underwriting excellence, and operating leverage—concluding a year in which momentum continued to build across key business drivers, as management explained.

At this point, I have no doubts that the data moat is real and working. This is visible not only in the numbers but also in Daniel’s words, as he described how the flywheel works—as if to say, “Yes, now we’re certain it really works.”

We continue to be highly focused on growth and accelerating growth because it’s a gift that keeps on giving. Faster growth drives better data and further sharpens our segmentation and pricing capabilities. This powers improving underwriting performance and rapid gross profit growth and we can swiftly redeploy gross profit, thus generated into profitable growth investments with compelling unit economics and so the cycle continues. It’s energizing to see the flywheel continue to compound even as we scale. What’s particularly encouraging is that all this progress is broad-based. Pet, car and [ Europe ] are all coming into their owner’s powerful growth drivers, each combining hyper growth with improving underwriting performance.

The best way to illustrate the perfection of these earnings is to share my updated custom Rule of 40, where I sum the IFP growth rate with “1-Net loss ratio”. The higher the better.

Gross profit and its growth lie at the heart of efforts to optimize IFP growth and the loss ratio. The trend shows that Lemonade has navigated this trade-off beautifully. But even more surprisingly, when you’d expect this metric to eventually flatten, Q4 printed an all-time high of 78—surpassing the value from Q3 2022, when the company was a hyper-growth startup with half the IFP.

This is truly surprising and exceeds even the best expectations. While 31% IFP growth was guided, you’d expect the loss ratio to stay flat or potentially worsen. The fact that Lemonade keeps improving both metrics simultaneously means my thesis is playing out perfectly: Lemonade is poised to disrupt a 300-year-old insurance industry.

Innovator’s Dilemma—Squared

Lemonade is leveraging one innovator’s dilemma, and soon they’ll leverage a second.

Regarding the first, here’s Shai speaking about incumbents in response to a question on how Lemonade can stay ahead of competition:

…companies built on the foundation of people, not technology. They treat tech as a cost center, not their core. They rely on third-party vendors that are themselves built on legacy systems which leaves insurers with hundreds of these connected systems, they need to run their business.

It’s very hard for an organization like that to compete with a full stack tech-first company like Lemonade. In fact, in the history of all tech resolutions, you can probably count on the fingers of one hand, there are companies that dominated prior to the tech revolution and still were there in a dominant position after that. It would be naive to expect the incumbents will be in this place forever. Of course, they are already talking about increasing investment in AI, ensuring a case study here and there. But by the time they make meaningful progress, we believe we’ll always be several steps ahead.

For those who’ve worked in big corporations, this is obvious. For everyone else, it will be obvious in hindsight.

It’s not just about the data flywheel that Lemonade built through its counter-positioned D2C approach. It’s also about the people. Lemonade’s employees embrace AI and enable efficiencies that incumbents can only dream of. Incumbents are dinosaurs and will likely go extinct or become irrelevant. This is life—it always happens, and this time is no exception. It starts from the top. You can’t expect a CEO of a 100-year-old insurer to radically change how they operate, even if he knows it’s the right thing to do. It would mean laying people off and radically changing internal politics built over decades that defend the status quo—with the risk of failure. This simply can’t happen at scale for incumbents.

Now, to the second disruption.

The economy is transforming quickly and will be radically different in a few years. People can’t realize it because they think in linear terms, but it’s very clear to the tech-savvy who work with technology daily that we’re reaching an inflection point. We’re only a few weeks into 2026 and already around 20–25 new LLMs or significant variants have launched—not to mention OpenClaw, which democratizes agentic capabilities, allowing anyone to easily build use cases to automate daily tasks.

While this technology will take years to radically change how large enterprises work, new AI-native startups will emerge quickly and eat the incumbents’ lunch.

This will result in a profoundly changed economy, where more and more “transactions” will be executed by AI agents, not humans.

The easiest way to visualize this is through autonomous cars, which are already here. Autonomous cars don’t just drive themselves—they’ll increasingly maintain themselves.

They go to recharge and pay for it themselves.

They drive on the highway and pay the tolls themselves.

Project this trend onto robots in general, and it becomes even clearer. Think about humanoid robots, which we’ll likely have at scale in 3 to 5 years. They’ll do tasks on our behalf, which means they’ll take risks. When doing laundry, they may damage our clothes. When walking on the street, they may hurt people or damage objects. Every autonomous object will carry potential liability that needs to be mitigated.

Now, how do you think this will work? Do you think a robot’s owner will visit an insurance agency to ask for a quote to cover the robot for certain activities?

When you stop laughing, you can keep reading!

Autonomous objects will get insurance autonomously, based on the activity they’re about to perform, in seconds.

Which company is poised to capture most of this trillion-dollar business?

You name it.

Lemonade has already proven its willingness to be first by taking the risk of insuring Tesla cars driven by FSD.

If the car becomes better and safer with software updates or hardware upgrades our pricing will automatically respond and continue to drop.

As of this moment, autonomously driven miles using Tesla’s [ FSD ], priced at about 50% of the equivalent human-driven miles. So we expect this to get better over time. We believe this represents a fundamental shift for the industry. As autonomous driving becomes safer and more widely adopted, prices should fall transparently and dynamically.

Shai Wininger said something else during the earnings call, and I believe he chose his words carefully.

As physical objects such as vehicles increasingly shift from being controlled by humans to being operated by AI, insurance needs to evolve as well.

Autonomous cars are just the beginning.

Lorenzo2cents (L2C) take aways and performance

My thesis on Lemonade—growth with near-zero marginal costs—is fully unfolding. As of now, there’s 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 & 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/autonomous objects market in 10 years, it will likely be a $1 trillion company.

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

3.3x 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 Lemonade and this quarter’s update.

Business Ontology

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

Read more

The most important sentence in the whole Nebius earnings call was this:

Demand from enterprises and AI native customers continues to outpace supply, allowing us to sell future capacity well in advance.

This is what you want to hear from any AI infrastructure company. We heard the same kind of language from Microsoft, Google, and Amazon. But when a neocloud says it, it matters even more, because it means the market is not “nice to have”, it is “I need it yesterday”.

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

Nebius reiterated a very bold target: an annualized revenue run rate of $7B to $9B by the end of 2026. And management sounded more confident than before.

Over the last few months, our conviction in this range has become stronger. Why? Because we exceeded the high end of our 2025 ARR guidance and showed more than $1.2 billion ARR, because we already contracted more than 2 gigawatts of capacity and are on track to exceed 3 gigawatts this year, because we have already delivered all of our capacity for the Meta contract, because we are on track to deliver the capacity for Microsoft through the course of 2026, exactly as planned. And lastly, the demand for our AI cloud continues to be strong.

So yes, demand is strong.

But the real story, in my opinion, is why demand stays strong and why Nebius might become a “go-to hyperscaler” for the next generation of AI-native companies.

And here comes my main narrative: Chinese open-source models are a big, underrated tailwind for Nebius.

The “token factory” thesis (and why open source makes it stronger)

I think too many people still look at AI compute as a generic commodity. “GPUs are GPUs.” That is the shallow view.

The deeper view is: the winners will be the companies that can turn GPUs into the cheapest, most reliable, most scalable token factory.

Tokens are the new unit of production:

• Not seats.

• Not licenses.

• Not “cloud instances” as we think about them today.

If the next decade is built on agents, copilots, and AI-native apps, then the world will buy tokens, not software seats.

Now, what changes the economics of tokens?

Models.

And specifically: high-quality open source models that keep getting better.

This is where China matters. Models like Kimi K2.5 and Minimax M2.5 are part of a trend: open source is not “second best” anymore. It is getting dangerously close to the frontier, sometimes it is better for specific tasks, and it is improving fast.

That creates a massive tailwind for companies like Nebius because:

1. Open source reduces pricing power at the model layer

   If anyone can host strong models, then the “model owner” does not capture all the value.

2. Open source shifts value to infrastructure + delivery

   If the model is not locked behind a single API, customers care more about:

   • performance per dollar

   • latency

   • uptime

   • scaling

   • security and enterprise controls

3. Open source increases experimentation

   When costs drop and restrictions drop, more startups build, more teams deploy, and usage grows faster.

And Nebius is positioned exactly where this value migrates: the production layer.

Nebius can say: “Bring your model. We will run it better and cheaper.”

That is token factory thinking.

“Robust performance at the cheapest price” is not a slogan. It is a strategy.

When models become more open, the competitive game becomes brutal:

• the buyer has more options,

• switching costs go down,

• and margins compress for undifferentiated providers.

So the infrastructure provider needs a real edge:

• better software stack,

• better orchestration,

• better reliability,

• and cost discipline.

Nebius is not trying to be “a GPU landlord”.

They are trying to become an AI hyperscaler.

That is a very important difference.

AI-native customers are becoming real enterprises (and that is the pipeline)

The second most important thing I heard in this call was this part:

AI start-ups are quickly evolving to real enterprise scale customers with revenues from their products resonating with real customers. Their demand quickly grows from hundreds of GPUs to tens of thousands. This is already a range the market saw from the biggest customers last year.

And then they give examples:

There’s a lot of customers from the sector, starting with Cuckoo, Cursor, Rodo, Higgsfield, Photoroom, Genesis Molecula, different sectors, and they all have their first traction and they’re becoming -- as we speak, becoming real, real companies. Actually, it’s the future enterprises.

This is the biggest long-term reason I am invested in Nebius.

The next mega-companies will be AI-native. Many of them are being born right now. Their demand curve is not linear. It is explosive.

And if Nebius becomes the “default cloud” for these winners, the compounding is insane.

Nebius is not “compute for hyperscalers”. Nebius wants to be the hyperscaler.

I repeat this because it is the core of my thesis.

Most neocloud companies survive by selling big deals to hyperscalers. They are basically a supplement, a pressure valve, or a short-term capacity partner.

Nebius said something that shows a different mindset:

Based on the traction we are experiencing and our extensive research on our total market opportunity, we are leaning into the verticals we’ve already laid out, health care, life sciences, media and entertainment, physical AI and retail, which give us ample runway to capture share and grow our business. While we are happy to service large strategic customers like hyperscalers, we will remain opportunistic with such large deals as we look to balance the opportunity with the long-term positioning we plan to achieve with our AI cloud

This is exactly what you want.

Hyperscaler deals are not the “oxygen” for Nebius. They are “gold opportunities” if the economics are good and if they help scale faster.

That’s a huge difference versus competitors.

Building the software moat: Tavily acquisition and the “developer platform” direction

If Nebius wants to be a hyperscaler, it cannot only be hardware and data centers.

It needs software. It needs a platform developers love.

This is why the Tavily acquisition is interesting.

Tavily is agentic search company. They connect AI agents to the web. And it very much fits in our strategy to become and to be the platform where all the AI developers from start-ups and enterprises building their AI applications and agents.

Then:

In addition to our organic roadmap, we take a product-led approach to expanding our tech stack and tech talent with M&A. The acquisition of Tavily, announced this week, expands our AI platform with market-leading agentic search capabilities. This gives Nebius customers the critical infrastructure they need to build autonomous agents that can navigate the web, verify facts, and execute complex real-world tasks, while eliminating the need for developers to patch together disparate vendors.

I like this because it is consistent with the token factory idea.

Tokens are not useful alone. Tokens are useful when they become:

• agents that act,

• apps that sell,

• workflows that replace labor.

Tavily is part of the “enablement layer” that makes Nebius sticky.

Enterprise adoption: Aether 3.1 and the roadmap

Nebius is also pushing hard on enterprise readiness:

We aim to make it easier for enterprises to adopt AI at scale, with features that enable easier integration into enterprise environments, deliver security and compliance, and enable cross-functional operations.

They released Aether 3.1 in December 2025:

• more transparency,

• greater controls,

• better enterprise-grade security.

And they claim a robust pipeline for 2026:

• integration with on-prem and multi-cloud,

• stronger security and compliance,

• operations across teams,

• and vertical-specific solutions (healthcare, physical AI, media, retail/e-commerce, financial services).

This matters because open source is not only a startup story.

If open source models like Kimi and Minimax keep improving, enterprises will want:

• flexibility,

• control,

• and deployment options.

Nebius can meet them there.

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 with acquisitions like Tavily.

5. And Chinese 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.

+90% 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 Nebius and this quarter’s update.

Read more

Marketing is not just competitive.

It is becoming negative-sum.

Not because “everyone loses money.”

But because the real scarce resource is data, and data cannot enrich everyone at the same time.

Think about it like oil fields.

If one company signs the contract to extract from the best field, competitors do not get to extract from that same field. The resource still exists, but access is exclusive.

That is the game Zeta is playing.

And the reason I’m spending time on $ZETA is simple:

Zeta is not trying to win by having “better software.”

Zeta is trying to win by locking up the best data relationships.

If they succeed, competitors do not just fall behind.

Competitors get starved.

1) What I mean by “negative-sum”

In a normal market, many players can improve together. You can build a great product, and competitors can build something similar. The pie grows.

In Zeta’s market, the key input is:

• first-party enterprise data

• identity data

• transactional and loyalty signals

• omnichannel behavior

And the key word is limited.

Enterprises do not do “deep partnerships” with ten vendors at the same time. They pick one system, integrate it, feed it, trust it, and then build workflows on top.

So the game becomes:

• Zeta signs a big enterprise.

• Zeta plugs into that company’s first-party data.

• Zeta improves outcomes for that company.

• That same first-party data does not enrich Zeta’s competitors in the same way.

So every enterprise that joins Zeta is not only a customer gained.

It is also a training and tuning opportunity competitors do not get.

That is negative-sum.

Because the “best data pie” does not expand fast enough to give everyone the same quality of learning loops.

2) AI makes this more brutal: data access compounds

In the old world, martech competed on features:

• better UI

• more connectors

• nicer dashboards

• more automation rules

Now software is easier to replicate.

AI changes what matters.

When outcomes depend on models and algorithms, the company with the best data relationships gets:

• better identity resolution

• better predictions

• better personalization

• better measurement

• better ROAS

And that loops back into sales.

Zeta’s CEO framed it directly as ROI:

Every dollar that is spent through the Zeta Marketing Platform returns a 6x return in revenue to our clients… on par with pretty much any walled garden.

If that is even approximately true, it creates a dangerous flywheel.

3) First-party data is the difference between “AI copilots” and real ROI

Here is my blunt take:

Most “AI marketing apps” without a strong proprietary dataset are just copilots.

Nice UI. Nice text generation. Nice automation.

But without deep data, they are generic.

They cannot “know.” They can only “suggest.”

For AI applications in marketing, data is the critical element. Without it, AI is mostly a layer on top of weak signals.

$ZETA stands out because it does not start from “AI features.”

It starts from the foundation: identity + first-party data + contextual signals, at scale.

And then AI becomes useful.

4) Why Zeta’s identity layer matters (and why they brag about match rates)

Zeta tells a story that is not random marketing.

A CMO expects a 70% to 75% match rate.

80% is “gold standard.”

Zeta plugs the company’s first-party data into their graph and says:

• first run: 92%

• second run: 94%

The CMO says “impossible.”

This is not just a flex.

This is the whole business.

Because if you can match more identities, you can:

• target better

• measure better

• retarget better

• personalize better

• waste less spend

It is like fishing.

If you can see 94 fish out of 100 in the lake, you fish differently than someone who sees 70.

In a world where cookies die and signals get restricted, match rate becomes a moat.

5) “Age of answers” vs “age of data”

There is a broader shift happening.

We are moving from:

data-driven → answers-driven

Marketers do not want a dashboard with 300 metrics.

They want:

• “What is working?”

• “What is not working?”

• “What should I do next?”

• “Where do I allocate budget tomorrow?”

In other words: they want actionable answers that produce ROI.

This is not only true in marketing.

It is true for AI in general.

If AI has to “pay for itself,” it cannot just aggregate data.

It must produce answers and decisions that create measurable outcomes.

Zeta is positioned well here, because with the right data foundation, “answers” are not just guesses. They can be grounded in real customer behavior.

6) The platform is not “vibe-codeable” (and this is underestimated)

A lot of people today think: “AI can build software, so moats will disappear.”

True for some categories.

Not true when the core value is a complex, deterministic system + data + routing + context.

Zeta’s platform is hard to replicate with casual vibe-coding because:

• It is built on a deterministic foundation, built in-house over years.

• It requires deep expertise in routing technology through LLMs and MCPs to keep functionality reliable, not just “cool demos.”

• And the real crown jewel is contextual intelligence: memory, personalization, and non-generic agents that feel like extensions of human users, not basic tools.

In short: you can copy UI.

You can copy AI features.

You cannot easily copy a system that combines deterministic infrastructure with contextual intelligence and data access at scale.

7) Switching costs are weakening, so Zeta is attacking migration friction directly

One of the most interesting points from management is about AI agents that help migration from legacy systems.

They basically say:

Zeta’s agents can look at the actual code and things a company built in systems like Salesforce, and recreate them inside Zeta, removing friction.

This matters because it highlights the bigger trend:

pure software is no longer a defensible moat.

Switching barriers get weaker.

So the real moats become:

• data

• ecosystem

• distribution

• outcomes (ROI)

Zeta is playing this correctly. They reduce migration pain, then they try to keep customers with outcomes and data advantages, not with lock-in UI.

8) Acquisitions: M&A as a data weapon (17 acquisitions in 17 years)

Zeta has done 17 acquisitions in 17 years.

That matters because it signals something very specific: this is not a company that occasionally buys growth. This is a company that treats acquisitions as a core operating strategy.

In a negative-sum market where data is scarce, M&A becomes a way to buy exclusive access to signal.

Two examples:

• LiveIntent (2024): hashed email scale and publisher-side engagement signals. This strengthens identity resolution where cookies are weak and attribution is messy.

• Marigold enterprise business (2025): loyalty, transactional, retention, and omnichannel behavioral data. This is the “real economy” layer that makes models smarter because it reflects what people actually buy and repeat.

Put simply: Zeta is building its moat the hard way, one dataset at a time. And if they keep doing what they have done for 17 straight years, the message is clear: they plan to keep consolidating scarce data sources faster than competitors can replicate them.

9) The flywheel (and why the market becomes “winner takes more”)

Zeta’s moat is reinforced by a powerful flywheel:

1. More first-party data and identity signal

2. Better personalization and measurement

3. Better ROI for clients

4. More clients join

5. Even more data and signal

6. Repeat

And here is the negative-sum twist:

Customers that partner deeply with Zeta and contribute data are unlikely to do the same with Zeta competitors.

So as Zeta scales, competitors do not just lose market share.

They lose access to the best learning loops.

That is how leaders get disproportionately larger.

This is why I call it negative-sum.

10) Important nuance: “Zeta aggregates first-party data across its customer base”

This is core to the value proposition.

Zeta connects client first-party data to its identity graph and uses it to drive better outcomes. In practice, the platform gets stronger as it sees more real-world behavior across more clients and channels.

Even if client data is handled with privacy constraints and processing rules, the strategic point remains:

Zeta becomes the system sitting in the middle of high-quality first-party relationships.

And in a world where first-party relationships are the scarce input, that position is extremely valuable.

Conclusion: Zeta is winning a negative-sum game by capturing scarce data relationships

Zeta is interesting because it may be building a moat that looks like:

• identity scale

• proprietary datasets (plus acquisitions)

• exclusive enterprise first-party data relationships

• an answers-driven platform that proves ROI

• and a flywheel that makes the leader stronger while starving competitors

In a market where data is scarce, that is how you win.

Because every enterprise that chooses Zeta is:

• revenue for Zeta,

• a “no” for competitors,

• and one less source of premium learning loops for everyone else.

That is compounding.

That is the bet.

This was my first deep dive into the qualitative side of Zeta. It has sparked real interest, so I’ll be going deeper with a follow-up piece focused on quantitative validation of the thesis. Earnings season sits right in the middle, so expect it in a few weeks.

In the meantime, please challenge anything I’ve said — I read every comment and will address the strongest ones in the next article.

In this fourth and final episode of Invest in AI, Avoid the Bubble, I’m exploring the remaining companies I’m invested in, explaining why—despite not appearing tied to AI—they actually are AI plays. I’ll also comment on the other categories in the AI Industries and why I don’t have any investments in them.

Let’s start with the latter.

AI-Powered Applications

AI-powered applications are smart software programs tailored for specific tasks, like adding AI to your sales dashboard to predict deals (Salesforce’s Einstein is a classic example). The barrier to entry is low—anyone can tweak existing AI with simple tools. The key to survival? Own unique data from your niche or build a user base that keeps coming back, creating a loop of more data and better service. Without that, it’s fun for a bit, but poof—gone when the hype cools.

Distribution and stickiness are among the best competitive advantages in this category. Think of companies that are gatekeepers for specific use cases, like CAD companies. A few tools dominate entire industries, and loyal users who developed their skills on those specific tools rarely switch. If these companies are flexible enough to upgrade their tools with AI-powered features, they win. These companies can be smart plays, but I’m not invested in them because they won’t deliver the returns I expect for the L2C portfolio. They’re mature businesses that are unlikely to grow aggressively.

Another example in this category is Palantir ($PLTR). Palantir is redefining industries by building a new kind of operating system to run your business, based on AI. It’s one of the essential enablers of the AI revolution. I owned the stock since the inception of the L2C portfolio (Jan 2024) and exited it in 2025. The reason isn’t that I no longer believe in Palantir, but simply that its valuation made it less attractive compared to other stocks in the portfolio. So far, I haven’t found another stock in this category that sparked enough conviction.

This is the revenue trend I expect from this category, alongside the AI Capital Expenditure trend, assuming demand—and therefore CapEx—lasts long enough.

As you may have noticed, I expect it to be—qualitatively—quite similar to the AI infrastructure cycle, following the AI CapEx curve on the rise and tapering its growth once the hype cools down.

AI-Model

These are the foundational AI models—like large language models (LLMs)—that power chatbots and code assistants. Thanks to the explosion of open and “open‑weight” models, many of these capabilities can now be downloaded, fine‑tuned, or reused by anyone with enough skill. Even NVIDIA has released high‑quality open models under permissive licenses, so teams can adapt them for their own products without starting from scratch.

Because of this, I think pure “model quality” by itself is becoming less defensible over time. Closed models will increasingly keep their edge where the bar is much higher: specialized domains like coding assistants trained on massive proprietary codebases, or biology models tuned on hard scientific and clinical data.

In practice, sustainable moats cluster around three things working together:

• Hard verticals: you solve a difficult, narrow problem where data, regulation, or expertise make it hard to copy you.

• Distribution: you sit where users already are—inside the main app, operating system, search bar, or workflow they use every day.

• Integration: your model is wired into the real workflow and systems of record, not just a chatbox. It sees the right data, triggers actions, updates dashboards, respects permissions, and closes the loop.

Take a CAD vendor as an example. If they build an AI copilot directly into the design tool that engineers already live in 8 hours a day, trained on years of proprietary design data, that assistant is very hard to displace. A slightly “better” generic model living on a separate website is much less attractive, because it has no access to the same data, no tight integration with the workflow, and no default distribution.

The same logic applies to players like Palantir in enterprise operating systems, or to a future “AI operating system” for factories, hospitals, or logistics. The moat is not just the model. It’s the combination of: hard vertical problem, proprietary data, default distribution, and deep integration.

For most everyday tasks—summarizing emails, answering questions, basic reasoning—the gap between leading open and closed models is already small enough that I don’t expect much of a lasting moat at the “generic chatbot” level. That’s why my view is simple: only pursue models if they are laser‑focused on hard vertical problems and you control the distribution channels where users actually show up. That’s where durable value accumulates.

I expect this category to follow a similar revenue trend alongside AI Capital Expenditure, assuming demand—and CapEx—lasts long enough.

AI-Silicon

Silicon is the physical layer of AI: the chips that make everything else possible, like Nvidia’s GPUs crunching massive amounts of data. This is where the system hits real-world limits. Advanced manufacturing capacity is tight, lead times are long, and every new fab costs tens of billions to build. You can’t “move fast and break things” when it takes years and huge capital just to add more supply.

On its own, though, even the best chip is not enough. The real moat shows up when you pair the hardware with a sticky software layer and tight integration into AI workloads. That’s when customers stop thinking in terms of a “GPU” and start thinking in terms of a platform they can’t live without.

Nvidia is the clearest example. CUDA and the broader Nvidia software stack have become the default environment for AI developers. Most frameworks, tools, and libraries are optimized first for Nvidia, which means teams naturally build and tune their systems around Nvidia GPUs. Moving away is possible in theory, but in practice it means rewriting code, retraining models, and rebuilding a lot of internal tooling. That’s a powerful kind of lock‑in.

The same logic applies at the cloud level. Hyperscalers like AWS, Azure, and others sign huge multi‑year deals to deploy Nvidia systems at scale and expose them as managed services. These agreements channel tens of billions of dollars of AI demand into a small number of tightly integrated hardware‑plus‑software platforms. If your workloads are built on those stacks, with their specific drivers, compilers, and orchestration tools, switching is painful.

So the lesson from AI-Silicon is simple:

• Chips alone are a good business.

• Chips plus a deep software ecosystem and distribution through the big clouds can become a fortress.

Without that software “glue” and distribution, even the most advanced GPU risks becoming just another commodity part in a crowded supply chain.

This is the revenue trend I expect from this category, alongside the AI Capital Expenditure trend.

Like the graphs and my analysis? Check out my “Tools I Trust & Use” page to see what I use—and grab ‘em via my links to support my work!

Tools I Trust & Use

TIKR is my go-to tool for all my stock research—it’s a game-changer for spotting winners before the crowd. For you, my Lorenzo2Cents readers: Use the Coupon Code lorenzo20 for 20% off, live now through February 6. Don’t miss out—grab it.

AI-Power

Finally, power—the electricity fueling it all. AI infrastructure is extremely energy-intensive, and datacenter operators are already competing for grid capacity and new generation facilities. At first glance, power looks like a pure commodity with no edge—everyone buys electrons from the same market, right?

Not quite. While electricity itself is fungible, strategic advantages do exist: locking in cheap power through long-term purchase agreements, building near abundant generation sources (hydro, nuclear, natural gas), or even owning generation assets outright. Companies that secure favorable energy access early can maintain cost advantages that persist even when spot prices swing. However, these moats are tied to location, capital, and contracts—not proprietary technology or network effects.

For the L2C portfolio, I see power as table stakes, not a high-conviction opportunity. It’s critical infrastructure, but the returns and defensibility don’t match what I’m targeting with my other AI plays. So while energy strategy matters for hyperscalers and neocloud providers (like Nebius securing datacenter power), I’m not investing directly in power generation or utilities.

This is the revenue trend I expect from this category, alongside the AI Capital Expenditure trend.

My other picks

Rocket Lab ($RKLB)

The leading public space company focused on small-to-medium launch vehicles and space systems. Rocket Lab is building an end-to-end space company, aiming to compete with SpaceX. So far, it has successfully scaled a small launch vehicle (Electron) from the ground up, and is now preparing to enter the medium-lift market in 2026 with its Neutron rocket, targeting a segment currently dominated by SpaceX’s Falcon 9. The company’s competitive edge comes from vertical integration—manufacturing nearly all components in-house—and exceptional leadership under founder Peter Beck.

Why is this an AI play? Space infrastructure is becoming critical to AI’s future. AI models need massive amounts of data, and much of that data will come from space: Earth observation satellites for climate modeling, agricultural optimization, disaster response, and autonomous systems. Additionally, as AI compute demands grow, there’s serious discussion about space-based datacenters to access abundant solar power and natural cooling. Rocket Lab provides the launch services and satellite manufacturing capabilities to make this infrastructure possible. As AI drives demand for more satellites and space-based services, Rocket Lab is positioned to benefit directly from that secular growth trend.

I expect these characteristics will make Rocket Lab compound for the foreseeable future. $RKLB is already a multibagger for me (about 10x).

Check https://www.lorenzo2cents.com/p/rocket-lab-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of the allocation, my strategies, and much more.

Mercado Libre ($MELI)

The backbone of Latin America’s digital economy, poised to benefit from continent-level network effect: a full‑stack “Amazon e‑commerce + PayPal fintech” platform using its scale, logistics, and data to build an unbeatable commerce moat that feeds a sticky, high‑margin fintech ecosystem across payments, credit, and financial services.

Why is this an AI play?

Mercado Libre is quietly becoming an AI infrastructure layer for LatAm’s consumer economy. It uses AI to optimize logistics and delivery routes, fight fraud, score credit risk, and personalize recommendations across commerce and fintech. On top of that, it is rolling out AI‑generated ads so even small sellers get high‑performing creatives, and building AI‑powered internal systems to drive real‑time decisions at scale.

The result: MELI doesn’t just benefit from AI demand—it is turning its data, distribution, and infrastructure into an AI‑native rails system for commerce and finance in Latin America.

I expect these characteristics will make Mercado Libre compound for the foreseeable future.

Check https://www.lorenzo2cents.com/p/mercado-libre-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of the allocation, my strategies, and much more.

Lorenzo2cents Portfolio strategy for 2026

The current L2C Portfolio allocation to $RKLB is about three times the optimal allocation suggested by the “Allocation_Optimizer” Tool, while the allocation to $MELI is about half of what’s recommended.

If you’re unfamiliar with this, please review https://www.lorenzo2cents.com/p/l2c-lesson-2-mastering-portfolio.

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In this third episode of Invest in AI, Avoid the Bubble, I’m exploring the category that most closely tracks AI Capital Expenditure—and will likely deliver the most satisfaction in the short term: AI Infrastructure.

This category isn’t the safest, which is why it represents less than 15% of my portfolio. If AI Capital Expenditure cools down over the next few months, these stocks—which heavily benefit from such investments—will tank. However, if the AI CapEx supercycle lasts for years (let’s say more than three), we’re in a much better position.

Here’s why: AI infrastructure serves two main use cases. First, training models—Google, xAI, OpenAI, and others still release updated models every few months. Second, inference—the actual usage of models for real-world applications (like when we ask ChatGPT a question). Right now, still a lot of workload is on training. Over time, inference will overwhelmingly dominate in terms of resource share.

As AI becomes the new way of doing everything and penetrates everyone’s life and industries, even when demand for new chips and infrastructure cools down, AI Cloud providers like AWS will keep invoicing customers as before. Meanwhile, their cash flow will spike as CapEx decreases. Sure, revenue growth will take a hit, but higher profitability will partially offset it—and stocks will likely reprice without hard crashes.

This is the revenue trend I expect from this category, alongside the AI Capital Expenditure trend, assuming demand—and therefore CapEx—lasts long enough.

Below is the company I’ve identified, researched deeply, and own for the long term. After owning it for a few months, it’s already a multibagger (2x), but I think much more will come.

My pick in the category

Nebius ($NBIS)

The universal substrate for agentic computation in application software. Nebius belongs to the neocloud category—alongside Iren, CoreWeave, and others. By neocloud, I mean a new kind of cloud provider built from the ground up for AI, where GPU-dense infrastructure, AI-native tools, and model-centric pricing replace the old “generic compute” cloud. What differentiates Nebius from the others are three things: top-level management, availability of capital, and a clear mission (becoming a hyperscaler).

On that last point, unlike most competitors, Nebius is purposely building a software layer on top of datacenter hardware to orchestrate computing—aiming to reach top-level performance and compute efficiency while locking in its customers. Nebius has just launched Token Factory, which lets you build enterprise workflows using different LLMs simultaneously with top-level efficiency. As open-source models reach satisfying intelligence levels, Nebius will become the go-to cloud provider for every enterprise and startup seeking performance, governance, control, and affordable prices.

I expect these characteristics will make Nebius compound for the foreseeable future.

Check https://www.lorenzo2cents.com/p/nebius-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of the allocation, my strategies, and much more.

Like the graphs and my analysis? Check out my “Tools I Trust & Use” page to see what I use—and grab ‘em via my links to support my work!

Tools I Trust & Use

TIKR is my go-to tool for all my stock research—it’s a game-changer for spotting winners before the crowd. For you, my Lorenzo2Cents readers: Use the Coupon Code lorenzo20 for 20% off, live now through February 6. Don’t miss out—grab it.

Next Episode

In the next and final episode of the series “Invest in AI, Avoid the Bubble,” I’ll dig into the rest of my indirect plays and explain which categories I’m avoiding entirely—and why. Knowing where not to invest is just as important as knowing where to invest.

Stay Tuned (It will be out in about a week).

Lorenzo2cents Portfolio strategy for 2026

The current L2C Portfolio allocation to this category is about two times what suggested as optimal allocation by the “Allocation_Optimizer” Tool.

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I’m waiting for my Business Ontology to tell me to smash the BUY button on my Interactive Brokers account.

As you can se…

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In this second episode of the series Invest in AI, Avoid the Bubble, I’m exploring what I believe is the most interesting category: AI-Powered Products & Services. The reason? These businesses benefit from AI progress and investment, but face no backlash when AI CapEx slows down. Unless the scenario is catastrophic—like a sudden investment cutoff that drags down the entire economy—they could even benefit from a rebalance of supply and demand, as inference compute costs drop.

These are the everyday tools that use AI to handle jobs humans used to do. Think getting insurance quotes in seconds (like Lemonade’s app that skips the long calls) or designing, manufacturing, and selling superior products that are more science- and data-based and increasingly tailored to individual consumers.

The secret sauce for creating such a business is having a unique, significant dataset at your disposal and/or (better yet, both) an AI-first organization. This means that once you reach a minimum headcount level, you can scale revenue without adding human resources.

In 2026, these organizations are extremely rare, but I expect more will emerge from now on.

Below is a list of companies I’ve identified, researched deeply, and own for the long term. I believe all of these will be multibaggers from here (some already are for me).

My picks in the category

Lemonade ($LMND)

Reinventing the insurance sector. Lemonade is redefining insurance by operating as an autonomous organization. Unlike incumbents that rely on brokers and agents, Lemonade uses a direct-to-consumer model. While this limits distribution in the short term, it allows them to collect orders of magnitude more data than the standard playbook. Since insurance is a statistical game—where you optimize top and bottom lines based on available data—Lemonade is poised to achieve superior results for years to come. Hardly anyone will catch up soon. To complete the data moat, Lemonade’s visionary leaders have built an autonomous organization where AI agents handle the work (onboarding new customers, paying claims, etc.), meaning they’re scaling revenue while headcount remains flat.

I expect these two characteristics will make Lemonade compound for the foreseeable future. Lemonade is already a multibagger for me (about 5x).

Check https://www.lorenzo2cents.com/p/lemonade-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of the allocation, my strategies, and much more.

Oddity Tech ($ODD)

Revolutionizing the beauty industry. Oddity Tech designs and sells beauty products, such as foundations, directly to consumers. Unlike incumbents that rely on the classic brick-and-mortar model, Oddity operates a direct-to-consumer model. While this limits distribution in the short term, it allows them to collect orders of magnitude more data than the standard playbook. Thanks to this data moat and its AI engine, Oddity recommends the best product for each individual—like the perfect foundation for your skin tone—and develops new molecules to better address individual needs.

I expect these characteristics will make Oddity compound for the foreseeable future.

Check https://www.lorenzo2cents.com/p/oddity-tech-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of my allocation, strategies and much more.

Duolingo ($DUOL)

Transforming education by making learning both fun and effective. Duolingo owns and operates a platform primarily for teaching languages through its app, and is slowly expanding to teach other topics—aiming to become the go-to place to learn anything. Duolingo is already the undisputed leader in this industry, and given its top-class leadership and data moat—millions of people using the app daily—I believe this gap will only widen. On top of that, Generative AI has increased by orders of magnitude the speed at which they can create new content for courses, making their data moat even stronger. Duolingo, like Lemonade, has a big advantage in this race: it doesn’t need to move atoms to deliver its services, meaning they will likely become an autonomous organization where the top line grows while headcount remains stable.

I expect these characteristics will make Duolingo compound for the foreseeable future. Check https://www.lorenzo2cents.com/p/duolingo-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of my allocation, strategies, and much more.

Hims and Hers ($HIMS)

Redefining healthcare by making it accessible and affordable. Hims owns and operates one of the largest telehealth platforms and aims to transform it into a longevity service delivering the highest value per dollar for individuals. Unlike big pharma—where collecting consumer feedback and tailoring medicines to individuals is an afterthought—Hims’s philosophy centers on personalization. With 2.5 million subscribers, Hims has built a clear data moat. They’re now developing an AI engine to leverage this data, positioning them to become the undisputed leader in healthcare. As a bonus, they’re investing heavily in verticalizing their business, allowing them to compound their own products, achieve cost leadership, and conduct research for better healthcare.

I expect these characteristics will make Hims compound for the foreseeable future. Check https://www.lorenzo2cents.com/p/hims-and-hers-articles to know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of my allocation, strategies, and much more.

Tesla ($TSLA)

The ultimate leader in transportation autonomy, sustainable energy, and humanoid robotics. There’s not much to say here—either you see it or you don’t. Tesla enables moving atoms with no people involved. Whether you’re thinking about autonomous cars or humanoid robots, Tesla provides the technology for paradigms that will completely change the world in a few years. But it doesn’t end there. Beyond providing technologies to make the world autonomous, Tesla is poised to become an autonomous organization itself—specifically, the first autonomous manufacturing organization. This stems from two drivers: first, Tesla will be the first to implement Optimus in its factories; second, Elon’s mastery of optimization (his early days at Twitter and DOGE tell you anything?). Tesla will scale cars, batteries, solar panels, and robots without increasing headcount beyond a certain point. And by the way, that will mark the beginning of the age of abundance. A new world.

I expect these characteristics will make Tesla compound for the foreseeable future. To know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of my allocation, strategies, and much more.

META ($META)

Unlocking AI’s future with an unparalleled dataset. Meta is the world’s largest social network by users, giving it the biggest people dataset on the planet. Meta also leads in large language models and has one of the largest AI compute infrastructures available. Combined with founder-led leadership—CEO Mark Zuckerberg has proven himself among the best at keeping his company competitive—I believe Meta will be one of AI’s greatest beneficiaries. I’m not sure exactly how yet, but they’ll either provide the best AI companion to billions of people or create the next consumer ecosystem by overtaking Apple with their smart glasses. Either way, I believe they’ll make it happen.

I expect these characteristics will make META compound for the foreseeable future. To know more and subscribe to Lorenzo2cents (L2C) Premium to access the details of my allocation, strategies, and much more.

What to expect

This is the revenue trend I expect from this category of businesses, alongside the AI Capital Expenditure trend.

Like the graphs and my analysis? Check out my “Tools I Trust & Use” page to see what I use—and grab ‘em via my links to support my work!

Tools I Trust & Use

TIKR is my go-to tool for all my stock research—it’s a game-changer for spotting winners before the crowd. For you, my Lorenzo2Cents readers: Use the Coupon Code lorenzo20 for 20% off, live now through February 6. Don’t miss out—grab it.

Next Episode

In the next Episode of the series “Invest in AI, Avoid the Bubble”, I’ll be digging into the “AI infrastructure” category, the one from which I will likely get most of the boost in the short term. Stay Tuned (It will be out in about a week).

Lorenzo2cents Portfolio strategy for 2026

The current L2C Portfolio allocation to this category aligns well with the optimal allocation indicated by the “Allocation_Optimizer” Tool, even though not exactly the same.

If you’re unfamiliar with this, please review https://www.lorenzo2cents.com/p/l2c-lesson-2-mastering-portfolio.

Read more

Investing in AI isn’t optional—it’s essential if you aim for meaningful returns over the next decade. Yet it carries an inherent risk: demand will eventually plateau, CapEx will contract in tandem, and stock prices will crater as a result. Call it a bubble or not, the reckoning feels inevitable—though whether it strikes in 10 months or 10 years remains anyone’s guess. So how do we capture the upside of this industrial revolution without betting the farm?

Here’s my strategy.

AI market Classification

Looking into the AI market and the investable companies, I divided it in a few categories and tried to associate to each of them three labels, if they are or can be a bottleneck for the supply chain, if they have a directly proportional benefit from AI Capital Expenditure, and if they have an inherent potential durable and defendable edge.

In the first scenario, bottleneck, stocks in that category can surge short-term because demand is exploding and supply is short. This acts as a quick boost—it might even last years until supply catches up. But it’s just a “nice-to-have.” Without a lasting moat and/or in case the business benefits proportionally from AI CapEx, you’re in trouble: the stock rockets on hot demand, then crashes when things cool off.

If the business has a strong moat and its revenue does not depend heavily on AI CapEx—ideally paired with a bottleneck—you’ve hit the jackpot. You get the short-term boom for fast growth, but when demand eases, your investment doesn’t vanish. The business keeps humming. Sure, you’ll face some market dips, but likely nothing brutal like losing two thirds of your stake. There, is where I want to be.

Overall Takeaways

To sum up, after cutting through all the AI buzz, here’s what matters most. Don’t run after every hot new company. Focus on the ones that can surf the big wave—and land softly if it breaks.

• Bottlenecks give quick boosts: Things like chips, servers, and power are hard to get right now. That pushes stock prices up fast. But watch out—without a strong edge, it’s a dead end. Demand will slow someday, and you’ll lose big.

• Strong edges keep you safe: The real win? Mix a bottleneck with something that sticks, like special data, smart add-on software, or a big group of loyal users. Picture AI insurance that really gets your life, or chips locked into tools only you offer. That’s your backup plan for years ahead.

• Build on the full pile, not one piece: AI is like a building—apps and services on top, models in the middle, hardware at the bottom. Pick companies that own the whole thing (think about Tesla). They grow wild during peaks and keep going steady later. Quick note on data centers: When the big training spend drops, everyday AI use (inference) keeps cash coming in. Growth eases, but costs crash too—turning spots like Nebius into money-makers fast. It’s not over; it’s a smart shift: from high growth to an utility.

• My top investing rule: Look for that perfect mix—big jumps now, solid steps later. Markets will shake you a bit, but you won’t sink. Whether it’s a bubble or not, this keeps your investments safe and growing for 10 years and more.

Like the graphs and my analysis? Check out my “Tools I Trust & Use” page to see what I use—and grab ‘em via my links to support my work!

Tools I Trust & Use

TIKR is my go-to tool for all my stock research—it’s a game-changer for spotting winners before the crowd. For you, my Lorenzo2Cents readers: Use the Coupon Code lorenzo20 for 20% off, live now through February 6. Don’t miss out—grab it.

Next Episode

In the next Episode of the series “Invest in AI, Avoid the Bubble”, I’ll be digging into the “AI-Powered Product & Services” category, in my view, the most interesting one. Stay Tuned (It will be out in about a week).

Lorenzo2cents Portfolio strategy for 2026

This is how I positioned my portfolio for 2026.

Here are the detailed positions for each category with their weights.

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