The AI Trade Deconstructed: AI Builders, AI Suppliers and AI Product firms!

    When I posted my valuation of Anthropic a few days ago, I noted that turbulence lay ahead, both for the company and the market. I did not expect that turbulence to be this forceful and arrive this soon, but markets don’t have an obligation to wait until it is convenient for investors, to adjust to new realities. With the election results on November 3 acting as a catalyst, investors initially responded positively with stocks up on Wednesday, but reversed course sharply on Thursday, with the NASDAQ down close to 6% and the S&P 500 declining 4%. This drop has again brought to the forefront questions about the AI trade and whether it has run its course. 

    In this post, I will start by referring to my earlier note on AI as a business, and use the ideas in it to deconstruct the many players that are lumped together as AI companies, from those spending the money building the data centers (hyperscalers) to the firms benefiting from selling the builders the ingredients for these data centers (chip makers, electrical equipment, power and even real estate) to the companies that are spending considerable amounts , leasing these data centers for long periods  (with the LLMs in the lead). I will argue that while all of these groups benefit, at least in general, from AI’s success at creating large and profitable markets, we are at a point in the AI disruption, where their interests and payoffs will diverge, with implications for both the businesses and investors. I use last week’s market carnage in technology and examine how the market treatment of these different groups may be a reflection of a more discerning market, less willing to mark up and mark down all AI companies, and more likely to treat them differently, as their business interests diverge.

The AI Transition 

    My journey on AI started with a selfish and narrow intent, which was to evaluate an investment I had made in Nvidia in 2018, for reasons entirely unrelated to AI. I thought that Nvidia, at the time, was a innovative chip company with more pricing power than many in its peer group, and felt that it was undervalued, given its prospects. While the stock outperformed my portfolio early on, it was the ChatGPT debut on November 30, 2022, and the subsequent AI buzz that put its stock price on steroids. That left me with a challenge, albeit a welcome one, of deciding whether I should continue to hold on to a stock that had already become a twenty-bagger or sell some or all of it. Since much of Nvidia’s supersized success was being propelled by AI demand, that required me to take my first look at how big the AI market would be, and how well Nvidia was positioned to take advantage of that market. If you have read my multiple posts on Nvidia over the last three years, you may be aware that I sold all my shares in the company in four installments, with the last sale happening earlier this year. As many of you have been quick to point out, my decision to gradually decrease my Nvidia position has led me to leave money on the table, but I have no regrets. 

The Players

    In the process of trying to understand AI and disentangle the many players in the AI space. I break them down broadly into four groups:

1. AI Suppliers: While AI is viewed as a technology business, we have learned over the last few years that it is much more capital intensive than any other tech endeavor in history. The data centers and infrastructure that are needed to power AI agents and models has created a surge in demand for ingredients. The biggest portion of the cost, at least so far, has been on high-powered  chips, with Nvidia claiming a lion’s share, and enough left over demand for other chip companies to benefit. This infrastructure build out has increased demand for electrical equipment, with power and real estate companies also gaining as data centers get built.
2. AI Builders: Alphabet, Amazon, Meta and Microsoft, four companies that were part of FANGAM, the acronym that predates the Mag Seven, bought into the AI hope, and joined by a handful of other players (Oracle, Coreweave), they have invested hundreds of billions of dollars into AI cap ex (data centers and infrastructure). The market cut these hyperscalers slack early on, rewarding them for their capital spending, even though that spending was denting profitability at these companies and demolishing cash flows, and part of the AI trade question has been whether it is running out of patience, waiting for payoff.
3. AI Products: In the last two years, companies which are on the frontlines of creating AI products and services, have became the focal point of investors, albeit in the private investing world, with talk of public offerings pricing them in the trillions of dollars. SpaceX became the first of these to list, but Anthropic’s IPO is imminent and OpenAI will join, sooner rather than later. There are  other companies (Palantir is the most prominent), albeit only a handful, that are  employing AI to create new products and services that they sell to their customers.
4. AI Customers: There is a fourth group, perhaps more critical than any of the above, that will determine the value creation potential and long-term standing of this business, and that includes all of the potential customers, individual as well as businesses, for AI products and services. While the premise for AI hype is that its reach (both in terms of numbers and monetization) will be huge, it is undeniable, even if you are an AI diehard, that there is little of substance to show at the moment. Even if you take the ARR numbers that are being leaked by the lead players, the collective revenues from AI products and services right now is about $250 billion, a small number relative to what has been invested in the space. The entire AI ecosystem is built on the premise that this group will become much larger, both in terms of reach across businesses and money spent in the trillions of dollars.

The Linkages

  There are links that hold these groups together, and I have tried to capture those linkages and how they play out as revenues, expenses and earnings in each group:

This simple, and perhaps even over-simplistic, breakdown of AI does, in my view, provide a good starting point for understanding why their interests and market pricing is more likely to diverge in the future, as the business takes form.  The linkages between the AI groups come from three core transactions between these groups that determine not just their current earnings but also their long term value propositions:

1. Building the AI infrastructure: The  AI builders are spending the money building the infrastructure and much of it is being legitimately classified as capital expenditures, though there is a discussion to be had about the life of that infrastructure. The reason that the investment life matters is that it determines the amortization schedule for the AI cap ex, and consequently the negative impact on earnings in the current year; the free cashflows  are lower since they take a direct hit from the cap ex. The suppliers report these same capital expenditures are revenues in the current year, and when netted out against their operating costs, increase earnings at these companies. I wrote about how the net effect of the infrastructure buildout has been an increase in aggregate earnings at US companies in the last year, and has supported stock prices, even as interest rates have soared.
2. Leasing the AI infrastructure: The AI architecture, once built, is being leased out by the builders to the businesses that use the data centers to create and support the products and services they offer. The builders report the lease payments as revenues, allowing them to earn a return back on their infrastructure build, but for the AI product and service businesses, these payments are either operating expenses, if annually reset, or financial expenses, if structured as contractual commitments over many years; the latter will create imputed interest expenses and depreciation.
3. Creating and selling AI products: Using the leased AI architecture, the AI product and service businesses create products they sell to consumers, both individual and enterprise, and record the revenues they generate. With the LLMs (OpenAI, Anthropic), these products are AI agents and frontier models where users pay either in subscriptions or based upon usage, and net of the costs of creating these products, hopefully into profits. 

The Timing of Benefits

    Given that AI infrastructure can take years to come online, there will be timing differences across the groups, in terms of seeing the benefits from their business models, with AI suppliers being the first to see revenues and profits, followed by AI builders, with AI product & service businesses generally being last to see profitability.

1. Hope and Hype        

    In the first phases of a disruption, the visions of a huge market often drive actions on the part of all of the AI groups, but the way they play out and how they show up in results will vary:

• The suppliers of the ingredients that go into AI infrastructure will be the first to see tangible benefits as the spending on AI architecture shows up immediately as revenues in income statement and as incremental profits. 
• For AI builders, no matter how merited the build-up (in terms of delivering value over time), the immediate effect on both earnings and cash flows will be negative. The reason the earnings effects has been muted with AI is because the biggest AI infrastructure investors (Meta, Microsoft, Alphabet and Amazon) have earnings and cashflows from legacy businesses and the cloud businesses (thus playing a role asiAI suppliers) that have provided a buffer, but the free cash flow impact has been real.
• For companies in the AI product group, which are nascent businesses, there will be little to show in terms of revenues, since neither the infrastructure nor the products are ready to go. With R&D and employee compensation expenses, they will be money-losing machines.

2. Investment Buildup

    In this phase, the AI infrastructure building continues at a torrid pace, but portions of it are complete and ready for use, and the consequences again will vary for the groups involved:

• As growth in AI infrastructure continues, AI suppliers will continue to see growth in revenues and profit from the continued building, though growth rates will come down with scale. In addition, new entrants may start to compete on prices, reducing pricing power at least for some of the supplier groups.
• For Ai builders, the portion of the AI that is ready to use can be leased out, allowing for a monetization of at least a portion of the AI investment. Since the investment continues to increase, and much of it is still not operational, on measures like return on capital, the AI buildup investment will not measure up. Markets will often be willing to forgive, at least with managers that they trust, on the assumption that things will get better over time.
• As AI infrastructure comes online, AI product companies can start generating products and services, and to the extent that they are growing from small scale, growth rates can be exponential. Those revenues though will generally be dwarfed by expenses from product development, continued research, and employee compensation, leaving the companies with large losses.
• With AI users,  a mix of early adopters and experimenters, in a subset of industries where AI is viewed as having the most promise, will emerge. Given AI’s strengths, software businesses were among the first to be exposed to AI products, with consequences for competitive position and profitability, but AI agents also have found a foothold in white collar businesses.

3. Business Building

    A big end market notwithstanding, business building is hard and in this phase it is inevitable that questions will turn to unit economics, economies of scale and eventual profitability, with each group facing different degrees of questioning.

• By this stage, AI suppliers should have fully formed business models, but revenue growth rates will ease, as new infrastructure spend becomes smaller, at least on relative terms. As builders try to develop cheaper supply sources, either by bringing some of it in-house or by looking at competitors, margins will start to compress, and you will see some suppliers focus on the infrastructure replenishment/replacement market, rather than on new infrastructure. Since markets are forward-looking, the pricing of these companies will reflect expectations of lower growth and the companies will look cheap on a pricing basis (low PE ratios, low EV multiples), relative to their history.
• As more infrastructure becomes operational, AI builders will begin to see more revenues and earnings from existing investments, but the magnitude will depend largely on whether demand from AI product companies is running ahead of capacity (good for revenues and earnings) or behind (bad for revenues and earnings). As returns on invested capital pick up, markets will make their own assessments of whether they will end up adding or destroying value, and reassess the pricing of these companies accordingly.
• AI product companies will jockey for position in the market, looking for ways to differentiate their products and preserve pricing power. With compute power being a key driver of both AI model power and growth in revenues, AI companies will try to not only get more AI infrastructure under control, but, if it is scarce, lock it for the long term. In the aftermath, AI product companies with a duration problem, where AI compute commitments are locked up long term, while their revenue streams are still in their formative phase and subject to short term volatility. Again, the forward-looking nature of markets implies that the pricing of these companies will look high, relative to current operating numbers, but that pricing will be volatile, as markets reassess prospects.
• AI users will find themselves on the receiving end of sales pitches for AI products and services, with the key selling point being improved productivity, with the warning included that not buying these products will put them at a disadvantage, relative to competitors.  For AI to find its business feet, you need the customer base for AI products and services to become both wider (cutting across many different industries) and deeper (where customers spend far more on AI, on a repeat basis). 

Commonalities and Competing Interests

    Even though the businesses in the AI space have very different business models, there is one common element that holds them together; the value of all of the players in the ecosystem ultimately will be determined by the size of the AI market and its profitability. As I noted in my earlier posts on AI, if the end market for AI products and services is small, because of the imperfections as AI tools, regulatory restrictions or limited reach, the entire AI ecosystem (builders, suppliers and product businesses will all feel the pain. Conversely, if AI delivers on its promise as a powerful productivity tool that works across many businesses, all of them benefit, albeit in different forms and different degrees. If history is a guide, it is the product companies that will be the primary benefactors, but the central role of compute power in the AI business may give AI builders and suppliers more of a share. In addition, if one of the groups faces growth and survival challenges, those pains will spread into the other groups as well. That may explain why you see so much circular financing in the AI ecosystem, where financially better- positioned and stronger players like Nvidia invest in or offer financing guarantees to weaker players, either to ensure survival or to provide an impetus for continued investment spending.

    Those common interests notwithstanding, these businesses also have competing agendas, and decisions and actions that affect one group negatively can create benefit for others. Thus, if AI builders choose to build too much infrastructure, too quickly, it will hurt them and their shareholders, but that overbuilding will be a bonanza for the AI suppliers, as chips, equipment, power and real estate players all benefit from the overbuilding. Once built, it is in the AI builders’ interests to lease out the infrastructure for longer periods than merited, locking in their revenue streams, but for the AI product companies, those long term commitments can increase default risk. In short, as the AI business matures, I would expect these competing interests will often overwhelm the common interests, creating divergence. 

    For much of AI’s early existence, the commonalities have overwhelmed the competing interests, with the hope of a large market for AI products and services the pushing up market pricing for all of the groups, with AI chip companies like Nvidia being rewarded for the expected growth in AI architecture, thyperscalers gettting marked up, when they spend more on architecture, and the compute power commitments atAI product companies being viewed as a competitive plus, rather than minus. That, in a nutshell, has been the AI trade, but as the AI disruption moves through the disruption, it is inevitable that this will change, with the market differentiating across groups, picking winners and losers.

AI Companies – Letting the Data Speak!

    With that long lead-in, I think that we are better positioned to take a look at the AI trade, both in terms of where it is right now and where it may be going next. I do believe that the easy AI trade, where you could buy any company that was part of the AI ecosystem (supplier, buyer or product) and make money from a general optimism about AI’s potential is mostly done, and there are many traders who can point to profits they made from that trade. While last week’s market collapse in AI stocks may have drawn attention, the AI trade has been far shakier in 2026, as strength is some AI groups has masked weakness in others, reflecting a market trying to discern differences across companies in the space. To get an empirical measure of how the different AI groups have performed this year, I create samples of companies in each of the three AI groups (supplier, builder, user) and before you take issue with my choices, I will concede on four points:

• Public vs Private: Data availability and the requirement that there be a market price restricts the sample to publicly traded companies. That not only reduces sample size and creates sampling bias, but it also has disparate effects across the different groups. In particular, two of the biggest players in the AI products grouping, OpenAI and Anthropic, as well as almost all of the smaller players (Mistral, Qwen and Deepseek) do not make it into the sample, because they stayed privately held, and SpaceX enters the public company sample in the middle of 2026.
• Missed companies and changing sample: Since there is no separate accounting categorization for AI-related revenues and expenses, I relied on public descriptions of the companies operating in each of three AI groupings. Those descriptions are likely to be incomplete, but the companies that are missed are smaller and lower-profile companies, in terms of both operating metrics and market capitalization. In addition,  as companies go public or exit the market, there will be changes in the sample.
• Not pure plays: Most of the companies in this sample operate in other businesses, many of which pre-date their entry into AI. This is especially true of the hyperscalers, where Amazon, Alphabet, Meta and Microsoft have legacy businesses that generate large earnings and market value. That will make it difficult to isolate the AI effect on market capitalization, because changes in market cap at these companies will be driven not just by their AI actions, but by legacy business performance.
• In multiple groupings: Some of the companies in this sample operate in more than one grouping. Meta, Alphabet and Microsoft, in addition to be major investors in AI cap ex, are working on their own versions of LLM (Muse for Meta, Gemini for Alphabet and Co-Pilot for Microsoft) to enter the product market. I have included SpaceX as an AI product company, but at the moment, almost all of its revenues on its AI arm come from leasing out its compute power to Anthropic and Alphabet, making it more of an AI builder than AI product company.

AI products group percentage change does not include SPCX

As you can see in the table, looking at the year-to-date numbers for 2026, AI chips and real estate have run ahead of the market, and the rest lag the market; again, you should read the AI products and services grouping with caution, because it has only three companies in it, and SpaceX made its entry into the group mid-year. In fact, AI companies, in the aggregate, increased their share of overall US equity market capitalization for much of the year, peaking at about 37% on September 30, 2026. Since the end of the third quarter of 2026, though, every AI group has struggled, with the exception of AI real estate & construction. Last week, in particular, was a testing one for AI companies, with Thursday (November 5) standing out and in the figure below, I report the dollar value changes of each group by day:

An Assessment of AI as investment: The Big Market Delusion!

    I would like to think that I have kept an open mind about AI’s business potential, in my writings, and I have been taken to task by AI advocates, for not being optimistic enough about the size of the AI market, and by AI skeptics, who believe that I am inflating its size. In almost all of my writing about AI, though, I have argued that it is exposed to what I call the big market delusion, where the potential of a big markets in conjunction with overconfidence on the part of founders, VCs and CEOs, leads to over investment and a correction. The picture below is the generic version:

I am seldom certain about anything in markets, but I believe that AI, because of its reach and potential, almost certainly has led to over investment, by AI builders, over commitments to compute power by LLM companies and over pricing, by the market, at least in the aggregate. The correction, when it comes, will cause pain, but the pain will be distributed unevenly, and for those who believe that AI has potential but have found the companies in the space over priced, it will provide opportunities. Before you decide that this (the over investment, over pricing and subsequent correction) is a problem, it is worth noting that this phenomenon is a feature, not a bug, that is associated with uncertainty and big change, and that it is, in spite of the costs of the cleanup, is a pre-requisite for change.

    This big market delusion suggests that you should either not buy any of the companies in the AI space or even sell short on these companies, but here is why you may not arrive at that conclusion:

1. Uncertainty about correction (when and what form): Even if you are right that investor and business overconfidence has lead to overreach, you need the market to come to agree with you, and it is difficult, if not impossible, to try to predict when in will happen, and what form it will take. The obvious response that many investors have is to just avoid AI companies for the moment, but since it is just a large portion of the market, this becomes a variant on market timing, with questionable payoffs.
2. Catalysts for correction: The catalysts that trigger the correction can  be bad news about a leading Ai company, a macro or political event that makes investors reassess the landscape, and history offers little useful guidance. The big market delusion is ultimately built on a behavioral characteristic (over confidence) and the correction has as much to do with investor psychology, as with financial fundamentals. It is one reason why I am not quick to dismiss charts and technical indicators (price and volume) as investing voodoo, since these tools may be better attuned to detecting these mood and momentum shifts. 
3. Acting in the fact of uncertainty: Since the correction process is difficult to time, if you are an investor of long standing in one of AI’s many winners, you should use each correction phase to sell a portion of your shares. You will give up upside, by doing so, but you will not be completely exposed, when the correction happens. If you decide to buy AI stocks after the correction, don’t wait for the bottom, since you may never hit it. Find the companies in the AI marketplace that you would like to own, set a trigger pricing or a valuation at which you would buy, and automate the process. Thus, if you like SpaceX, but only if its market cap is $1 trillion, put in a limit buy at that value, and let it get filled automatically. 
4. Go where it is darkest: One of the themes of my valuation and investment philosophy classes is that the payoff to assessing fundamentals and valuing companies is greatest in sectors and at times of maximum uncertainty. If you are an AI advocate, and you have generated supersized returns over the last four or five years from just buying companies in the AI space,  you are entering a phase, where  even if your AI optimism holds, you should prepare yourself for more periods like last Thursday, where the market mood on AI darkens, and use those periods to be more discriminating in your AI investment choices.

For those of you have read this far expecting a clear signal of whether now is the time to sell or buy, I know that you may find my conclusions to diffuse to be actionable. 

Implications for Anthropic IPO

    Since I have spent much of the last few weeks valuing Anthropic, ahead of its IPO, and following up on that valuation, there is no avoiding the question of what the market and political turmoil in the two weeks will do that IPO. I have seen rumors floating around of a cut in the IPO pricing (from the initial estimate of $1.5 to $2 trillion to a lower number and some that the IPO may be delayed into next year. 

• As I noted in an earlier post, IPOs are priced, and in young businesses, with uncommon metrics. After not hearing talk of annualized revenue run rates for a few weeks, there was a leak that Anthropic’s ARR for October hit $80 billion, which was touted as both good news (by some) and bad news by others. The truth is that pricing is a mood and momentum game, and it is undeniable that the mood has darkened not just for AI, but also for the market in the last two weeks. If you consider bankers who are terrified of over pricing a trillion-dollar plus offering and picking up the pieces, the pressures to lower the offer price only get stronger.
• On the valuation front, it is not market mood that matters but events that change the fundamentals. The most critical of these were the election results, where voters sent a clear message about their aversion to data centers. In Texas, which is home to about a third of the new data centers coming online in the near term, it may not stop existing data center construction, but it will slow the growth of data centers, at least in the United States. It is also possible that the newly elected congress will write in tighter restrictions on AI, creating a bigger drag on margins.  For those who are more focused on seeing the glass as half full, this will also vindicate Anthropic’s rush to lock in compute power commitments, before the rules of the game changed, and the limits on data centers may give Anthropic a moat against competitors. Just to give you a measure of how sensitive the Anthropic valuation is to these changes, increasing how much Anthropic will have to invest to add compute power in future years (by lowering the sales to cap ratio from 3 to 2.5) and increasing the drag on margins from regulation (from 5% to 7.5%) reduces the estimated equity value to $1.25 trillion, even if you leave the target revenues at a trillion dollars.

I have no sources in investment banks, but I think that this IPO is too far along for bankers and the company to withdraw or delay, without markets reading worst case scenarios into their actions. That said, I would not be surprised to see Antrhopic priced closer than $1.25 trillion, with perhaps a smaller cash raise (with fewer shares being offered), allowing for that “price jump” on the trading day and a buzz that carries over.

Conclusion

    As AI moves from the hype phase to business building, I hope that we stop talking about  “the AI trade”,  as a catch all for AI companies, and start differentiating between the different groups for two reasons. The first is that as the AI business starts to take form, there will be developments that are good for one group and bad for others; the election results clearly affected AI suppliers more negatively than it did AI builders. The second is that when you have crises, the market will lose its head and mark down all AI companies indiscriminately, offering you the chance to buy those that you believe should not have been marked down.   I also think that it is time that we stop making overly broad assessments of the AI business that lock us into our priors. Thus, if you start with the presumption that the entire AI phenomenon is a “bubble” (notice that this is the first time in the post that I have used that word), your mind is already made up and no AI company, no matter what group it belongs to, will be worth its price. That view, no matter how strongly held, would have kept you out of a segment that has added almost $18 trillion in market capitalization between 2023 and 2026, and even with a strong market correction, it is likely that staying out of the AI bubble would outweigh what you would have earned by investing in some of these companies. At the other extreme, if you are so dazzled by the technology that you see this as a sure win, you are setting yourself for disappointment, because not only are there no sure wins in equity markets, but the pathway to AI nirvana will be come with challenges that test your faith. In short, stay with your beliefs about AI but leave the door open for different opinions.

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Spreadsheets

1. Anthropic IPO valuation: A post-election update

Blog Posts on AI in 2026

1. To a Trillion Dollars and Beyond: A SpaceX Odyssey!
2. Revisiting SpaceX: A post-Prospectus valuation
3. OpenAI, Anthropic and SpaceX: The S&P 500 Inclusion Question and Investment Consequences
4. AI Scenarios: From Doomsday to Do-nothing!
5. AI’s Bar Mitzvah: From hope & hype to business questions!
6. Stock Prices, Earnings and Cashflows: The AI Effect!
7. From Eyeballs to ARR: Buzzy Pricing Metrics and the Anthropic Pricing
8. The Anthropic Prospectus Arrives: My (but not the) valuation of Anthropic
9. Heeding the Anthropic Feedback Loop: Cost of capital delusions and distractions!