Jahanara Nissar

NVDA bulls appear to have decided to sit out the immediate aftermath of the earnings call and prepare for a temporary pullback. They appear secure in their belief that the H-series continues to be supply -constrained, and with the added insurance that the beefier B100 is just around the corner. So why worry? Let’s buy the dip, say the bulls. But what if the tip of the spear in mass-market AI development is no longer chasing beefier GPUs and ever larger GPT-type foundational models?

For mass market AI app developers, the divergence between H100-GPT4 pricing and a path to profitability is too daunting, we believe. And so, they necessarily must walk from H100 for now and seek cheaper solutions. If we are right, then the supply-demand imbalance the Street infers from upstream checks could be closer to an end. And that will not help the bull thesis.

Mass-market AI applications, such as tech support chatbots and email summaries, need to seek out an alternative path. We believe there is intense development activity ongoing to fine-tune GPT4 class of LLMs in order to fit the vector database into NVDA’s older gen A100, which happily enough seems to be in surplus. Our checks show A100 servers running open source LLMs are less than a tenth of cost of GPT4-H100 in terms on $/token. And that is unbearably attractive to AI app developers. We believe the initial wave of H100 users has crested.

The print/guide provided today may not be quite as relevant as qualitative commentary on out-quarters. NVDA management is likely to provide the Street with just enough juice to model next year up y/y. Purchase commitment for Fy26 is likely to go up vs. the ~$1bn commitment disclosed a quarter ago. The Street appears to be modeling NVDA’s Fy26 Data Center revenue growth anywhere from up 20% to up 50% based on supply-chain checks. We think upstream supply-chain signals are not a reliable indicator of future growth when there are shifting trends downstream with AI developers.

We do not think NVDA is trading on fundamentals as expectations for next year seem unmoored from business realities downstream. We take our previous PT of $425 (link) from 3 months ago and index it up by the Sox up 17% to get to a new PT level of $500. We will need to see NVDA stock give up all its ytd gains before we get interested on the long side especially as, on a macro level, a whiff of inflation is making long-duration secular names such as NVDA incrementally less desirable to investors.

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Development costs are simply too crushing to all but a handful of hyperscale AI-service providers, in our view. Even at hyperscale AI-service providers, we doubt if their services/applications are close to profitability. Certainly not at Google GCP – external AI users have essentially been getting free access to Bard/Gemini. Microsoft’s Copilot priced at $30/month may not margin accretive, our industry checks show. But these giants have the financial muscle to take losses while they seed a new market. Smaller 3^rd party app developers do not have that luxury. So how are they going about it?

The more innovative app developers appear to be pivoting away from supply-constrained AI-hardware. The tip of the spear appears to be pointing away from expensive H100 servers and towards older generation A100, for which we think there is surplus supply. We think there is now an active secondary market for A100 servers. This has encouraged a new crop of relatively unknown boutique CSP to enter the AI-fray and act as price-spoilers to the CSP incumbents

We believe rental pricing of A100 servers has been dropping rapidly and is now a fraction of the H100 servers. This acts as a powerful motivator for app developers, who were previously dabbling with H100 but with limited success due to high pricing, to gravitate to the older generation A100. A race to the bottom in pricing – this has been ethos of tech innovations over the past 4-5 decades. This time is no different.

Who is the incremental buyer for the B100? When the beefier B100 hardware becomes available in a few months, if the H100 user base is already cresting, who will be the incremental commercial buyer for hardware that is more expensive than the H100? We find it hard to believe hyperscale CSPs could acquire the B-series with the same gusto they did the H-series servers.

There must be a reason Jensen has been, of late, cozying up to national leaders. Maybe it takes the backing of a national budget to fund the enormous outlays B100-based data centers may require. We note in passing that the estimated combined capex of the top 4 US-based CSPs sits just below the #2 national defense budget (China), and larger than the combined budget of the #3 and #4 nations (Russia, India). At some point of time, commercial CSPs need to disclose profit metrics; they have been silent on that front so far. Loading up on more capex does not help. We think Cy25 will be a year of capex digestion as the top 4 US players allow costs to run-off via depreciation.

A new phase: We believe the initial spurt of activity in Gen AI is fast maturing and is entering a new phase. The period of intense experimentation at all costs may be behind us. Our checks across the industry show mass-market application developers are moving away from experimentation to a new phase of discovering paths to profitability. This new phase, while just as exciting and innovative, may not be quite as fast-moving. And the path to profitability, may not run through NVDA’s most advanced GPUs

At the very high end of the spectrum of applications are the ones which hold the promise of dramatic productivity gains in the near term. These applications, such as GitHub CoPilot, could well be profitable on the existing high-end solution, i.e. GPT4 running on NVDA’s H100.

The H100 user wave may already have crested: However, proliferation of Gen AI applications beyond just the most lucrative end, we think necessarily requires innovation in lower cost hardware and open source LLMs. We think developers in mass market applications are moving away from NVDA and OpenAI’s cutting-edge solutions – they simply do not see a path to profitability. In our conversations across private developers, we think the wave of GPT4-H100 users may have crested. We think 3^rd party app developers have been moving away from the GPT4-H100 combination in the past few months.

Signs of cresting user base: 1) OpenAI is having to make drastic cuts to pricing every three months, 2) VC-funded startups on Y-Combinator platform (Altman’s alma mater), we hear are being encouraged to use OpenAI’s GPT-models instead of cheaper open source models, 3) Microsoft CoPilot, our checks show, is handing out free seats to app developers, 4) Google GCP AI appears to be in no hurry to move away from free access to external users.

In search of cheaper hardware: So where are mass-market AI app developers headed? To AMD? To proprietary solutions from hyperscale CSPs? No. We don’t think so. The non-NVDA solutions are far from the plug-and-play stage. These developers have no choice but to continue working within the NVDA family of products, for now. However, we think they may have discovered that a potential path to profitability runs through NVDA’s older gen product, the A100. And why is that?

A secondary market has emerged in A100: We think the A100/80GB is already trading on the secondary market, and with it, a drop in hardware cost and server rental pricing. Less than three years since launch, we think there is surplus of A100 in the market. Hourly rates for A100 servers at data centers have come down over the past year. A100 is offered at a fraction to H100’s hourly rates. Our checks show A100 servers running open source LLMs are less than a tenth of cost of GPT4-H100 in terms on $/token. And that is unbearably attractive to AI app developers. And so, many developers are taking their H100 models and are trying to optimize them to fit on A100 servers.

Price spoilers enter the AI DC market: The clearest signal of surplus A100 in the recent emergence of price-spoilers entering the AI data center market. Boutique DCs with sub-$billon annual revenue and annual capex of only ~$100mn have begun to enter the AI data center market with the explicit goal of poaching users from incumbents. Some of the outfits are highly profitable and prefer to stay that way after entering the AI market. We think they are managing to procure A100 servers at super low prices, thus ensuring continued profitability as they scale up their AI customer base. This is very good news for 3^rd party AI app developers.

The H100 too will eventually go into the secondary market perhaps sooner than the A100. As supply of H100 catches up with demand and as the initial wave of H100 users crest, we think the H100 too is likely to go into surplus supply. While it took ~2years for the A100 to go into surplus supply, we think the H100 may get there sooner due to the steeper increase upstream in H100 supply capacity vs. the A100.

Net/Net:

• While H100 may seem supply-constrained to the Street going purely by upstream supply chain checks, our downstream checks seem to indicate that the initial wave of H100 users may have crested as the pricing is too rich for most mass-market Gen AI applications
• Mass-market AI app developers need to 1) find innovative ways to take their GPT4-H100 models and adapt them onto A100 servers running LLMs smaller than GPT4, or 2) suspend all development until H100 supply goes into surplus.
• We expect H100 to go into surplus once hyperscale players such as Meta and Microsoft Azure run into excess capacity and start dumping servers into the secondary market. We think Microsoft Azure could be close to hitting excess capacity. Why else would they be signing up new users of Copilot for free?

We do not think NVDA is trading on fundamentals as expectations for DC growth next year seem unmoored from business realities downstream. We take our previous PT of $425 set 3 months ago (link) and index it up by Sox advance up 17% to get to a new PT level of $500. In other words, we will need to see NVDA stock give up all its ytd gains before we get interested on the long side.