The business of AI is facing 4 harsh realities

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The business of AI is facing 4 harsh realities

Investors are starting to realize that things may not be as rosy as they wanted.

TL;DR

CEOs and Microsoft acknowledge AI is too expensive.
A new Bain study indicates AI is not yielding expected returns.
Broadcom's weak forecast suggests infrastructure demand is lower than optimistic predictions.
Rising interest rates will make financing AI infrastructure more expensive.
These realities challenge assumptions that drove recent market highs.
The market sold off Friday, with the Nasdaq experiencing its worst day in 14 months.
The S&P 500's decline, despite most stocks rising, mirrors a moment during the dot-com bubble's collapse.
Outstanding growth may not be enough if expectations are overly stretched.
The SpaceX IPO and Asian stock market performance will provide further signals.

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clem 🤗 reposted

Tommy

The most basic way AI could blow up imo. I'm not saying it does but this is the most obvious way I can see it happening

- Per seat subscriptions are massively subsidized. The flat fee was priced way below what heavy usage actually costs

- For real business use you have to move to the API anyway. Data protections, work integrations and compliance officer approval

- On the API you pay metered rates, and businesses are burning credits way faster than the per seat pricing ever led them to expect

- This is everywhere right now. Internally for us, Codex users, Uber torching its entire 2026 AI budget in 4 months, the Microsoft comments. Just go try an API

I shared more on this here: https://t.co/iZrqrCAIRW

- And I don't think most businesses have the money to keep paying increasing API rates without a real change to how they operate (caps needed)

- Because they have a cheap alternative. They can reach open source models through any aggregator (OpenRouter, Venice, Baseten, Together) and still get strong privacy. Venice private data centers, or E2EE/TEE serving GLM 5.1.

More on open source inference provider raises here: https://t.co/7kf56P44yQ

- And the discount is enormous. DeepSeek V4 codes within a hair of Opus on SWE bench at roughly 1/30th the price, and the cheapest open models run closer to 1/100th

- Chinese labs open source frontier grade models. The model is the single biggest cost an inference provider has, and they get it for free

- This idea dies if China goes closed source. That is actually bullish web2 AI labs, because if everyone is closed you pay up for the best intelligence. China goes closed source if they are tired of giving away an asset and they want the revenue and data flow to train new models

- Is this showing up in web2 AI lab revenue yet? No. Revenue is off the charts. Anthropic went from 9B to 47B run rate in five months

- So go forward, what happens?

- I think revenue slowly starts leaking to the open source inference providers (see Venice usage, OpenRouter's $113M raise, Baseten is raising at $11B or triple its valuation in three months, on revenue that went from $200M to $600M annualized in a single quarter)

- It doesnt move overnight, but it caps the labs ability to raise prices, and margins are already deeply negative. OpenAI is reportedly running near negative 122%

- With margins that bad there is no cash flow, so the labs are fully dependent on outside capital to buy GPUs, train models, and keep subsidizing usage (I.e. see Google tapping $80b equity sale, granted 30b for employee RSU taxes. Clearly they think Equity is overvalued or you wouldn't sell it)

- The break comes when that capital stops. Pricing is capped so margins cant improve, and the moment investors lose conviction on payback, the whole flow reverses

- Why would they lose conviction on payback? Back to the start - the inability to improve margins or get businesses to pay more

- This is also limiting, if we start making new drugs with AI or create entirely new businesses, you better believe people will pay up to the max for AI usage

Aravind Srinivas reposted

Harry Stebbings

This show with @AravSrinivas is one of the best we have done.

- The biggest problem today is power.

- We will see large resistance to data centre buildout continue.

- Micron will be worth more than Meta.

- Export controls have helped China.

- Monitoring token budgets is BS and only for losing companies.

How is that for spice? I have added my notes from the discussion below but such a special show to do with Aravind in London.

1. Are Agents the End of the Internet Advertising Model?

AI agents will disrupt industries built around objective transactions, but subjective spaces like travel, fashion, and shopping are more resilient. When intent is driven by vibes, exploration, and aesthetics rather than one correct answer, chat interfaces struggle to replace browsing, keeping much of the advertising model intact.

2. Biggest Takeaway From Elon Musk?

An elite entrepreneur can identify the single limiting bottleneck in a business and focus on it with unusual intensity. True concentration requires discipline: you must ignore even important issues when they distract from the immediate objective that matters most.

3. Did Export Controls Hurt or Help US Competition With China?

Export controls gave America a short-term edge by preserving a capability gap between open-source and frontier models. But they may have also forced China to become a stronger physical competitor by pushing its tech ecosystem toward memory-efficient architectures and deeper vertical integration.

4. How Will the Best Organizations Approach Token Budgeting?

Enterprises will stop manually tracking model capabilities or micromanaging token budgets across teams. As model optimization accelerates, the best companies will outsource this complexity to AI orchestrators that automatically route tasks to the right models at the right cost.

5. How Can America Retain Competitiveness in AI?

The West must build physical infrastructure aggressively, streamline power procurement, and counter alarmist narratives around data centers and job displacement. Leaders should explain the upside clearly: AI can let tiny teams build billion-dollar companies and drive major new GDP growth.

6. What Is the Single Most Important Metric in AI?

The key metric is token value per watt per user. Raw model building and fine-tuning are becoming commoditized. The real venture-scale value will accrue to orchestration layers and agent harnesses that deliver high-quality outputs while using minimal data center power.

7. Why Is Moving Fast the Ultimate Expression of Humility?

Founders often waste early cycles searching for a permanent moat, when speed is their only real defense. Moving fast is an act of humility because it forces constant contact with the market, repeated assumption testing, and a willingness to let reality overrule ego.

(links below)

Yann LeCun reposted

Derek Thompson

New newsletter: THE FOUR HORSEMEN OF THE AI BUBBLE APOCALYPSE

I am neither anti-AI nor certain that AI is a bubble. But the last four weeks have made clear that the AI buildout now faces a very clear quadruple-headed risk hydra.

1. A spending risk, as the hyperscalers run low on cash and take on $170b in annual debt—which is more than the projected UK deficit.

2. A revenue risk, as open-weight models threaten to compress the margins of frontier labs ... and as AI become the sort of internationally competitive asset-heavy industry requiring stable and determined long-term policy consistency, which is arguably China's competitive advantage.

3. A political risk, as anti-AI populism becomes one of the easiest applause lines, even as AI becomes a more and more foundational pillar of US economic growth

4. A technological risk, as the frontier labs bear down on RSI, which I think could significantly change the basic business model of the labs, as compute costs rise and rise for a set of super-advanced models that are fit for, and affordable to, a small minority of users (in, eg, cyber security)

In one sentence: The capabilities of AI are becoming more powerful, while some economic underpinnings of the AI buildout—and, as we’ll discuss, the political support for AI—are becoming more vulnerable.

Today's (long, 5k word) piece deeply considers each risk and also—because over-confidence in this space is typically a sign that you're not thinking hard enough—I offer the strongest reason to think each risk might be overblown.

https://t.co/iHpP2e5zEV