CEOs are bargain hunting for AI

tech

CEOs are bargain hunting for AI

As AI labs hit record valuations, their customers are searching for cheaper alternatives.

TL;DR

Corporations are looking for cheaper AI models as usage increases IT costs.
High valuations of AI labs like Anthropic and OpenAI may be threatened by this cost-saving trend.
Companies are monitoring AI usage and switching to more economical models.
There is a reluctance among clients to standardize on a single AI vendor due to fears of future price gouging.
The use of open models has significantly increased compared to closed, more expensive models.
Switching AI platforms can be challenging despite high costs.
Enterprise revenue for AI labs continues to grow, even as customers seek alternatives.

Continue reading the original article

clem 🤗 reposted

The Transcript

Sam Altman: AI spend intensity has become a major customer concern almost overnight

"Probably the second biggest theme is just around cost. People are really saying, that's kind of become a meme now, but 'my company spent my entire 2026 budget in Q1. Can you make this more efficient?'...that went from, at the beginning of this year, an issue that never came up—I know people were totally happy with the amount they were spending—to all of a sudden a huge issue"

clem 🤗 reposted

Polymarket

JUST IN: Sam Altman says AI budgeting has suddenly become a “huge issue” for companies.

clem 🤗 reposted

Chamath Palihapitiya

Your margin is my opportunity: AI version…

The biggest surprise of 2026 is that the capability gap between the best open-weight/source models and the best closed models has narrowed much faster than the pricing gap. The pricing gap remains enormous while the capability gap is quite narrow.

What does this means in practice?

For a company consuming 1 billion input tokens and 1 billion output tokens per month:

GPT-5.5 Pro: ~$105,000

Claude Opus 4.8: ~$30,000

DeepSeek V4 Pro: ~$5,220

DeepSeek R1: ~$2,740

I asked ChatGPT what it thought about this and it answered as follows:

“If I were building a company today, the economic frontier would look roughly like:

DeepSeek V4 Pro / R1 for high-volume inference.

Claude Opus for premium agent workflows where reliability matters.

GPT-5.5 Pro only for workloads where its incremental capability demonstrably produces enough business value to justify a 20–40× token premium.”

Most CEOs have no idea that, instead of this nuanced approach, their teams are running amok internally by picking the most expensive models in most cases and burning through massive budgets with zero governance, audit ability and control.

As control planes like our Software Factory become more standard, you can expect the run rate revenue growth of the frontier labs to go down meaningfully and the revenues of the open models to skyrocket.

Why? Because we can implement the nuanced approach above and be agnostic to model - instead focusing on customer intent, model task and cost management among other things.

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

clem 🤗 reposted

Eric Newcomer

Soaring Costs Prompt Fresh Interest in Open Source AI. Chinese Firms Are Way Ahead. https://t.co/EufHdg5xN2

clem 🤗 reposted

Itamar Golan 🤓

Hot take:

GLM 5.2 might be the first open/public model that actually changes the enterprise AI cost equation.

I played with it for a few hours today after friends told me to stop ignoring it.

I expected the usual open model experience:

“Cool, but clearly not frontier.”

That’s not what I felt.

Across a bunch of different tasks, it was the first public open model that didn’t feel obviously worse than the best closed models. Not perfect. Not fully benchmarked. But shockingly capable.

The first open model that made me think:

“Wait, this could actually replace a meaningful chunk of frontier model usage.”

That matters because the economics are wild.

Yes, serving it properly is expensive. Something like 8 Nvidia H200s, around $400K to buy or $20K/month to rent.

But many enterprises are spending millions per month on Anthropic/OpenAI.

If GLM 5.2 is even close enough for 30-50% of workloads, that’s not a model launch.

That’s a market disruption.

clem 🤗 reposted

Rohan Paul

UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models

The pressure is coming from extreme bills, including users spending up to $35K/month, teams exceeding quotas by 200%, and companies cutting internal AI tools from 5 to 2.

Companies are not abandoning AI, they are using model routing, which sends easy tasks to cheaper models and saves premium models for hard reasoning, code, and long-context work.

Chinese open-source models such as Qwen, DeepSeek, MiniMax, GLM, and Kimi now fit the enterprise cost curve because they can be run locally or used through cloud catalogs.

---

news .futunn.com/en/post/75068082/ubs-group-finds-60-have-already-started-curbing-ai-spending?level=2&data_ticket=1780870170397383

Greg Brockman

sounds accurate