Winners and losers in the coming AI margin collapse (part 2) - Martin Alderson

This is the second article in a two part series focusing on what I believe is perhaps the least understood upcoming shift in AI economics. If you haven't read it yet, I'd recommend starting with part one. As always, if you enjoy my writing I'd love it if you subscribe to my newsletter or RSS feed.

As always a week is a long time in AI. In the previous article I discussed the impact of "good enough" models for many agentic workflows - focusing on GLM5.2. In the brief spell of time since I wrote that, Grok 4.5 was released with similar capabilities and is also aggressively priced, which strongly hints at a glut of similar quality models coming out.

Your margin is my opportunity

This is one of Bezos's most famous quotes, and for good reason. It illustrates the dynamic in highly competitive markets - any margin becomes a weakness that others can exploit.

I think Grok 4.5's aggressive pricing - at $6/MTok output, it's being offered at a similar cost to hosted GLM5.2 - shows this up. While xAI is unlikely to beat OpenAI or Anthropic at the very frontier of intelligence, it shows exactly where they can get some traction - price.

It's going to be telling to see what happens to pricing over the next few months. It feels like the market is bifurcating into two - expensive very high end models (Fable, and perhaps GPT5.6 Sol) - and then a broad swath of good (~Opus), cheap models. While this has always been the case - a lag between the frontier and everyone else - I strongly believe the dynamic has switched a bit with these models now becoming good enough for many agentic tasks.

The winners

There is no doubt to me the real winners in all this are semiconductor companies and the entire downstream supply chain to LLM inference. Memory, GPUs, datacentres and the power and cooling needed continue to be severely supply constrained. And with cheaper models, microeconomics tells us that demand increases. But my guess is that the value increasingly accrues to the hardware layer of the value chain, not the software layer.

This isn't what we've typically seen in tech, and is a significant adjustment for many working or analysing the space. Typically hardware was viewed as the ugly duckling value wise - with punishingly low margins and poor ability for suppliers to differentiate products in the market for the most part. Software would sit on top and take all the margin.

Now, by no means am I suggesting that hardware will take all the value. But compared to previous technology waves, perhaps with the limited exception of Apple and the iPhone's cash generation ability[1], this has very much been the exception to the rule.

Apart from the hardware supply chain itself, there are opportunities for the hyperscalers/neoclouds and hosted inference providers to take some value from serving these lower cost models. Serving models like this at serious scale is still difficult, and proprietary efficiency improvements these companies can come up with will give them a competitive advantage. And the quality of these companies relationships with the underlying hardware providers do give them an edge, at least until/if supply starts to catch up with demand.

The most interesting case here is the coding agents - the Cursors of the world. For a long time they faced a brutal path forward: they were reselling frontier inference bought at close to retail API prices, which left them with wafer-thin (or outright negative) margins on their heaviest users. "Good enough" cheap models flip that overnight. A coding agent can now offer something 90% of the way to Opus on a model costing a fraction of the price - and actually make money doing it. But the bigger prize is the data they sit on top of: a firehose of real-world agentic usage - which prompts work, which edits developers accept or throw away, and exactly where the model gets stuck. That is the kind of signal a model provider would kill for to train the next generation. It's no wonder xAI bought Cursor - not for the IDE, but for the cheap-model economics and the analytics flywheel underneath it.

But, I think the real winners out of all this are the users and consumers of LLM inference. Being able to access such high quality intelligence for such a relatively low price is hugely exciting. Back when inference APIs were in their infancy, it looked quite possible that there might be just OpenAI providing inference to any reasonable quality, now we have a multitude of models with substantially better intelligence than GPT4 available for 5-10% the price of that model.

The losers

This is where it gets tricky. You're probably expecting for me to say the frontier AI labs, but I'm extremely torn on this.

Predicting AI market dynamics is tough. On one hand, I do believe that there are suddenly a large chunk of AI use cases that can be moved over to open/cheaper models with little to no loss of quality. This is no doubt...