# Enterprise AI adoption follows inference spending

Markdown copy of https://realalpha.app/s/enterprise-ai-adoption-follows-inference-spending/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Brendan Foody](https://realalpha.app/builders/brendan-foody/) · Brendan Foody is co-founder and CEO of Mercor. · Co-founded Mercor, a labor marketplace supplying AI companies with expert data, paying tens of millions weekly.
- Host: [Molly O'Shea](https://realalpha.app/people/molly-oshea/) · Founder, Sourcery · Ex-Upfront Ventures investor; has interviewed Alex Karp and Palmer Luckey.
- Show: Sourcery
- Episode: [Mercor Raises $750M at $20B Valuation | CEO Brendan Foody](https://realalpha.app/v/xzA6lYBzmzQ/), Nov 10, 2026
- Moment: 43:35 to 49:59 of the episode (6 min), playing on https://realalpha.app/s/enterprise-ai-adoption-follows-inference-spending/; the episode from there: https://realalpha.app/v/xzA6lYBzmzQ/?t=2615

## Quote

> “Once they're past $**100 million in inference**, like a Harvey or a Cursor or all of these companies, then they need to be really seriously thinking about how do they post-train models”
> — [Brendan Foody](https://realalpha.app/builders/brendan-foody/) at [47:10](https://realalpha.app/v/xzA6lYBzmzQ/?t=2830) · Sourcery

## Takeaways

1. Under $10M in inference, many companies are still figuring things out.
2. At $10M–$100M, allocate roughly 10% of inference spend to evals.
3. Above $100M, post-training can make economic sense.

## About this segment

Brendan Foody predicts Fortune 500 companies will build their own models and explains how inference spend shapes adoption. His thresholds give operators a way to plan for evals and post-training.

From “Mercor Raises $750M at $20B Valuation | CEO Brendan Foody”, Nov 10, 2026. [Watch the full episode](https://realalpha.app/v/xzA6lYBzmzQ/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[43:41](https://realalpha.app/v/xzA6lYBzmzQ/?t=2621) So how does this play out over the next 10 years? I think that the key development is that every Fortune 500 is going to look like a Neolab, where they have their core data sets, their core distribution that they care about. to serve their customers, and they need to mobilize some combination of their full-time employees and contractors to curate really high-quality tasks for evaluation and training of the models that they're building. Because ultimately, so many of these processes that are currently human are going to become agentic, and this is the most efficient and scalable way to distill all of that human knowledge throughout their company into the models. And so that will be a giant transformation that we're just starting the first inning of.

[44:36](https://realalpha.app/v/xzA6lYBzmzQ/?t=2676) Damn, I was going to ask you that. So I actually, I asked Winston for some questions ahead of time, and he wanted me to ask about AI data sovereignty. It's going to be so critical. I mean, there's a reason that Jensen and Alex Karp and Satya are all talking about this. Like, I think that Anthropic and OpenAI will have extraordinary businesses. I think both of them could very well be 10 trillion plus dollar companies.

[45:04](https://realalpha.app/v/xzA6lYBzmzQ/?t=2704) But ultimately, we don't want them to subsume the global economy and be the only place that you can go to access frontier intelligence. And for value to accrue to every other company in the Fortune 500 and Americans more generally, we need to make sure that all of these companies are able to train their own models and build their own intelligence that codifies their competitive advantage. I think there's sometimes this narrative from certain labs when they're fundraising that all of a sudden they're just going to turn on training on the usage data flywheel of all of the enterprise data. The Fortune 500s are going to be fine with this and this is the way that they're going to like collect all the environments throughout the economy when clearly that's not the case right clearly the reason that a company like Goldman Sachs exists is that they

[45:58](https://realalpha.app/v/xzA6lYBzmzQ/?t=2758) have a set of competitive advantages associated with the people the processes the documents etc in their business and turning on allowing a frontier lab to train on that data would be analogous to just sharing all of it with a competitor and so they're not going to do that And I believe that instead they're going to be building their own models, deploying their own agents that codify all of this knowledge.

[46:25](https://realalpha.app/v/xzA6lYBzmzQ/?t=2785) And really the heuristic to look at for what is the catalyst of this transition and happening within the enterprise is how much they're spending on inference. Because what we basically find is that when a company is spending under $10 million on inference, they don't generally care that much about evals or how to optimize it or whatever. They're still figuring things out. Once they go from the $10 to $100 million on inference range, then they generally want to spend...

[46:54](https://realalpha.app/v/xzA6lYBzmzQ/?t=2814) That is such a wide gap. It's a wide gap, but we're spending almost a million dollars a day on inference. So like some companies scale fast. And once they're in the 10 to 100, then it's roughly 10% of their inference budget should be going to eval of how do they build out the data sets that tell them like what are all the models they should route this to. Once they're past $100 million in inference, like a Harvey or a Cursor or all of these companies, then they need to be really seriously thinking about how do they post-train models because the economics start to check out.

[47:32](https://realalpha.app/v/xzA6lYBzmzQ/?t=2852) If you can spend tens of millions of dollars a year, sometimes hundreds or billions in the case of Cursor, in training your own models because you can amortize that fixed cost across all of the inference that you have. And so I expect the inference spend of enterprises will ramp materially, but they'll also go through these curves of adoption where they roll out evals in that 10 to 100 range and then start post-training in the 100 plus range.

[48:00](https://realalpha.app/v/xzA6lYBzmzQ/?t=2880) As they deploy frontier intelligence throughout their businesses. Because you have such a unique and advantageous perspective, I mean, you see literally every company and you see how their data is being used and all this kind of stuff. What advice do you have, taking into account everything that you just mentioned on inference, what advice do you have to these enterprises as they continue to build up their sovereign AI stack?

[48:24](https://realalpha.app/v/xzA6lYBzmzQ/?t=2904) I would say that the largest thing that enterprises misinterpret is how frontier models actually learn, where the vast majority of where the compute gets spent on in trading is no longer just like read all the documents of the company or learn from inputs and outputs and SFT. Like most of the compute is now going towards RL, where the model will attempt the problem many times and then get a score on one of these rubrics.

[49:00](https://realalpha.app/v/xzA6lYBzmzQ/?t=2940) And RL in the vast majority of cases requires subject matter experts that are able to create these rubrics and tasks to evaluate and train models. And so for any company that is training their own model, they need to build tasks that produce clear eval scores. And then once they feel confident in the eval, then they should scale up the distribution of what they care about to hill climb that eval. So maybe those are the two pieces of advice is, first of all, build tasks rather than just like pretending the model can read everything and learn.

[49:39](https://realalpha.app/v/xzA6lYBzmzQ/?t=2979) And second of all, that they should start with the eval because that is the foundation for how every AI lab and successful application layer understands how well they're doing and has a baseline in improving their models of their training.