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Tanay Jain argues that AI products operate across three layers—model…

2026-03-31 · 00:05 UTC ·tanayj ·5 min read

Brief

Tanay Jain’s March 31, 2026 post frames AI companies as converging on two forms of vertical integration rather than remaining pure application-layer vendors. In his simplified stack, models sit at the bottom, agents and application logic in the middle, and humans or services at the top for review and last-mile execution. One route is 'full stack down,' where companies internalize intelligence by tuning or training domain-specific models using proprietary interaction traces; he points to Cursor’s Composer 2, built from Kimi K2.5 with continued pretraining and RL on long-horizon coding tasks, and Intercom’s Fin Apex, which reportedly powers nearly all of its English-language support conversations. The other route is 'full stack up,' where firms own the workflow outcome by combining AI with services, as seen in Crosby AI, WithCoverage, Harper, and Mechanical Orchard. Jain’s broader thesis is that usage data, cost pressure, differentiation needs, and imperfect model reliability will push many AI startups to capture more of the stack over time.

Why it matters

Tanay Jain argues that AI products operate across three layers—model, application/agent, and human or service—and that most successful AI application companies will eventually vertically integrate beyond the middle application layer.

Key details

  • The 'full stack down' path involves application companies moving into the model layer using proprietary usage traces as training data; examples include Cursor’s Composer 2, launched in late March 2026 on top of Kimi K2.5 with continued pretraining and reinforcement learning for long-horizon coding tasks, and Intercom’s Fin Apex, which the company says now handles essentially all English-language chat and email customer conversations.
  • The post identifies three main reasons for downward integration into models: a performance flywheel from prompts, outputs, edits, acceptances, and rejections; lower COGS and faster inference from smaller fine-tuned models at scale; and stronger differentiation when competitors rely on the same foundation models.
  • The 'full stack up' path involves AI companies selling outcomes rather than software by adding human and service layers; cited examples include Crosby AI’s legal 'Neofirm' model, WithCoverage and Harper in AI-native insurance brokerage, and Mechanical Orchard in AI-driven software modernization services.
  • A core prediction is that AI-native services firms may eventually expand in both directions: they can start with application plus service to close the last-mile reliability gap, then later post-train specialized models and end up owning all three layers of the stack.
Cleaned source text

title: @tanayj: Over time, every AI application company will vertically integrate to become full...

author: tanayj

content_type: twitter_article

published: 2026-03-31T00:05:15+00:00

source_url: https://x.com/tanayj/status/2038769555745411300

word_count: 1029

Over time, every AI application company will vertically integrate to become full-stack. The question

Over time, every AI application company will vertically integrate to become full-stack. The question is in which direction?

I’ve been thinking about a pattern that we’re increasingly starting to see across AI application companies. Over time, I think most of them become “full-stack”.

At a high level, you can think about an AI product that achieves outcomes as having three layers:

At the bottom, the model

In the middle, the application or agent which includes the data/context, etc

At the top, the human or service layer needed to review/prompt/do the last mile to actually get to an outcome

This is obviously a simplified way to describe the world. Models sit on top of chips, etc and there’s a lot of infrastructure, data systems, orchestration, evals in building agents. But I still think the simplification is useful.

Traditional application layer companies would sit just in the middle layer. But these companies are increasingly beginning to (or starting off) vertically integrate in one of two directions. Some move down into the model layer. Others start or move up into the human or service layer. Both end up looking “full-stack” just in very different ways.

Yes, I realise that per the three layered stack, neither approach is technically “full-stack” but you get the point :)

1. Full stack down

The first version of full-stack is when the application company integrates down into the model layer.

This is the pattern we are seeing in coding, customer service and a few other categories where the company has enough usage, enough proprietary traces, and enough economic incentive to start pulling intelligence in-house.

Cursor is a very recent example. Last week it introduced Composer 2, positioning it as a frontier-level coding model with meaningfully improved benchmark performance and lower pricing than many alternatives. In its technical report, Cursor says Composer 2 uses Kimi K2.5 as a base model, then extends it with continued pretraining plus reinforcement learning on long-horizon coding tasks.

Intercom made a similar move this week with Fin Apex. Intercom CEO Eoghan McCabe described it as “the age of vertical models” and Intercom says Apex now powers essentially all of its English-language chat and email customer conversations.

Cursor, Intercom, Cognition, Harvey, Sierra, and are increasingly trying to shape, tune, route, and in some cases train the intelligence itself around a specific domain.

Why does this happen?

The biggest reason is that for AI companies, this is the most important flywheel they have to drive increased performance. They have the traces of the agents. They see the prompt, the outputs, the edits/acceptances/rejections. Over time, that becomes extremely valuable proprietary training data. Better product creates more usage. More usage creates more traces. More traces make the model better. Then the better model improves the product again.

Other reasons include:

Cost and Speed: Once you have enough scale, the COGS can add up, and smaller, fine-tuned models can end up giving you enough performance for your use case at much lower cost and at much faster speed.

Differentiation: If everyone in a category is calling the same handful of models, it becomes harder to build real product distance from the intelligence layer alone.

2. Full stack up

The second version of full-stack goes the other way. Instead of integrating into the model, the company integrates upward into the human or service layer. It sells true outcomes instead of software.

This is the more classical full-stack startup idea that Chris Dixon wrote about in 2015. You do not just sell software into a workflow. You own the end-to-end process and provide an outcome or service to the end customer.

Historically, that often meant ugly services economics and challenges with scaling. But AI changes the shape of that equation and opens up the possibility to take this route in many end markets that weren’t attractive.

This idea of “sell outcomes, not software”, “services-as-software” and "AI-native services" has been growing in popularity over the last few years, but as models increasingly get better at agentic tasks over longer horizons, we’re now starting to see the first wave of these companies truly break out.

There are a number of examples of these AI-native Services business across various services categories such as legal, insurance, accounting, customer support, recruiting, and IT modernization with the extreme version of these the “roll-up and layer on AI approaches”. On the de-novo AI-native services businesses side, we see companies such as:

Crosby AI combines software, AI, and attorneys to own more of the delivered result and outcome in what co-founder and CEO Ryan calls a “Neofirm”.

WithCoverage and Harper building AI-native insurance brokerages

Mechanical Orchard is an AI-native software modernization services firm

AI is great but it isn’t 100% there, and a company that owns the end result can both plug the gaps in all the missing places is both incentivized to contuosly improve and allows the customer to not have to think about the last mile.

This path is especially interesting because it may eventually absorb even more of the stack. What starts as application plus service can, over time likely post-train specialized models too. As AI does more of the work, some of these companies may end up owning two layers at first, then increasingly all three.

3. Closing thoughts

I think AI application companies will not tend to stay just AI application companies. Over time, they will vertically integrate. Many will use their traces to own the intelligence as well to increase differentiation, performance and reduce costs, particularly given that the model companies themselves are encroaching on some of their applications. The others will either start out delivering full outcomes or over time as they can do an increasing amount of the work, decide they can capture more value by selling the full service rather than an agent that does 90% of it.

For more related to this topic, here are some good pieces:

Ryan Daniels (Crosby AI) on the

Rise of the Neofirm

Cursor’s

Composer 2 Technical Report

Eoghan McCabe (Intercom) on the

Age of Verticalized Models

Chris Dixon’s original piece on

Full-stack Startups

Posted: 2026-03-31T00:05:15.000Z

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