Sequoia's thesis that the next $1T company will sell work, not software, is the most important reframe in AI right now.
The argument: if you sell a copilot, you're competing with every new model release. But if you sell the outcome — books closed, contracts reviewed, claims handled — every AI improvement makes your margins better, not your product obsolete.
The key insight most people miss: for every $1 spent on software, ~$6 is spent on services.
The entire SaaS playbook was about capturing the software dollar. The AI playbook is about capturing the services dollar — at software margins.
Not "AI for accountants." The AI accounting firm.
Not "AI for lawyers." The AI law firm.
The companies that figure this out won't look like SaaS companies. They'll look like services firms rebuilt on software infrastructure.
That's a fundamentally different company to build, fund, and scale. And most founders are still building copilots.
The more enterprises I talk to about AI agent transformation, the more it’s clear that there is going to be a new type of role in most enterprises going forward. The job is to be the agent deployer and manager in teams. Here’s the rough JD:
This person will need to figure out what are the highest leverage set of workflows on a team are (either existing or new ones) where agents can actually drive significantly more value for the team and company.
In general, it’s going to be in areas where if you threw compute (in the form of agents) at a task you could either execute it 100X faster or do it 100X more times than before. Examples would be processing orders of magnitude more leads to hand them off to reps with extra customer signal, automating a contracting review and intake process, streamlining a client onboarding process to reduce as many straps as possible, setting up knowledge bases than the whole company taps into, and so on.
This person’s job is to figure out what the future state workflow needs to look like to drive this new form of automation, and how to connect up the various existing or new systems in such a way that this can be fulfilled. The gnarly part of the work is mapping structured and unstructured data flows, figuring out the ideal workflow, getting the agent the context it needs to do the work properly, figuring out where the human interfaces with the agent and at what steps, manages evals and reviews after any major model or data change, and runs and manages the agents on an ongoing basis tracking KPIs, and so on.
The person must be good at mapping the process and understanding where the value could be unlocked and be relatively technical, and has full autonomy to connect up business systems and drive automation. This means they’re comfortable with skills, MCP, CLIs, and so on, and the company believes it’s safe for them to do so. But also great operationally and at business.
It may be an existing person repositioned, or a totally net new person in the company. There will likely need to be one or more of these people on every team, so it’s not a centralized role per se. It may rile up into IT or an AI team, or live in the function and just have checkpoints with a central function.
This would also be a fantastic job for next gen hires who are leaning into AI, and are technical, to be able to go into. And for anyone concerned about engineers in the future, this will be an obvious area for these skills as well.
@trq212 I've build https://t.co/hjBhfS87Xc on top of this new channels framework, its working pretty well
allows for internal agent to agent communication, throw in an agent that can also talk to telegram and you got yourself a network!
CDD (Context Driven Development) is working very well for me, this repo is a small showcase for it.
Want to shoutout a few people who's ideas I've run with:
@mattpocockuk - just about everything he posts about is awesome and I've used a bunch of concepts he's introduced me to
@jamonholmgren - his nightshift post hit home in a big way, its even a skill now in CDD
@saasmakermac - all round ralph fun, among other things
lots more, but super happy about all the information sharing here on X and wanted to throw something into the mix, however small it may be
@adityaag articulates that "oh wow I can build things again" feeling better than anything I've read before. The shift is amazing and truly life changing for those of us who went down the CTO/VP route and are now back to building
Every Fortune 500 executive is buying AI subscriptions and calling it a strategy.
Palantir CEO Alex Karp has a word for that.
Karp: “The general approach of just buying models is going to be essentially self-pleasuring for an enterprise at the cost of the enterprise.”
Karp: “You buy some large language model, you party with it basically, and the next day you have a hangover.”
The entire corporate world is mispricing the AI transition.
They are renting intelligence with no foundation to run it on.
A raw model floating in a vacuum hallucinates over your unstructured data, generates the illusion of work, and executes nothing.
The party ends. The hangover begins. Nothing changed.
Karp identified exactly where the value actually goes.
Karp: “All the value in the market is going to go to chips and what we call ontology.”
Not the models. Not the subscriptions. Not the chatbot interfaces layered on top of them.
The ontology.
The precise digital architecture of how an organization actually operates. Its security permissions. Its supply chain physics. It’s operational logic.
Karp: “The ontology will allow you to take a large language model and use it, refine it, and then impose it on your enterprise in the logic of your enterprise, in the security model of your enterprise.”
When you bind a frontier model to the strict underlying logic of a specific enterprise, something fundamental shifts.
It stops generating text.
It starts generating action.
Karp: “We’re using it on the battlefield, we’re using it to compress margins. We’re making engineers better engineers. We’re making people who are not engineers into engineers using our ontology and a large language model.”
The traditional engineering bottleneck does not slow down.
It disappears.
Karp: “We are sitting on the only thing that actually creates quantifiable, transformational value.”
The companies renting models are paying for the feeling of transformation.
The companies building ontologies are executing the actual thing.
One of them will define the next decade.
The other will wake up in 2030 wondering where their market share went.
Exactly like a hangover.