The future belongs to differentiated intelligence.
Foundation models are becoming a commodity. What creates enduring value is not the base model. It is how your organization teaches it to think, decide, and act in the context of your own business.
I help startups and enterprises build that intelligence, and keep it inside their own trust boundary. Advisory, hands-on build, and a diagnostic that tells you where your judgment actually lives.
Anub Sinha · engineering leader, builder at Opscale and LegalStreet, author of the Applied series.
The argument
Three beats
The strategy question for the next few years is not which frontier model you use. It is what the one model only you could have built is, and whether you get to keep it.
01
Rented intelligence is not an edge
If my competitors and I are renting the same intelligence, then none of us has a durable edge. We are all standing on the same floor, paying the same rent, arriving at the same answers. The frontier model is table stakes. It was never the moat.
02
You cannot prompt taste
Real judgment comes from experience, and experts struggle to articulate it even to other humans. A prompt can describe judgment. It cannot transfer it. To get judgment into a model you have to train it in, on examples only your people could have labelled.
03
Your corrections are the asset
Every trace, trajectory, and correction your experts produce is the raw material of differentiated intelligence. If it flows out to the provider, you are paying to build an asset your competitors will rent back from them later. That is the leak worth closing.
Renting
Your experts do the hard work. The learning leaves with the provider, and reaches everyone who pays the same subscription.
Owning
The same work, kept. Every correction compounds into a model that gets sharper at your business, and nobody else's.
This is not a forecast
The evidence
Bridgewater and Thinking Machines took a task investment professionals do every day, and tested whether a tuned open model could beat the frontier at it.
84.7%Accuracy from a fine-tuned open model, against 78.2% from the best frontier model with expert prompting.
~30%Fewer mistakes than the frontier model. The difference between below the trust bar and above it.
~14×Cheaper to run at inference, because the specialist model is smaller than the generalist.
Out of the box, frontier models scored around 50% on the task. Expert prompt engineering pushed them to roughly 78%, still under the 80% bar a professional needs before they will trust the output. The win did not come from prompting harder. It came from training judgment in, using expert-labelled data the firm already had.
That is the whole thesis in one experiment. Smaller model, your data, your taste, beating the giant that serves everybody.
“Outperforming the market is hard. When every investor has access to the same sources of public information, alpha must come from unique insight built on taste and judgment.”
Thinking Machines × Bridgewater. The same logic holds for every industry that has just been handed the same API.
How I work with you
Four engagements
Most engagements begin with the diagnostic, because the expensive mistakes happen before anybody trains anything.
Intelligence Diagnostic
Start here
Two to three weeks, working with your leadership and the people who actually make the calls. We map where proprietary judgment gets created in your business, what is currently leaving your trust boundary, which of it is genuinely defensible, and what is realistically tunable today. You finish with a clear picture and a ranked plan, whether or not you keep working with me.
A map of where judgment is made, and by whom
An audit of what leaves your boundary today, including vendor terms
A ranked shortlist of candidate tasks worth tuning for
Honest calls on what you should keep renting
You get: a written diagnostic, a ranked roadmap, and a decision on build versus rent that survives scrutiny from your board.
Advisory
Ongoing
A standing relationship with founders, CTOs, and heads of AI. The questions that do not fit into a project: build versus rent, architecture, what to keep in house, how to structure the data and the team, what to insist on in a vendor contract.
Regular sessions with the leadership team
Review of architecture and vendor terms
On call for the decisions that are hard to reverse
You get: a senior second opinion before the costly commitments, not after.
Hands-on build
Project
The work itself, with your engineers rather than around them. Instrumenting the traces, standing up the expert labelling loop, fine-tuning and evaluating against your experts' bar, and deploying it where your data already lives.
Data capture and expert labelling pipelines
Fine-tuning, distillation, and evaluation harnesses
Deployment inside your trust boundary
Handover your team can actually run
You get: a working model, the loop that keeps improving it, and a team who can operate both.
Workshops & talks
Half or full day
For leadership teams and boards who need to reach the same understanding at the same time. The argument, the evidence, and a working session applying it to your own business rather than a generic case study.
Executive briefing on differentiated intelligence
Working session on your own candidate tasks
Conference and offsite keynotes
You get: a leadership team that stops arguing past each other about AI strategy.
The compounding loop
The method
Differentiated intelligence is not a project you finish. It is a loop you close, and then keep turning.
01
Locate
Find where proprietary judgment actually gets made. It is rarely where the org chart says it is, and it is never in the parts already easy to automate.
02
Capture
Instrument it. Traces, corrections, and the reasoning behind the calls become a dataset instead of evaporating into chat logs you do not own.
03
Train
Tune a model on it and hold it to your experts' bar, not a public benchmark. The bar is whatever score makes your people trust the output.
04
Compound
Feed every correction back inside the boundary. The gap between you and anyone renting the same base model widens each quarter you keep turning it.
The loop only compounds if it closes inside your boundary. If step four leaks, you are running a very expensive training program for the whole market.
Whether this is for you
Plainly
I take a small number of engagements at a time, so it is worth being direct about where this works and where it does not.
A good fit
You have proprietary data, or experts making judgment calls that are hard to write down
There is a real decision in your business that AI keeps getting almost right
You are at enough scale that a few points of accuracy change the economics
You are uneasy about what your vendor terms let providers learn from you
You want your team to own the capability afterwards, not depend on me
Not a good fit
You want AI added to the product without a specific decision it should improve
There is no proprietary data and no expert bench to learn from
You are looking for a thin wrapper over an API, shipped this month
The goal is a demo for a fundraise rather than a capability that lasts
Nobody senior is willing to spend time on the labelling and the standard
Who you would be working with
I am Anub Sinha. I build software and write about the ideas underneath it. I am currently building at Opscale and LegalStreet, and I write the Applied series, two online books that take one idea at a time and chase it from first principle to the machine it ends up inside.
That is the same instinct I bring to this work. Most AI strategy advice stops at the level of the slide. The useful part is one layer down: what the data actually looks like, where judgment actually gets made, what a training run actually costs, and which of it your team can actually run once I leave.
I have been making the case for differentiated intelligence since well before it had evidence behind it. Now it has evidence.
What is the one model only you could build?
If you can answer that, you probably do not need me. If the question makes you uncomfortable, that is the conversation worth having.