The role AI just made possible: five quant functions, merged into one person.
A paid four week accelerator that teaches a newly defined role: one person operating as quant researcher, analyst, developer, trader and strategist at once, using AI to hold the mechanical work while personally holding the judgment across the full lifecycle of a trading or research idea.
The curriculum is revised several times a year as the role moves. Enrol once and you keep every future revision: your access does not expire, and neither does the material.
Formulates the question.
Tests it against data.
Builds it into working code.
Executes it.
Decides whether it fits the broader portfolio.
Every handoff between those people is a place where context gets lost. AI collapses the need for five separate people: it does not collapse the need for the judgment that used to travel between them.
A Quant Merger is the person who holds that judgment continuously, across the whole lifecycle, using AI to execute the mechanics underneath.
It is not a quant who uses AI tools. It is a restructured way of thinking: a specific discipline about what to delegate, what to never delegate, and how to stay accountable for a model's real world behaviour rather than its backtest.
The framework at the centre of the program. Three tests a task has to pass before AI is allowed to own it.
If it goes wrong, can you undo it. Delegate freely where the cost of being wrong is a rerun, never where it is a filled order.
Can you check the output in the time you actually have. Work you cannot verify before you need it is work you have not delegated: you have simply stopped looking.
Someone has to be able to defend every assumption in the model. That someone is you, whatever wrote the code.
MIT Media Lab researchers gave the failure mode a name: cognitive debt. Measured with EEG, participants who wrote with an LLM showed the weakest neural connectivity of any group, recalled little of what they had produced, and reported a diminished sense of ownership over it. The effect persisted once the tool was taken away.
In quant work that debt has a price attached. A model you did not think through is a position you cannot defend. The three gates exist so the leverage compounds and the judgment does not atrophy.
Read the MIT study↗How AI is restructuring quant work at a structural level. The three gate framework for what to delegate and what to never delegate: reversibility, time gated verifiability, model ownership. How to think alongside a model without letting it think for you, and the Quant Merger role explained plainly, diagrammed, and shown through a real project carried end to end.
Agentic tools, connectors and skills as a working pattern rather than a product tutorial: how to arrange them around research, analysis and execution so the workflow holds together when the tools change.
Real project examples of the output: strategies, backtests, research notes, code, and the full path from idea to deployed product. The standard your own work is measured against.
The module people kept asking for: where quantitative method meets crypto market structure, taught from real DeFi and options protocol work rather than as a survey of the space. Same maths, different plumbing, and the plumbing is where the money is lost.
Constant product and concentrated liquidity, impermanent loss as a short volatility position, and what an LP is actually being paid for.
Perpetuals and funding, on chain options and vaults, and how a protocol's own mechanics reprice the risk you thought you had.
Rate curves, collateral and liquidation mechanics, and how to decompose a yield into the risks that pay it.
Which models carry over intact, which need recalibrating, and which assumptions simply do not hold in a market that never closes.
On chain and exchange data quality, liquidity fragmented across venues, MEV and the real cost of getting a trade filled.
Smart contract, oracle, bridge and counterparty exposure: the risks with no equivalent in equities and no line in a standard risk report.
Visibility is the new currency: the work is the asset, and being known for it is what the market actually pays on. Capability nobody can see does not get hired, funded or asked to speak. A live one hour call each week on positioning yourself as a quant on LinkedIn: rebuilding the profile around the role you want, what to publish from your own work and what to keep, and compounding a following made of the right people rather than a number.
Access to the desks, funds and protocols we work with, and the recruiters who staff them. Not a job board: introductions into a network built over years of consulting, where most roles are filled through someone who already knows the work.
Module 3 gives you the artifacts, Module 5 makes you visible, and this is where both are pointed at an actual opening.
Adapting an existing workflow to AI without losing the rigour that made it work.
Who want to operate across the full stack rather than one function of it.
Bringing the method with them and learning where the market breaks it.
Moving into quant work from another quantitative field.
Also available as a private program for trading desks, funds and research teams: the same material, run against your own book, your own data and your own constraints, so the team leaves with one shared method instead of five private ones.
Scoped in conversation, not self serve.
Bring this to your teamRegister your interest and you will get the full syllabus, the price and the enrolment window before they are public.