Innovation Practice
Innovation Lab & Innovation Venture Funds
The Innovation Lab is the hands-on R&D environment where new product theses, AI-first architectures, and category bets are prototyped, stress-tested, and turned into venture-ready blueprints. The Innovation Funds are the capital vehicles that back those bets — co-created inside the studio with the governance, technical diligence, and operating support institutional investors expect. They are designed to run together: thesis built in the Lab, capital deployed through the Funds, with the same operating team accountable end-to-end.
Most ventures fail because the thesis was never built — it was only pitched. The Lab exists so the thesis is built first, in code, with users, against real economics. The Funds exist so the capital that follows is aligned to that same discipline.
Why it exists
An R&D bench, not a pitch room
When I co-founded Blackboard, the work that mattered most happened before there was a company — small rooms at Cornell, a few of us trying to make sense of a change arriving inside higher education. The Innovation Lab is the deliberate, modern version of that room: a place to do the unglamorous, high-leverage R&D before naming a venture, raising capital, or hiring a team.
Most early-stage failures I see on boards are not execution failures. They are thesis failures dressed up as execution problems. Founders compress the discovery phase, raise on conviction, and spend the next eighteen months reverse-engineering a story to fit the burn rate. The Lab inverts that — the thesis is built, not pitched.
How the Lab operates
Six disciplines run in parallel
Every thesis that enters the Lab is worked through the same six disciplines, in parallel, by a small senior team. The output is not a deck; it is a venture-ready blueprint with working artifacts attached.
- Thesis architectureThe market change being bet on, why now, and what becomes possible (or impossible) if it is true. Written tightly enough to be falsified.
- Technical prototypeA real, running AI-first prototype — built using the studio's vibe-coding stack — that proves the core mechanic, not just a UI.
- Customer pull-throughDirect conversations with named buyers and end users. We measure pull, not interest. Letters of intent count; nodding heads do not.
- Unit economicsA defensible model of cost-to-serve, gross margin, and CAC payback under AI-native assumptions — not 2018 SaaS heuristics.
- Category architectureWhere this venture sits, who it displaces, and the language that lets buyers, analysts, and capital understand the bet.
- Governance scaffoldThe audit, security, and AI-governance posture the venture will need at Series A and beyond — designed in from day one, not retrofitted.
What ships from the Lab
From the Lab to a venture-ready blueprint
A thesis exits the Lab in one of three ways. It graduates into the studio for incubation with capital — drawn from the Innovation Funds — and an operating team. It is licensed or partnered into an existing operator that is better placed to run it. Or it is killed cleanly, with the learning written down and the next thesis better for it.
The discipline of being willing to kill is what makes the rest credible. A Lab that never says no is just an expensive idea factory.
The Funds thesis
Capital aligned to category creation
I have spent my career on both sides of the table — as the founder being underwritten, and as a director or advisor sitting on the underwriting side of governance. The Innovation Funds are designed from that dual vantage point. They back ventures co-created in the studio so the capital, the operating team, and the technical thesis are aligned from the first dollar in.
The bet is not on a sector. It is on a structural change in how software companies are built when AI is the operating substrate — leaner teams, faster cycle times, defensible category positions, and unit economics that look nothing like classic SaaS.
Where the capital goes
What the Funds back
Allocations concentrate where the studio has unfair operating advantage and where category architecture is most needed.
- AI-first vertical platformsSoftware systems where AI is the substrate, aimed at regulated or workflow-heavy industries that legacy SaaS underserves.
- Education and credentialingThe next generation of learning, assessment, and credentialing infrastructure — informed by category-creation experience at Blackboard and the broader online-learning practice.
- Governance, audit, and trust toolingSoftware for boards, auditors, and CISOs who now own AI risk — built from direct experience inside audit, risk, and compensation committees.
- Operator-led spinouts and recapitalizationsSelective situations where a strong operating thesis, an AI-native rebuild, and patient capital can unlock value the prior structure could not.
How it is governed
Institutional discipline from day one
Every fund is structured to meet the standards LPs and limited partners expect from institutional managers — independent oversight, technical and security diligence, ESG and AI-governance posture, and clear reporting cadence to investment and audit committees.
The studio model is the differentiator. The fund does not just write checks; it underwrites alongside operators who are building the venture in the same room. That is what makes the diligence real and the post-investment support credible.
Founders, operators, LPs, family offices, and strategic partners — whether you have a thesis to pressure-test in the Lab or capital to deploy through the Funds, open a direct conversation.