The ten-year journey
For a decade, I've been obsessed with a fundamental problem: startup ecosystems are broken by design.
As CMO of Microsoft Accelerator London from 2015-2019, I watched 2,000+ founders pitch annually. I hand-picked the best 20 and guided them into Microsoft's global co-sell programme. I built 150+ VC relationships, influenced over £500M in ecosystem investments, and spoke at Oxford Saïd, LBS, and HEC Paris about what actually works.
But here's what haunted me: even the "winners" were burning cash faster than they could prove value. The traditional funding model forces founders to choose between speed and equity preservation. It's a false choice that kills momentum.
The Consilience Ventures experiment
In 2018, I founded Consilience Group and co-founded Consilience Ventures, which we launched in 2020 to test a radical hypothesis: what if we could turn expert networks into sustainable revenue streams while preserving founder runway?
Over two years, we deployed £3M across seven AI, fintech, and blockchain startups using milestone-driven expert engagement with capped repayments.
The data was undeniable. Through our Global Expert Network of 250 vetted professionals (25 years average experience), we could recruit senior talent in 5-12 days versus 30-105 days through conventional methods. We cut time-to-expert by 85% while creating aligned incentives across the entire network.
"Access to the Consilience curated experts has accelerated solutions to a wide variety of business challenges that require bespoke expertise which typically is difficult to find," said Povl Verder, CEO of SIME Clinical AI. "The sprint financing approach delivers improved timelines both from funding and commercial growth perspectives."
But Consilience Ventures was just the beginning. We'd proven the model worked at venture scale. Now I wanted to rebuild the entire startup ecosystem.
The Execution Capital vision
For many months, I've been building Execution Capital—the first Execution Finance infrastructure for startup programmes worldwide. EC's Growth Pool Units (GPU) convert equity into execution, letting founders hire senior experts now and repay later through capped FlowShare arrangements.
But here's what makes EC revolutionary: it's not one-size-fits-all. Different Operators (venture studios, incubators, accelerators, VCs and PEs) can deploy GPU strategies based on their specific priorities, strategies and market conditions. The question became: what are the optimal strategies, and when should each be used?
I needed an answer backed by hard mathematics, not founder folklore.
The AI experiment
I designed an experiment to stress-test the Execution Capital model from every angle - to be very critical about it. I locked in the numbers for a B2B SaaS company (£2M pre-money valuation, £200K runway need) and defined three distinct strategic approaches:
Lower equity stack — Use £200k GPU on founder-friendly equity terms Aggressive growth — Layer £400k GPU on top of £200k cash for 3× the budget
Fair equity stack, same runway — Split £200k as 30% cash / 70% GPU for maximum alignment
Then I set ChatGPT, Grok, and Claude loose on the problem. No hand-waving allowed. Pure mathematical argumentation.
The battle of the algorithms
ChatGPT's position: lower equity stack
"Execution Capital doesn't need VC-sized equity if it also earns FlowShare (a small percentage of capped future revenue). Issue £200k GPU for 5% equity plus a capped FlowShare. Same firepower as a £200k cash round, less equity given up."
The mathematics: 5% equity dilution, £600K FlowShare cap, £2.2M future valuation
Grok's position: aggressive growth
"Don't chase 'cheap equity'. Mirror investor terms. The win is 3× more firepower: take £200k cash + £400k GPU = £600k budget. That's how you hit 3× more milestones."
The mathematics: 23.08% equity dilution, £1.2M FlowShare cap, £7.8M future valuation
Claude's position: fair equity stack, same runway
"Keep the £200k budget but split it 30% cash / 70% GPU. Equity stays 'fair' like a normal £200k round, but you align experts via FlowShare. Same runway, stronger alignment."
The mathematics: 9.09% equity dilution, £420K FlowShare cap, £2.2M future valuation
What ChatGPT, Grok and Claude revealed: three operator archetypes
Each AI had to grapple with the complex FlowShare economics and discovered completely different ways to optimize the system. They essentially identified three distinct Operator personality types:
The Deal Hunter (ChatGPT) prioritizes superior selection through irresistible terms. These operators believe the best startups will always choose the best offer, and EC's FlowShare structure lets them consistently offer what traditional VCs simply cannot—while building a liquid expert network that compounds over time. Perfect for operators in competitive ecosystems where deal flow quality determines everything.
The Growth Accelerator (Grok) leverages proven momentum for exponential outcomes. These operators don't want to do early-stage risk assessment—they want to take validated teams and supercharge them with both capital and liquid expert networks. The FlowShare payments from aggressive growth create the largest liquidity pools for expert reinvestment. Perfect for operators focused on later-stage scaling in high-growth sectors.
The Velocity Optimizer (Claude) reduces friction in the funding process itself while maximizing FlowShare alignment. These operators see fundraising delays as the enemy of execution and use EC to eliminate lengthy capital-raising cycles. The 70% GPU allocation creates immediate expert liquidity while preserving founder control. Perfect for operators in fast-moving markets where timing beats everything.
The mathematical proof was stunning: each strategy delivers strong founder outcomes while creating different FlowShare liquidity patterns that attract different expert participation models and founders.
The Operator's superpower
Here's what the AI experiment proved that most human analysis could have missed, given our pre-conception of fundraising, startup building, and acceleration: Execution Capital isn't a product—it's an infrastructure. Operators can deploy different GPU strategies across their portfolio:
- Star experts recruited through lower-equity-stack tickets (most founder-friendly)
- Market-blitz cohorts using aggressive growth budgets (most shareholder-friendly)
- Ecosystem loyalty built through fair equity alignment (most ecosystem-friendly)
The model adapts to the mission, not the other way around.
Traffic-light decision framework
| Strategy | Growth Opportunity | Alignment Strength | When to Use |
|---|---|---|---|
| Lower equity stack | 🟠 Medium (same runway as £200k round) | 🟢 Strong (experts share ~27% via FlowShare) | When prioritizing minimising immediate dilution while aligning talent |
| Aggressive growth | 🟢 High (3× budget → 3× milestones → stronger terms) | 🟢 Strong (everyone shares; founder retains majority) | When prioritizing speed and scale — ship more, faster, with senior talent |
| Fair equity stack | 🟠 Medium (same runway as £200k cash) | 🟢 Strong (experts share ~19% via FlowShare) | When prioritizing balance — keep equity "fair" but make the system 70% more aligned |
The FlowShare revolution: how capped liquidity can transformsportfolio economics
The AIs unanimously validated something deeper than FlowShare's fairness—they revealed how it fundamentally restructures startup finance by creating expert liquidity where none existed before. However, FlowShare is optional in Execution Capital, Operators can decide to leave or take this option while designing their investment strategies.
The traditional expert problem
In conventional models, experts face an impossible choice: take cash (limited upside) or take equity (illiquid for years, concentrated risk in one company) - even if we know most startup programmes leverage free mentor pools. Most choose cash, creating a permanent talent shortage for startups that can't afford senior rates and transferring all the risk to founders and shareholders.
FlowShare: capped, liquid, diversified
FlowShare solves this through three breakthrough mechanics:
1. FlowCap creates predictable liquidity. Unlike traditional equity that remains illiquid until exit, FlowShare has a defined cap (3× investment value) that creates measurable, time-bounded liquidity expectations. Experts know exactly when their participation ends and can model their portfolio returns.
2. Portfolio-level cash flow distribution. Here's where it gets revolutionary: as portfolio companies hit milestones and generate revenue, they begin FlowShare payments across the entire expert network. An expert who worked on Company A's AI strategy starts receiving payments when Company B hits its revenue targets, and vice versa.
This creates a portfolio-wide liquidity engine where expert compensation flows continuously from the collective success of all portfolio companies, not just the individual company they worked with.
3. Compound expert capacity. As experts receive FlowShare payments from the portfolio, they can reinvest that liquidity back into GPU work with new startups. This creates an expanding cycle:
- Expert works for GPU equity in Startup A
- Startup B (different expert project) hits revenue milestones
- Expert receives FlowShare payment from portfolio pool
- Expert uses payment to take on more GPU work with Startup C
- Expert's portfolio exposure grows while maintaining liquidity
The network effect
This transforms expert incentives completely. Instead of choosing between "safe cash" and "risky equity," experts can build a diversified portfolio exposure while maintaining regular liquidity. The more successful the overall portfolio becomes, the more capacity experts have to take on additional GPU work.
The result: GPU liquidity becomes self-reinforcing. Success breeds more expert availability, which creates more execution capacity, which drives more portfolio success, which generates more FlowShare payments, which attracts more expert participation.
Why do these changes portfolio strategy
Traditional VC portfolios depend on 1-2 massive exits to generate returns - we call it the Power Law distribution. EC portfolios generate continuous cash flow distribution across the expert network as companies hit revenue milestones. This creates:
- Predictable liquidity cycles instead of binary exit events
- Distributed risk across expert network participation
- Compound execution capacity as FlowShare payments fund more GPU work
- Self-sustaining expert ecosystem that grows with portfolio success
The AIs proved this mathematically: in every scenario, experts capture 15-27% of future value through FlowShare, but that value flows as operational cash flow, not illiquid equity positions. This liquidity then funds the next generation of GPU deployments, creating an execution finance flywheel that traditional models simply cannot match.
The punchline
After watching three LLMs fight with locked mathematics and a good sense of investments, the answer to "Is equity plus 3× repay fair?" became crystal clear:
Fairness comes from a capped, milestone-triggered FlowShare that brings experts into the outcome.
Value comes from choosing the right Operator strategy — lower equity today, aggressive growth tomorrow, or fair equity with alignment always.
Alignment is the force multiplier: the more aligned the network, the less founders and shareholders carry alone.
The startup ecosystem doesn't need another funding mechanism. It needs an execution infrastructure that adapts to every Operator's vision of what success looks like.
What is your strategy to win in 2026?