Capital Gains Emmet King, Co-Founder and Managing Partner at J12, shares what founders need to get right before their first fundraise, how to turn rejection into momentum, and why AI's next frontier is closer than we think.

When it comes to early-stage investing, Emmet King doesn't sugarcoat it: storytelling trumps spreadsheets, proof outshines potential, and vision needs a roadmap. As Managing Partner and one of the founders at J12, a venture firm backing European start-ups at the intersection of data and AI, Emmet sits at the frontlines of the continent's next wave of breakout companies. In this edition of Investor Insight Entrepreneur UK, he breaks down what makes a pitch unforgettable, why 'no' doesn't mean never, and the AI shifts he's betting on right now.

What are the top three things founders should focus on when preparing to raise their first round of funding?

1. Storytelling
   As a founder, being objectively investable is not enough. You have to find ways to clearly describe how you see the world, what you are building, why it matters, why now, and how you will win. The time, intelligence, and willingness an investor may have to deeply understand your company should never be overestimated - storytelling is how you convey a vision in a way that is so compelling that it cannot be ignored.
2. Proof
   No matter how early you start raising capital, you need evidence to support your case and strengthen the validity of what you are building. This could include design partners, pilots, early customers, or a growing community of users. At even earlier stages, such as pre-traction, the validation may come from the calibre of talent on your team, your unique expertise in the space or your understanding of market dynamics. The goal is not traction for its own sake, but demonstrating inevitability and that gravity is already forming around your company.
3. Plan
   You need to show how the capital raised today will define your position tomorrow. What are the key milestones you will hit in the next 12-18 months? What does your product roadmap and go-to-market strategy look like? What talent are you going to hire? What is the story you are going to tell when raising your next round of capital? Technical ambition alone is not enough, it must be paired with the ability to articulate a clear, executable path forward.

When evaluating early-stage start-ups, what key factors do you prioritise?
Above all, we underwrite the founder. At the earliest stages of company building, everything else is variable - products pivot, markets shift, capital cycles change - but the founder is constant and the key factor we prioritise. We value those that possess relentless drive, extreme adaptability, and the strong magnetism to attract early customers, world class talent and future capital. Beyond that, within our focus on the data and AI landscape - specifically the Enablers and Applications - we value ambitious teams and companies that demonstrate:

- Domain-specific insight and founder-market fit
- Defensible data advantage: access, rights, and flywheels
- Workflow ownership: solutions that are "painkillers", and not just features
- Speed and adaptability: ship fast, learn faster
- Opinionated UX and trust: usable, auditable, governable and resilient

How should founders handle a "no" from an investor? What's the best way to build long-term relationships, even if they don't secure investment straight away?

1. Make the interaction count
   A good meeting should yield more than just a "no". Try to understand how the investors view the market, how they perceive your strengths and weaknesses, and how they think about opportunities at large. You do not have to agree, but after several rejections you may start seeing patterns that reveal some red flags or can help sharpen how you communicate your conviction further down the line.
2. Use progress to build momentum
   The goal is not to keep every investor constantly updated with newsletters, but to make your momentum visible and hard to ignore. Share progress and milestones publicly on Linkedin or other channels, and keep existing investors closely informed so they can amplify your story. Naturally, you should stay engaged with the prospective investors you respect, but focus most of your energy on building your company. Results speak louder than words.

What start-up sector or trend excites you the most at the moment, and why?
The shift to AI is a generational opportunity for value creation, particularly through agentic solutions that augment or automate work. Unlike traditional software, these solutions can be funded from labour budgets, not just IT, unlocking an addressable market opportunity up to ~50× larger. Yet we are still in the early stages - most organisations are only just starting to move from AI experiments to scaled deployment and integrate intelligence into core workflows.

With this backdrop we see two massive frontier opportunities unfolding: (1) the infrastructure and tooling that make AI practical at scale, and (2) domain-specific applications that transform critical workflows and outcomes. Our mission at J12 is to partner early with founders across Europe defining either of these frontiers, and support them from first build through to global scale.

Which three start-ups that you funded this year excite you the most - and why?

1. DataCrunch – the AI cloud providing premium GPU servers and clusters, and model inference services. The gap in cloud capacity in Europe remains one of the defining challenges for the ecosystem. From early-stage companies to large enterprises and research institutions, the demand for GPU access is growing rapidly. The DataCrunch team is executing their mission phenomenally well and is positioned to become Europe's first AI hyperscaler.
2. DropCode – the experimental infrastructure providing data for AI-native biology at scale. Modern enzyme discovery is constrained by throughput. DropCode merges droplet microfluidics with DNA barcoding, enabling over a million unique experiments per hour - 1000x faster than conventional wet labs. By generating functional data at unprecedented scale, they provide the training material generative AI needs to design and optimise entirely new categories of enzymes, expanding the possibilities of drug development, industrial biocatalysis, and sustainable manufacturing.
3. Ayora – the platform for legal firms turning matter and time data into commercial intelligence. Legal firms now compete not just on excellence in law, but on commercial execution - client management, pricing, billing, and project management. Ayora transforms matter and time data into actionable commercial insights, enabling firms to optimise pricing, resource allocation, and client relationships. This gives legal firms a scalable, defensible advantage in the AI era.