From the Editor’s desk:
• Please join for our upcoming Mixer featuring a VC panel — including breakout roundtables and networking drinks — Thursday, Feb 26, London “AI, Power & the Road to 2026”
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The Latest on Pathfounders:
• JOIN our first mixer: “AI, Power & the Road to 2026” featuring a VC panel — including breakout roundtables and networking drinks — Thursday, Feb 26, London
Featuring:
• Sitar Teli, Managing Partner, Connect Ventures
• Alex van Someren, recently exited as the UK's Chief Scientific Adviser for National Security and former VC
• Manjari Chandran-Ramesh, Amadeus Capital Partners, Partner in the Early Stage Fund focusing on AI, Robotics and Quantum
• Ana Barjasic, Connectology, European Innovation Council
We’ll explore the major forces shaping tech and venture in 2026, including:
• AI’s next phase
• What’s next for Deeptech
• Geopolitics and the future of sovereign technologies
Agenda:
6:00pm – Arrival & Networking Reception
6:30pm – Panel Discussion
7:15pm – Pathfounders Speed Tables
8:00pm – Open Networking
The latest on Pathfounders:
Quantonation raises the largest ever quantum fund
The Paris and New York City-based venture capital firm is backed by Novo Nordisk, Toshiba and Vertex Holdings
by Amelia Isaacs
Pathfounders Interview: Pierre Festal on Kembara's billion euro growth fund
Spain-based Mundi Ventures recently launched the Kembara Venture Fund to address this with a €750 million first close, and the ultimate target of a billion euros. Partner Pierre Festal joined Pathfounders to unpack what this means for Europe.
• Watch on YouTube
• Listen on Spotify
Pathfounders Interview: EdTech investment is changing fast
‘Edtech’ is morphing into ‘Learning & Work’ according to a new report from Brighteye Ventures. Pathfounders caught up with report author Rhys Spence to unpack where VC investors are putting their money in Europe.
• Watch on YouTube
• Listen on Spotify
Pathfounders interview: Philip Bahoshy, CEO of Magnitt, on the AI investment boom in MENA
Venture capital in the MENA region is moving decisively toward AI-native platforms, which captured 69% of AI funding. The UAE and Saudi Arabia jointly attracted 87% of all AI capital deployed. Pathfounders caught up with Philip Bahoshy, CEO of Magnitt, a key research house in MENA, to discuss how investment AI is shaping the region's tech industry.
• Watch on YouTube
• Listen on Spotify
Who is David Silver?
Pathfounders did an AI-assisted deep dive into the man of the week, David Silver, who left Google DeepMind late last year to launch a London-based start-up, Ineffable Intelligence.
This week, the startup raised raising $1bn at a reported $4bn pre-money valuation, led by Sequoia, with big tech also in discussions to join the round. (Financial Times).
If you want the short version, it’s this: Just creating bigger chatbots via LLMs is a dead end. His idea is to create AI agents that can create experiences and environments in which they themselves can learn more than what human knowledge can teach them.
So Ineffable Intelligence will be a lab built around Reinforced Learning as the scaling paradigm, aiming at “superhuman intelligence” as an explicit target.
If he’s right, whoever cracks scalable “experience” loops gets a compounding platform that isn’t bottlenecked by human knowledge. Hence, the investor appetite.
What we know:
David Silver (b. 1976) is one of the best-known reinforcement learning (RL) researchers of his generation: He’s a DeepMind “original,” UCL professor, and a key figure behind AlphaGo, AlphaZero, and AlphaStar—systems that hit superhuman performance in games via deep RL.
Why is this important?
There’s been a capital stampede into “founder-scientist” AI labs aiming to go beyond today’s LLM paradigm, especially by building agents that learn by doing, not just by reading.
What is his core thesis?
Silver’s big idea (formalised with Richard Sutton) is that AI is shifting from systems trained mainly on human-produced text/images toward systems that improve mainly through experience:
We are now in the era of Human Data: models absorb “everything humans wrote down,” then get nudged by human preference. Powerful, but bounded by human knowledge. (Here’s his paper on the idea)
We are entering the era of experience: “a new generation of agents” will become superhuman by learning predominantly from experience—i.e., by interacting with environments, generating data, and iterating. Experience will dwarf the scale of human data used today.
He claims AlphaZero has already demonstrated this, the lesson being: “remove the human ceiling”
What’s his story?
AlphaGo (2016) used human expert games to get started, then reinforcement learning to improve.
AlphaGo Zero / AlphaZero showed something more radical: human data wasn’t necessary and could even be constraining; self-play + RL could blast past the best human play and keep improving. (This is where he leans on the “bitter lesson” framing.) (Deepmind Interview)
He treats “Move 37” as the symbolic moment: “One of the biggest moments of the Alph Go story was move 37 that everyone always references. Move 37 was a move that happened in the second game of Alph Go against Lisa Dal. Alph Go played a move that defied everyone's expectations. an “alien” move the human community didn’t expect—evidence that experience-driven systems can produce novelty that isn’t just “average human internet text.”
What he thinks is missing in LLMs:
Silver’s critique is not that LLMs are useless, but they are based too much on us:
LLMs + RLHF (Reinforcement learning from human feedback) are great at human-aligned fluency and usefulness, but anchored to human judgments—so they struggle to reliably discover strategies humans wouldn’t recognise as good ahead of time.
He draws a sharp distinction between:
Ungrounded preference (a rater saying “this cake recipe looks good”),
versus grounded feedback (someone actually bakes/eats it and the outcome is good/bad).
He argues the latter is what unlocks open-ended improvement and genuine discovery.
In short: human feedback provides scaffolding but not a jumping off point to go somewhere new.
He thinks AI agents are the commercial bridge:
Silver/Sutton explicitly bet on agents learning from interaction as the route to “superhuman capabilities.”
Ineffable is there positioned as building on Silver’s RL work, training systems through interaction with environments, not only static text.
This matches the broader industry thesis: agents are how you operationalise intelligence into workflows (planning, acting, iterating), not just chat.
His view of creativity and “beauty” as pertains to AI:
Creativity is not mystical; it’s what happens when a system performs massive trial-and-error and accumulates “a million mini discoveries.”
He’s motivated not just by utility, but by the idea that intelligence can produce beauty: the “sense of beauty” Go players reported when AlphaGo revealed new possibilities.
Safety, risk, and alignment (as he frames it):
Silver is concerned about the unintended consequences of humans (climate, pathogens, etc.) and sees AI as potentially a tool to avert disasters—if regulations prohibit unacceptable uses.
In the “experience era” framing, he acknowledges serious risks in “untethering” systems from human data and says the transition needs to be taken seriously (part of why he wrote the position paper at all).
He also implicitly runs into the classic specification/metric problem (“paperclips,” tyranny of metrics): he argues real-world environments contain many signals and that systems could adapt goal proxies over time, with humans as part of the environment, but he does not claim alignment is solved. (Alignment Forum)
What Ineffable Intelligence likely represents:
Based on news reporting and his published thesis, Ineffable looks like a very specific bet:
Post-LLM frontier: not “bigger chatbots,” but experience-generating, environment-interacting agents.
A lab built around RL as the scaling paradigm (“sustainable fuel” vs mining human data), aiming at “superhuman intelligence” as an explicit target.
If he’s right, whoever cracks scalable “experience” loops (in sims, tools, code, robotics, science) gets a new compounding curve that isn’t bottlenecked by the web. Hence, the investor appetite.
Op-Eds and Contributed Columns
We’re ready to start receiving pitches for Op-Ed columns. These can be on tech, startups, investing, AI, the future, frankly, you name it. So long as it tech starup or VC-related and bloody interesting.
The rules are: A short paragraph outlining the piece. Followed by three or four bullet points about the main issues you’d cover. The argument must be backed up by third-party references. We’ll only take pieces we green-light. Nothing submitted ‘on-spec’. We will build a form for these submissions, but in the meantime, you can pitch us at [email protected]. Pitching us more generally? Here’s all you ned to know about that subject.
Hot Tips and News
Please send all tip-offs, suggestions, and news to [email protected]
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