Europe is losing the AI race. This is the only initiative that gives me hope that this might be turned around.
Interview Daniel W. Dippold
Daniel studied pure mathematics, machine learning, and business. He raised $100M in his twenties and created companies worth hundreds of millions. He invested in 50 companies as an angel investor (7 unicorns) and is an LP in multiple funds. He trained the Jiangsu Olympic Math team, wrote a book on the scientific principles of productivity, endorsed by the BBC, and gave his first TED Talk at age 20. Today, Daniel runs EWOR, a radically selective Fellowship for founders with the potential to create $10B+ organisations. EWOR counts 40,000 yearly applications, employs a full-time team of which 40% have built $100M-$10B companies, and whose companies are collectively worth over $5B, only 2.5 years after the Fellowship's inception.
Why must this initiative exist - now?
Europe is losing the AI race. This is the only initiative that gives me hope that this might be turned around.
Beyond money: what's the real 'operating space' teams get here?
It's about the money. Let's be honest - $30M is what AI labs in Europe need right now. The $1B raise after even more. Compute = capital and talent is abundant in Europe. Let's cut the fluff and focus on what moves the needle.
What would a real breakthrough look like?
We've lost the race on the big foundational models. I have hope that we can win the race when it comes to a) disruptive approaches to LLMs (offering the same value, but more compute-efficiently driven by a different technology), b) neurosymbolic AI & neo-reasoning models, and c) verification-layer stuff (see Axiom, Terence Tao, …), and d) infrastructure for multi-agent systems
What responsibility comes with building foundation models?
You run a research lab and a company at the same time. I believe foundation models should not be responsible for de-biasing
their answers; they should educate the everyday consumer of the risks in using them, akin to Anthropic and OpenAI, and focus on pushing progress forward. We won't win the AI-race in Europe by discussing which data to use for a million years just to learn while we discussed, others have monopolised the landscape. It is responsible to care about these things, but irresponsible to render ourselves redundant while other nations push progress forward and outcompete us.
What's the biggest challenge for Frontier AI in Europe right now?
Capital and talent.