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20VC · 3 min · Apr 7, 2026

Algorithmic breakthroughs could widen the gap between AI labs

Demis Hassabis expects frontier labs with algorithmic research capacity to pull ahead as current ideas yield less. Open models may trail by about six months, while small Gemma models target researchers, startups and edge use.

Demis Hassabis

Demis Hassabis is co-founder and CEO of Google DeepMind.

  • Harry Stebbings, Host · Founder, 20VC

    Built a venture firm on his podcast; its third fund closed at $400M.

About this segment

Demis Hassabis expects frontier labs with algorithmic research capacity to pull ahead as current ideas yield less. Open models may trail by about six months, while small Gemma models target researchers, startups and edge use.

From “Demis Hassabis: Why AGI is Bigger than the Industrial Revolution & Where Are The Bottlenecks in AI”, Apr 7, 2026. Watch the full episode

Answers “What will AI look like in the next few years?” See all 45 answers

Answers “Is AI progress slowing down?” See all 5 answers

Answers “Is open-source AI catching up?” See all 7 answers

“invent new algorithmic ideas are going to start having bigger advantage over the next few years”
Demis Hassabisat 9:35 · 20VC9:35

Takeaways

  1. New algorithmic ideas may matter more as current gains get harder.
  2. Open models typically lag the frontier by about six months.
  3. Small Gemma models target researchers, startups and edge computing.

In this episode

Full episode
  1. 2:43AI still lacks continual learning despite strong scaling returns3:37
  2. 12:18AI drug design must still clear the clinical-trial bottleneck3:04

  3. 8:41Algorithmic breakthroughs could widen the gap between AI labs3:05

  4. 15:46AI safety needs global standards and independent model audits4:10Up next
  5. 25:11The UK offers DeepMind talent, while Europe lacks growth capital4:19

Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

  1. 8:53

    if you think about how our minds work, it shouldn't have those kinds of holes in it. We said about a plateauing of scaling rules. Everyone talks about a commoditization of models in terms of capabilities. Do you think we see that or do you think we see ones to continuously accelerate ahead of the others? Yeah, I feel like maybe, you know, the three or four leading labs now, which we're one. the gap is sort of starting to pull away because a lot of these tools also, of course, help you build the next generation. So things like coding tools, math tools, and it's getting harder and harder, I would say, to kind of eke out the same gains from just the same ideas. So I think those labs that have capability to, you know, invent new algorithmic ideas are going to start having bigger advantage over the next few years as the last set of ideas are sort of, you know,

  2. 9:56

    all the juices being wrung out of them. You were very open with a lot of your research for years and we see many very good quality open models. How do you think about the future of open? I have many portfolio companies that kind of use frontier models and then they use that to set a benchmark and then they use open models to kind of get as close as possible but with more cost effectiveness. What does that future look like? Yeah I think it's probably similar to what we're seeing today. I mean we're big supporters of open science and open models and we've done many many things obviously from the original Transformers to AlphaFold.

  3. 10:29

    You know, these are all things we've sort of given out into the world and to help the research community. And we plan to continue to do that, especially in applied domains, you know, scientific domains, applying AI to science, which is obviously my passion. But increasingly, you know, what you're going to see is the open source models are probably one step back from the absolute frontier. You know, it usually takes about six months for the open source community to sort of reimplement and figure out what those ideas are.

  4. 10:54

    But we are also pushing hard on a kind of suite of open source models called Gemma, which are, you know, we're determined to kind of make best in class for their sizes. So specifically for small developers or academics or, you know, the beginnings of a startup, I think they're perfect for that. And also edge computing, too. So we're very interested in open source models for certain types of applications.

  5. 11:29

    How do you think about a world post LLMs? You have different people with different views. You're Yalana Koons with very different views.