DeepMind CEO laments that AI commercialization is too rapid: if more time had been spent in laboratories for a few more years, humanity might have already conquered cancer.

Google DeepMind CEO Demis Hassabis laments that the rush in AI commercial competition is too hasty; if the technology had been refined in laboratories for a few more years, humanity might have already conquered cancer.

AI is rapidly transforming humanity, with new technologies and tools emerging every few weeks or even days, but Demis Hassabis, CEO of Google DeepMind and 2024 Nobel laureate in Chemistry, believes that the pace of AI competition is too hurried. If he had his way, AI would spend more years in labs honing its capabilities, and perhaps humans would have already solved cancer.

Hassabis shared this sentiment about current AI development during a podcast with video journalist Cleo Abram. In a past interview with Time magazine, he described himself as a scientist, emphasizing that his exploration of AI is driven by a pursuit of knowledge and understanding of the world.

He mentioned that his initial motivation for entering the AI field was not to create chatbots, but to accelerate scientific discovery. Their most famous achievement is AlphaFold, a system that solved the “protein folding problem,” which had eluded biologists for 50 years. Hassabis pointed out that this benefits over 3 million scientists worldwide, especially in research on diseases like malaria, as AI provides free structural databases, allowing researchers to skip basic experiments and move directly into drug development.

Image source: YouTube AlphaFold research results, which contributed to Hassabis becoming a Nobel laureate.

He believes that if AI had been allowed to stay longer in labs focusing on such critical issues, humanity might have already made more decisive breakthroughs in cancer treatment or materials science.

Cutting-edge technology reaching the public in just months, yet key problems lose resources

Hassabis outlined his ideal AI development path—what he calls the “CERN model.” He hopes the process of developing artificial general intelligence (AGI) can be as rigorous, cautious, and methodical as CERN’s operation of the Large Hadron Collider, ensuring progress only after thorough understanding of each step.

However, reality has diverged from Hassabis’s ideal scenario. The explosion of ChatGPT and breakthroughs in generative AI at the end of 2022 sparked a chaotic global business race. He admits this has accelerated AI deployment, with advanced tech reaching the masses in just months, but it has also diverted resources away from truly critical issues.

In the race for market and technological dominance, development has been forced into high-speed progress. Hassabis confesses that they can no longer develop technology with the philosophical deliberation and cautious evaluation he once envisioned.

While AI chatbots are useful for summarization and brainstorming, they still have inherent flaws like hallucinations. Yet, commercial pressures push these experimental products rapidly into the market. This results in a large portion of R&D focus and resources being channeled into the cycle of releasing general-purpose foundational models aimed at mass users.

To balance reality and ideals, Hassabis adopts a more pragmatic approach—leading Google’s consumer AI products like Gemini, while also investing in applied AI (Narrow AI). He believes there’s no need to wait for true AGI; systems like AlphaFold that solve specific problems can already bring tangible benefits to energy, materials science, and healthcare.

AlphaGo’s legendary move reveals AI’s potential to surpass human thinking

Hassabis’s confidence in AI largely stems from the 2016 AlphaGo match against South Korean Go master Lee Sedol. During that game, AlphaGo played the famous “Move 37,” which was initially mocked as an unthinkable move, yet ultimately led to AlphaGo’s victory.

Image source: gogameguru.com The move AlphaGo played that no human player would have considered, which Hassabis sees as a breakthrough in AI surpassing human cognitive frameworks.

From this signal, Hassabis realized that AI had developed the ability to go beyond human experience and seek entirely new solutions. He aims to apply this creative capacity—surpassing human thinking—to science.

AlphaFold exemplifies this mindset. Traditional methods require hundreds of thousands of dollars and years to determine a single protein structure, but AlphaFold 2 has predicted nearly 200 million known protein structures.

Now, Hassabis is leading his team into deeper drug discovery. Traditional drug development takes about 10 years with only a 10% success rate. He founded Isomorphic Labs, which uses AlphaFold 3 and subsequent models for “virtual screening.” AI can simulate millions of compound-protein interactions in minutes, while also checking for toxicity across over 20k human proteins, allowing most failures to be filtered out in silico before laboratory testing, thus only the most promising candidates proceed to experimental validation.

Concerns about two risks AI might bring

However, as AI technology advances into an era of AI agents, Hassabis’s concerns about the future have become more concrete. He categorizes the risks into two main types: the first is “malicious actors”—individuals or nations who might misuse technologies originally intended for curing diseases or developing new materials for harmful purposes.

The second, more sci-fi but real threat, is “going rogue.” When systems become extremely intelligent and autonomous, ensuring they execute human-set goals precisely and do not bypass safety measures becomes an extremely difficult technical challenge.

In response to these challenges, Hassabis calls for leading AI research institutions, governments, and academia to establish international cooperation mechanisms, emphasizing the need for more safety research on the final stretch toward AGI.

Despite regrets that AI didn’t stay longer in labs, Hassabis remains optimistic about the next 50 years. He envisions AI helping humans achieve breakthroughs in nuclear fusion, discovering room-temperature superconductors, and even reducing space travel energy costs to near zero. To him, AI is not just a technology but a magnifying glass for exploring the universe’s truths. Whatever the answers may be, he is eager to uncover the truth.

• This article is reprinted with permission from “Digital Times”
• Original title: “Nobel Laureate laments ‘AI commercialization too fast’: If labs had been kept longer, humans might have already conquered cancer!”
• Original author: Chen Jianjun