Why Google fell behind in the AI race

Summary

Podcast Summary: The Indicator from Planet Money

Episode: Why Google Fell Behind in the AI Race

Date: July 9, 2026
Hosts: Darian Woods, Tarian Woods, Waylon Wong
Featured Guest: Sebastian Mallaby (journalist and author)

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Overview

This episode delves into why Google, long considered the internet’s king, has found itself trailing in the race to dominate artificial intelligence, especially as products like ChatGPT and Claude have garnered public mindshare. The hosts discuss Google’s paradox: despite pioneering AI breakthroughs with researchers like Demis Hassabis, it hasn’t become synonymous with AI chatbots or tools. The episode features insights from author Sebastian Mallaby, who recently profiled Hassabis, and explores the so-called “triple innovator’s dilemma” that haunts big tech incumbents like Google.

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Key Discussion Points and Insights

1. Google’s Fall from Tech Dominance

• (00:08) Google was once king—“You Google information. That is the verb. You don’t Yahoo the web.”—but in AI, it’s fallen behind flashier upstarts.
• ChatGPT and Claude have become the cultural stand-ins for chatbots and coding tools.

2. AI Ambitions at Google

• (00:44–01:16) Demis Hassabis, CEO of Google DeepMind, demonstrates playful AI (video generation), but also lays out ambitious aims:
  • “Our mission is to reimagine the drug discovery process with the goal of one day solving all disease.” (01:12, Hassabis via soundbite)
  • The paradox of immense ambition against a backdrop of failing to lead the AI race.

3. Sebastian Mallaby on Demis Hassabis

• (01:30–03:46)
  • Mallaby calls Google “fantastic at coming second.” (01:30, Mallaby)
  • He spent 30+ hours with Hassabis, capturing his intense, almost mad-scientist drive:
    • “At 2 in the morning, Sebastian, I’m reading a scientific paper. I can feel reality staring at me, calling out to me, waiting to be discovered. That’s why I’m building AI. We need to understand these things.” (03:14, quoting Hassabis)
    • “Why is this table solid? It’s a bunch of atoms jumping around with gaps in between. Why is that laptop... Why can it think? These are mysteries...” (03:28, Hassabis via Mallaby)

4. DeepMind’s Major Achievements

• (04:24)
  • Under Hassabis, Google DeepMind solved protein folding (predicting 3D shapes of amino acids)—a breakthrough that won a Nobel Prize in Chemistry.

5. Why Didn’t Google Release Chatbots First?

• (05:05–05:16)
  • “Why did Google not release a language bot? Because it had the technology, it could have done it. Why did they not release it first?” (05:05, Woods)

6. The Triple Innovator’s Dilemma

• (05:16–07:22) Mallaby’s framework on Google’s hesitancy:
  1. Brand Risk:
  • Search depends on trust; unreliable chatbots (“hallucinations”) could undermine Google’s reputation.

    “Its dominance in search depended on a reputation for providing reliable information. So it couldn’t afford to release chatbots that hallucinated.” (06:10, Mallaby)

  2. Business Model Uncertainty:
  • Google’s revenue relies on ads; unclear how to monetize AI chatbots without disrupting search.

    “It wasn’t so clear how you would integrate ads into the chat.” (06:36, Mallaby)

  3. Political and Monopoly Risk:
  • Already under antitrust scrutiny, a risky AI move could provoke regulators and alienate partners.

    “That position… would be untenable if Google alienated politicians and journalists and advertising partners...” (06:50, Mallaby)

7. Google’s AI Response

• (07:25)
  • Eventually released its own AI (Gemini, launched in March 2023), now used for research tasks at NPR, but still trailing public buzz leaders.

8. Will Google Suffer Kodak/Xerox’s Fate?

• (07:51–08:41)
  • “If you were to put your money on one company to be in the lead in a few years’ time, which company would it be?” (08:03, Woods)
  • Mallaby’s take:

    “I would say Alphabet and Google, because I think being the best at coming second is a very good strategy for surviving the distance… They are just going to roll it out at a scale that nobody else can match.” (08:10, Mallaby)

    • Emphasis on Google’s immense user base and monetization skills.

9. Hassabis’ Duality and Big Scientific Dreams

• (08:41–09:21)
  • Hassabis balances business with scientific curiosity, seeing AI as a tool to understand reality.
  • His outsize vision is exemplified by the goal to “solve all disease”—admired but viewed skeptically as overreaching.

    “He likes to make these very sweeping claims and all disease feels a bit much. But… you should be able to understand the origin of disease quite minutely through very advanced AI.” (09:31, Mallaby)

10. Closing Tone

• (09:56–10:01)
  • Playful banter: “AI is pretty good at explaining AI. It knows itself well.” (10:01, Woods)

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Notable Quotes & Memorable Moments

“Google looks like the lab that is just fantastic at coming second.”
(01:30, Sebastian Mallaby)

“Our mission is to reimagine the drug discovery process with the goal of one day solving all disease.”
(01:12, Demis Hassabis)

“At 2 in the morning, Sebastian, I’m reading a scientific paper. I can feel reality staring at me, calling out to me, waiting to be discovered. That’s why I’m building AI.”
(03:14, Demis Hassabis via Mallaby)

“The question was kind of why did Google not release a language bot? ... it had the technology, it could have done it. Why did they not release it first?”
(05:05, Darian Woods)

“Being the best at coming second is a very good strategy for surviving the distance.”
(08:12, Sebastian Mallaby)

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Timestamps for Key Segments

• 00:08 — Google’s place as former internet king
• 00:37–01:12 — Hassabis’ AI ambitions and video demos
• 01:30 — Mallaby: “fantastic at coming second”
• 03:14 — Hassabis’ scientific obsession (Mallaby anecdote)
• 04:24 — DeepMind’s protein folding breakthrough/Nobel Prize
• 05:05 — “Why did Google not release a language bot?”
• 05:16–07:22 — The triple innovator’s dilemma (brand, business, politics)
• 07:25 — Google’s AI chatbot Gemini and its utility
• 08:03 — “If you were to put your money on...” (long-term outlook)
• 08:41–09:21 — Hassabis’ dual drive: science and business
• 09:21–09:56 — Ambitious goal: "solving all disease"
• 10:01 — Closing lighthearted exchange

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Conclusion

This episode gives a concise, insightful look at why huge incumbents like Google can fumble technological pivots, despite deep R&D and visionary leadership. Google’s “triple innovator’s dilemma”—brand risk, business model disruption, and monopoly scrutiny—explains its measured approach. While it may not be first to market in consumer AI, Mallaby suggests Google’s patient, large-scale strategy and ongoing ambition may yet keep it at the forefront of tech’s next wave.

Transcript

[Darian Woods]

Npr.

[Tarian Woods]

This is the indicator from Planet Money. I'm Tarian Woods.

[Waylon Wong]

And I'm Waylon Wong. Until recently, Google was the king of the Internet.

[Tarian Woods]

Yeah, you Google information. That is the verb. You don't Yahoo the web.

[Waylon Wong]

You don't Alta Vista the web. No, no one's ever done that yet. With the rise of AI, Google has invested a ton in the technology, but that hasn't kept its crown. Like Google isn't synonymous with chatbots.

[Tarian Woods]

That is ChatGPT.

[Waylon Wong]

That's right. And it's not the go to tool for coding either.

[Tarian Woods]

That is Claude code.

[Waylon Wong]

And in a recent presentation it kind of appeared like Google was throwing everything AI at the wall to see what sticks.

[Darian Woods]

You can give it your own videos, for example, this selfie, and change reality in a really fun way.

[Tarian Woods]

Dimas Asabis is the CEO of Google's in house AI group DeepMind. And Demis was showcasing a seemingly quirky video generator that could give you a new outfit or background. But then the next minute he was speaking with extraordinary technological ambition. Our mission is to reimagine the drug

[Darian Woods]

discovery process with the goal of one day solving all disease.

[Waylon Wong]

Yeah, solving all disease. Pretty ambitious.

[Tarian Woods]

And yet despite these stratospheric goals, good Google never seems to quite get to the front of the AI race. That's according to journalist Sebastian Malaby.

[Darian Woods]

Google looks like the lab that is just fantastic at coming second.

[Tarian Woods]

Today on the show we talk to Sebastian about how Demis Hassavis made incredible breakthroughs at Google. Yet the company is only fantastic at coming second. And we learn about what's been called a triple innovator's delivery.

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[Tarian Woods]

Sebastian Mallaby writes books featuring titans like former Fed chair Alan Greenspan and investor George Soros. And he figured out that Demis Hassabis was another powerful person in one of the Most exciting industries today, AI. So Sebastian pitched him.

[Darian Woods]

There is going to be a book about you. You're cooked. It's done.

[Waylon Wong]

Demis acquiesced to having Sebastian be the one to write that first book. Sebastian spent over 30 hours interviewing him, mostly in a London publish.

[Darian Woods]

There was this possessed scientist staring at me and saying, at 2 in the morning, Sebastian, I'm reading a scientific paper. I can feel reality staring at me, calling out to me, waiting to be discovered. That's why I'm building AI. We need to understand these things. And then he would bang the table and say, why is this table solid? It's a bunch of atoms jumping around with gaps in between. Why is that laptop, Sebastian? Why is that? Why can it think? These are mysteries that we have to understand. I need to understand before I croak,

[Waylon Wong]

why is the table solid, Darian?

[Tarian Woods]

Is that a threat?

[Waylon Wong]

At 2am everything sounds like a threat.

[Tarian Woods]

I think never before has a conversation over beer seemed like at such high stakes. So what's happening here is Dennis wants to understand the universe, so he's building AI. And to build AI he studied for a PhD in cognitive neuroscience and then he co founded DeepMind in 2010 in London.

[Waylon Wong]

By 2013, Demis was being courted by various tech leaders who wanted to acquire DeepMind. Mark Zuckerberg, Elon Musk, Google's Larry Page. There were pros and cons with each. Eventually he agreed to a purchase by Google.

[Tarian Woods]

Under Demis Hassavis leadership, Google has made some incredible breakthroughs in AI research. Google DeepMind successfully predicted how amino acids form three dimensional shapes, which is a fundamental process known as protein folding that got Demis a Nobel Prize in chemistry.

[Darian Woods]

He tells me that that one Nobel Prize was a nice start, but he'd quite like another one.

[Tarian Woods]

Yeah, the buzz of the first Nobel Prize wears off pretty quickly, I hear.

[Waylon Wong]

Yes, I know the feeling well. But an incident in late November 2022 revealed a potential downside to being part of a behemoth like Google. Even though Google had the technology ready, the famous AI chatbot ChatGPT was first launched by OpenAI.

[Darian Woods]

The question was kind of why did Google not release a language bot? Because it had the technology, it could have done it. Why did they not release it first?

[Tarian Woods]

Sebastian believes that Google suffered from what he calls a triple innovator's dilemma.

[Darian Woods]

Innovators dilemma basically says, look, if you have a successful incumbent company with a very good product that's making a lot of money, it won't want to back an innovation that will cannibalize the existing product because the status quo is too sweet.

[Tarian Woods]

That's why Kodak failed to seize the digital camera market, because it was set up for the analog era. It's also why Xerox couldn't profit from personal computers, even though its researchers pioneered the technology.

[Darian Woods]

Google was making a ton of money from search. Why would it want a rival product like a large language model that would disrupt and destabilize search?

[Waylon Wong]

That's the standard innovators dilemma. Sebastian says. For Google specifically, this innovator's dilemma came in three forms.

[Darian Woods]

First of all, you know, its dominance in search depended on a reputation for providing reliable information. So it couldn't afford to release chatbots that hallucinated.

[Waylon Wong]

Remember when it was saying that you could put glue on pizza to keep the cheese from sliding off? I mean, it does work, but at what cost, Darian?

[Tarian Woods]

It does make it inedible. And Google's second force working against AI was that it was unclear how to make money from it.

[Darian Woods]

Google's revenues kind of depended on serving ads alongside the search results. And it wasn't so clear how you would integrate ads into the chat. And then thirdly, Google's market share was so big that politicians, journalists, even lawyers described it as a monopoly. And so that position, which was kind of politically precarious, would be untenable if Google alienated politicians and journalists and advertising partners by putting out a model like ChatGPT, which in the early days spewed toxic results and hallucinated and appeared weirdly sentient to some people. I mean, that would have been a shortcut to business suicide.

[Waylon Wong]

So, yeah, political risks was barrier number three.

[Tarian Woods]

Google did eventually release an AI chatbot in March of 2023, thanks to Demis Asavis and his team. That's morphed into Gemini. It's not the leading model, but it has at some points been right at the frontier on various benchmarks.

[Waylon Wong]

Yeah, we use Gemini at work at npr.

[Tarian Woods]

Yeah, not for writing, obviously, but sometimes for sorting through numerous documents in our research. Google's also a financial supporter of npr.

[Waylon Wong]

And so where does this leave Demis, Hassabis and Google or its parent company, Alphabet? Will it end up like a Kodak or Xerox consigned to the history files as a cautionary innovator's dilemma?

[Tarian Woods]

If you were to put your money on one company to be in the lead in a few years time, which company would it be?

[Darian Woods]

I would say Alphabet and Google, because I think being the best at coming second is a very good strategy for surviving the distance. There's so many people in the world who are touched by some kind of Google product, whether that's Maps or Search, Google Drive, Gmail, that they are just going to roll it out at a scale that nobody else can match. And in the long term, being able to make money from that is what you need to stay alive in this very capital intensive race.

[Tarian Woods]

There is a sense in which all of this talk of business dominance and capital and competition might seem a little at odds with Demis Hassabas scientist Persona.

[Darian Woods]

He's always had that duality, right? He spent some of his time building video games which are kind of trivial compared to the grand ambitions of solving sc and he's got space in his head for both. Like you can't pigeonhole him. It's not detracting from his fascination with what is the nature of information, what is an emergent property and can we understand the fabric of reality?

[Waylon Wong]

And those lofty ambitions include that pronouncement to one day use AI to solve all diseases.

[Tarian Woods]

The goal of one day solving all disease? Does that ring as completely absurd to you?

[Darian Woods]

He likes to make these very sweeping claims and all disease feels a bit much. But I do think that if AI is a super tool with which to sort and make sense of information, you should be able to understand the origin of disease quite minutely through very advanced AI.

[Tarian Woods]

Are you sold? Waylon?

[Waylon Wong]

Yeah, maybe advanced AI could explain this to me over a beer.

[Tarian Woods]

AI is pretty good at explaining AI. It knows itself well. This episode was produced by Julia Ritchie with engineering by Travis Hagan. It was fact checked by Sarah Juarez. Cake and Cannon edits the show and the indicator is a production of npr.

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