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Vint Cerf, an American computer scientist, is widely regarded as one of the founders of the Internet. Since October 2005, he has served as Vice President and Chief Internet Evangelist at Google. Recently, he sat down with Google DeepMind’s Public Policy Director Nicklas Lundblad, for a conversation on AI, its relationship with the Internet, and how both may evolve. The interview took place with Vint in his office in Reston, Virginia, and Nicklas in the mountains of northern Sweden. Behind Vint was an image of the interplanetary Internet system – a fitting backdrop that soon found its way into the discussion.

I. The relationship between the Internet and AI

II. Hallucinations, understanding and world models

III. Density & connectivity in human vs silicon brains

IV. On quantum & consciousness

V: Adapting Internet protocols for AI agents

VI: Final reflections

The Coming of Agents
First thing’s first: eject the concept of a chatbot from your mind. Eject image generators, deepfakes, and the like. Eject social media algorithms. Eject the algorithm your insurance company uses to assess claims for fraud potential. I am not talking, especially, about any of those things.

Instead, I’m talking about agents. Simply put and in at least the near term, agents will be LLMs configured in such a way that they can plan, reason, and execute intellectual labor. They will be able to use, modify, and build software tools, obtain information from the internet, and communicate with both humans (using email, messaging apps, and chatbot interfaces) and with other agents. These abstract tasks do not constitute everything a knowledge worker does, but they constitute a very large fraction of what the average knowledge worker spends their day doing.

Agents are starting to work. They’re going to get much better. There are many reasons this is true, but the biggest one is the reinforcement learning-based approach OpenAI pioneered with their o1 models, and which every other player in the industry either has or is building. The most informative paper to read about how this broad approach works is DeepSeek’s r1 technical report.

GenAI is never going to disappear. The tools have their uses. But the economics do not and have not ever made sense, relative to the realities of the techonology. I have been writing about the dubious economics for a long time, since my August 2023 piece here on whether Generative AI would prove to be a dud. (My warnings about the technical limits, such as hallucinations and reasoning errors, go back to my 2001 book, The Algebraic Mind, and 1998 article in Cognitive Psychology).

The Future of AI is not GenAI
Importantly, though, GenAI is just one form of AI among the many that might be imagined. GenAI is an approach that is enormously popular, but one that is neither reliable nor particularly well-grounded in truth.

Different, yet-to-be-developed approaches, with a firmer connection to the world of symbolic AI (perhaps hybrid neurosymbolic models) might well prove to be vastly more valuable. I genuinely believe arguments from Stuart Russell and others that AI could someday be a trillion dollar annual market.

But unlocking that market will require something new: a different kind of AI that is reliable and trustworthy.

Google DeepMind CEO Demis Hassabis said that artificial general intelligence (AGI) will compete with human competence in the next five to 10 years, and that it will “exhibit all the complicated capabilities” people have. This could escalate worries over job implications around AI—which is already in motion at companies like Klarna and Workday.

What your coworker looks like is expected to change in the very near future. Instead of humans huddled in office cubicles, people will be working alongside digital colleagues. That’s because Google DeepMind CEO Demis Hassabis said AI will catch up to human capabilities in just a few years—not decades.

“Today’s [AI] systems, they’re very passive, but there’s still a lot of things they can’t do,” Hassabis said during a briefing at Deepmind’s London headquarters on Monday. “But over the next five to 10 years, a lot of those capabilities will start coming to the fore and we’ll start moving towards what we call artificial general intelligence.”

But there is no such thing as a newspaper, a magazine, a TV news channel or even a news website anymore. There is only the Web. If you want to live there, you must build a community within it.

That means doing something I hate, namely specializing. It also means creating a two-way street, like Facebook without the sludge. A safe place for locals to not only vent but connect, emphasis on the word SAFE. You’re about as safe on Facebook as you are on an unlit alleyway behind a strip club after midnight on a weekend.

Once you build a community, you can build another, but it won’t be any cheaper than the first one was. Doing this takes deep learning, expertise, and a desire to serve. The best publishers have always identified with their readers, sometimes to a ridiculous degree. Their business is creating =communities around shared needs, through unbiased journalism and a clear delineation between advertising and editorial.

In a world with over five million podcasts, Dwarkesh Patel stands out as an unexpected trailblazer. At just 23 years old, he has caught the attention of influential figures such as Jeff Bezos, Noah Smith, Nat Friedman, and Tyler Cowen, who have all praised his interviews — the latter describing Patel as “highly rated but still underrated!” Through his podcast, he has created a platform that draws in some of the most influential minds of our time, from tech moguls to AI pioneers.

But of all the noteworthy parts of Patel’s journey to acclaim, one thing stands out among the rest: just how deeply he will go on any given topic.

“If I do an AI interview where I’m interviewing Demis [Hassabis], CEO of DeepMind, I’ll probably have read most of DeepMind’s papers from the last couple of years. I’ve literally talked to a dozen AI researchers in preparation for that interview — just weeks and weeks of teaching myself about [everything].”

On my AI courses, I don’t just teach how to build AI; I emphasize understanding what it is. Most importantly, I explore the what and the why. My goal is to leave no stone unturned in the minds of my students and executives, fostering a comprehensive awareness of AI’s potential and its pitfalls.

Crucially, this involves cultivating widespread AI literacy, empowering individuals to responsibly understand, build, and engage with these transformative technologies. Our exploration centers on developing applications that enhance societal well-being, moving beyond the pursuit of mere profit. My AI app for a major bank, designed to assist individuals with vision impairment, exemplifies this philosophy.

This focus on ethical development and human-centered design underscores my conviction that the future of AI depends on our ability to move beyond simplistic narratives and embrace a nuanced understanding of its potential. Whatever we may think of AI, and I have many conflicting thoughts, it is certain that it will foretell our future, so we must learn to shape it and rebuild our humane qualities.