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Mark Blyth

๐Ÿ‘ค Speaker
435 total appearances

Appearances Over Time

Podcast Appearances

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Is it possible to have a dual strategy of chasing AGI and superintelligence, as OpenAI very clearly are, and creating valuable products at the same time that can be used in everyday use? Or is that balance actually mutually exclusive?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

If I push you, if you think about your priority, your priority at OpenAI is, say, achieving superintelligence and AGI. Their best researchers, their best developers, the core of their budgets will go to that. When you have dual priorities, one takes the priority. And so there is that conflict.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

If I push you, if you think about your priority, your priority at OpenAI is, say, achieving superintelligence and AGI. Their best researchers, their best developers, the core of their budgets will go to that. When you have dual priorities, one takes the priority. And so there is that conflict.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

If I push you, if you think about your priority, your priority at OpenAI is, say, achieving superintelligence and AGI. Their best researchers, their best developers, the core of their budgets will go to that. When you have dual priorities, one takes the priority. And so there is that conflict.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

What did you mean when you said to me that AI companies should pivot from creating gods to building products?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

What did you mean when you said to me that AI companies should pivot from creating gods to building products?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

What did you mean when you said to me that AI companies should pivot from creating gods to building products?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Do you think it's even possible for companies to compete in any level of AGI pursuit? When you look at the players and the cash that they're willing to spend, you know, Zuck has committed $50 billion over the next three years. When you look at how much OpenAI has raised over the last three years and they carry on that run rate, it's something crazy like that.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Do you think it's even possible for companies to compete in any level of AGI pursuit? When you look at the players and the cash that they're willing to spend, you know, Zuck has committed $50 billion over the next three years. When you look at how much OpenAI has raised over the last three years and they carry on that run rate, it's something crazy like that.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

Do you think it's even possible for companies to compete in any level of AGI pursuit? When you look at the players and the cash that they're willing to spend, you know, Zuck has committed $50 billion over the next three years. When you look at how much OpenAI has raised over the last three years and they carry on that run rate, it's something crazy like that.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

It'd still be $38 billion short of a Zuck spend over a three-year period. Can you create AGI-like products or God-like products unless you are Google, Amazon, Apple, or Facebook?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

It'd still be $38 billion short of a Zuck spend over a three-year period. Can you create AGI-like products or God-like products unless you are Google, Amazon, Apple, or Facebook?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

It'd still be $38 billion short of a Zuck spend over a three-year period. Can you create AGI-like products or God-like products unless you are Google, Amazon, Apple, or Facebook?

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

With the commoditization of those models and the appreciation that value can be built on top of them, does that not go back to what I said, though, which is really there is three to four core models which are financed by cash cow cloud businesses. You know, the obvious says Amazon, there's Google. And then for Facebook, there's obviously Instagram and News Feed.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

With the commoditization of those models and the appreciation that value can be built on top of them, does that not go back to what I said, though, which is really there is three to four core models which are financed by cash cow cloud businesses. You know, the obvious says Amazon, there's Google. And then for Facebook, there's obviously Instagram and News Feed.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

With the commoditization of those models and the appreciation that value can be built on top of them, does that not go back to what I said, though, which is really there is three to four core models which are financed by cash cow cloud businesses. You know, the obvious says Amazon, there's Google. And then for Facebook, there's obviously Instagram and News Feed.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

And there are three large model providers which sit as the foundational model there. And then every bit of value is built on top of them.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

And there are three large model providers which sit as the foundational model there. And then every bit of value is built on top of them.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

And there are three large model providers which sit as the foundational model there. And then every bit of value is built on top of them.

The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
20VC: AI Scaling Myths: More Compute is not the Answer | The Core Bottlenecks in AI Today: Data, Algorithms and Compute | The Future of Models: Open vs Closed, Small vs Large with Arvind Narayanan, Professor of Computer Science @ Princeton

If you were, as you said at the beginning about kind of your work on policy, you have US regulators and European regulators, what would you put forward as the most proactive and effective policy for US and European regulation around AI and models?