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In Machines we Trust

Trusting AI: Analyzing Major Funding Deals

In Machines we Trust

In Machines we Trust

Technology

4.36 Ratings

🗓️ 4 May 2026

⏱️ 20 minutes

🧾️ Download transcript

Summary

In this episode, we analyze the trust factors behind the major funding deals of OpenAI and Anthropic. Explore how trust can influence future AI developments.


Chapters
00:00 Introduction to AI Deals
01:59 Greg Brockman's AI Insights
04:19 Launch of Humane's AI System
07:20 Morgan Stanley's Spending Forecast
12:41 OpenAI and Anthropic's Strategies
16:38 Conclusion and Future Predictions


Show Links
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Transcript

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0:00.0

Open AI and Anthropic, the two biggest AI companies in the entire world.

0:04.4

They both signed a multi-billion dollar private equity deals, and they did it on the exact same day.

0:08.9

The structure of how those deals happened is different, though.

0:12.0

So I think that's going to be really interesting when it comes to what's going on with AI and enterprise.

0:16.6

Also, Greg Brockman says that AI went from writing 20% to 80% of all OpenAI's code in one month.

0:24.1

Saudi Arabia's Humane just launched on AWS, and it is calling it the first enterprise-grade agentic AI operating system.

0:31.5

And Morgan Stanley just bumped the 26 hyperscaler forecast to $805 billion.

0:39.3

The first thing I want to break down, though, is Greg Brockman's.

0:42.3

He went on a podcast recently and he had all these wild quotes, including talking about AI.

0:47.3

He said, quote, even over the course of December, we went from these agentic coding tools, writing 20% of your code to writing 80% of your code.

0:55.5

This is a 4x jump in one single month.

0:58.5

And he also said that OpenAI is 70 to 80% of the way to AGI.

1:04.2

He also agrees with Sam and Demise over at Google DeepMind and says that we're maybe two

1:09.8

breakthroughs away.

1:11.0

Now, of course, this wasn't everybody that agreed with him over on ex-Yan Lekun,

1:15.6

predictably has been dunking on the AGI claim.

1:18.7

He's been basically doing this all week.

1:20.2

You also have Andre Carpathie, who was a lot more, I guess you could say like measured,

1:24.2

but basically the thing that I've heard him talk a lot about is he says

1:27.8

that I'm sure some of these coding writing percentages are like huge, right, going from 20 to 80

1:33.7

percent. But what he said is if you measure tokens emitted versus what is so, and that's kind of

1:41.3

where you're getting this 20 to 80 percent jump, but he says the difference is and a harder question is what fraction of all of these tokens, all of this and that's kind of where you're getting this 20 to 80% jump but he says the difference is

...

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