# AI Regulatory Pressure and Infrastructure Financing Shifts

**Podcast:** The AI Engineer Podcast
**Published:** 2026-10-28

## Transcript

Welcome to the podcast.
The FTC is demanding that Anthropic executives testify in their AI consumer harm probe.
We've seen this in a lot of different industries with social media, and now it is moving into AI.
We have banks and Blackstone that are lining up $63 billion right now to fund Broadcom AI chips, and these are all going to Anthropic.
We also have OpenAI that says their internal model disproved the exponential time hypothesis.
This is my science corner that I'm excited to cover today on the show.
We have Verizon that is signing a $2 billion AI deal with AWS, and they're raising their 2026 outlook, which a lot of people are excited about.
OpenAI is launching GPT 6.1 Tera.
It's going to be half the price of GPT 6.1 Sol.
And there's a bunch of interesting reasons why that's going to make a difference.
Oracle has launched 25 Fusion Claw agentic apps across ERP.
HCM, Supply Chain, and CX.
So we're going to get into what Oracle is launching there and a couple other interesting things.
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All right, let's talk about what's going on with the FTC right now.
So they have demanded that Anthropik executives are going to come and testify for their probe.
The testimony request follows a 50-page document demand sent to Anthropic and OpenAI.
And of course, everyone knows this is happening right as Anthropic is pitching their investors on their IPO.
They've already filed for their IPO.
OpenAI said that they're not going to file till next year.
So we have these two different timelines going on right now.
Anthropic and OpenAI have both each received this 50-page civil investigation demand.
They're talking about a bunch of different things here.
One of them is security incidents.
One of them is safeguards.
One of them is marketing.
And one of them is privacy.
An FTC spokesperson said that individuals at other AI companies have also received testimony demands.
They didn't say what companies, but we know for sure we've got Anthropik and OpenAI that are requested to come and testify, and we probably have a bunch of others.
And I think there's a whole bunch of different things that we'll see here.
There's kind of the usual lawsuits that we've seen around like teen safety and different things like that.
But I think at the same time, we're also going to see, you know, when they talk about marketing, it makes me think of what some people have accused Anthropik of doing with marketing stunts where they'll...
go and find security vulnerabilities.
I mean, it's an interest.
It's so tricky because some of these security vulnerabilities are real.
The Entropic is discovering and they're telling people about, but at the same time, it's just, a lot of people have criticized them saying it comes off as wild that they discover security vulnerability that requires millions of dollars worth of tokens to go fix.
And then they tell all the companies and all the companies have to use their models to go in.
fix all the security vulnerabilities.
So they make money discovering them, make money from the patching.
But at the end of the day, people have bounty programs for these discoveries.
And these are some really big security issues that have probably existed for a very long time.
So, you know, obviously there's a lot of good, but it's going to be a pretty wild investigation to see how much of this is, you know, legit, everything that's going on.
Anyways, the demands went out the same day the Anthropic filed their IPO.
So it's definitely at a tense place for the company.
Banks and Blackstone right now are lining up $63 billion to fund Broadcom.
These are going to be chips that are going to Anthropic.
It's a new special purpose vehicle, an SPV, that is going to buy Broadcom XPU accelerators and networking gear, and it's going to basically lease them to Anthropic.
The deliveries are starting in 2027.
It's interesting because we have to get creative.
Anthropic's already raised so much money.
They're about to IPO.
Broadcom is obviously a...
a company that is very well known.
But even with a company like Broadcom and a company like Anthropic, making it just a clear deal for Anthropic to go acquire chips from Broadcom is sometimes tricky because of the way that these deals are structured.
And so we're able to do all sorts of...
creative financial things like spinning up new SPVs to go and actually acquire the chips and lease them.
So it's not like Anthropica is buying them directly from Broadcom for basically financial reasons.
Bank of America, Blackstone City, Morgan Stanley, all of them are creating this new SPV.
And Broadcom is going to give residual value support to it, which is equal to the first lien portion of the debt.
The way debt works, the way lending works, the way the terms work, this is kind of the best way for Anthropik to get its hands on these chips.
Deliveries are expected to start next year under the compute capacity agreement that Broadcom announced earlier.
Apollo and Blackstone previously agreed to finance a $35 billion extension to Anthropik's computing capacity.
And so now we kind of have these follow-on companies that are...
that are adding more to this.
All right, we have a big one.
This is a science corner for me, but basically OpenAI says that their internal model has disproved the exponential time hypothesis.
So I found a really good tweet over on X that explains the exponential time hypothesis, and I want to read it to you.
It's not too long, but I just feel like this is a really interesting problem that computer science has been grappling with for a super long time.
Anthrop OpenAI is able to solve it.
There's a bunch of really interesting implications.
So we had Greg Brockman that was actually tweeting about having this solved among a bunch of other ones, right?
So we had a whole bunch of, you know, math problems that were solved a while back by OpenAI.
And apparently there's a whole bunch of new ones that they've now just done.
So anyways, there is a really interesting tweet that went out all about this by Rebesh Karnawat.
And they said, many of you don't understand how serious this is, so let me break it down.
In computer science, there's a famous kind of puzzle called the 3SAT.
You're given a long list of yes or no switches and a set of rules about how they must be arranged.
And you have to find a combination that satisfies every rule.
The obvious way to solve it is to try every combination.
But each new switch that you try doubles the number of possibilities.
So a puzzle with a few hundred switches would take longer than the age of the universe to brute force, right?
So it's like, you know, here's a hundred switches, get them all in the right order, and, you know, you've got to guess the order.
And every time you switch one to test if it's the right order, it doubles and doubles and doubles.
Okay, so it's kind of been this problem where it's just like, it's impossible to solve because it's constantly doubling and you're never going to get it in the right order because it just keeps getting bigger and bigger.
For decades, the smartest people in the field found a whole bunch of really clever ways that made this a little bit faster.
But every trick still hit the same wall.
And that is that the time required kept exploding as the puzzle grew.
About 25 years ago, two computer scientists, Russell Impoglacio and Rohamo Patry, were working with Francis Zane.
And they turned that wall into a formal idea.
And that's what we call the exponential time hypothesis.
This is what they proposed, which basically says that this...
explosion is not a failure of human cleverness, but it's just a law of nature.
So they're like, look, no algorithm, no matter how smart it is, is ever going to be able to solve a 3SAT without the time blowing up exponentially.
Nobody could prove it, but almost everyone believed it.
And I think it became one of the foundations that a lot of modern computer science, it used it in a bunch of really weird ways, to be honest.
Researchers basically were using this as a measuring stick.
So if you could show that solving some other problem quickly could also crack 3SAT quickly, you had shown basically the problem was hopelessly hard.
Hundreds of papers, you know, things from scheduling, graphs, logic, a lot more, were all kind of sitting on the same assumption.
And so now, fast forward to today, an internal AI model, OpenAI says that they basically broke it.
They said that they found an algorithm that solves the puzzles.
a lot faster than the hypothesis allows.
And it has checked the proof in on lean, which is the famous computer verification system.
So the thing that is the most wild is that of all the people that have kind of the people that originally proposed this hypothesis, Impelagi Z and Patri, along with MIT's Ryan Williams, are they're basically now leading the public review of whether this has actually proved their own idea.
So they came up with this and now they're actually.
kind of checking the facts on it.
So basically what happens next is if the, if the disproof is accurate, then a huge chunk of a theoretical computer science has to be revisited.
And I know it's like, it's like theoretical computer science.
People kind of hate these problems sometimes because they're like, you know, they're like this problem that has no impact on the world is all hypothetical, you know, but there's actually some interesting things because basically because they were kind of using it as like a yardstick or a measurement for how hard like a problem was.
Every result of previous problems that said, you know, this problem can't be solved faster than assuming ETH, right?
They're like, you know, this problem we're currently trying to solve can't be solved any faster than that one.
Basically, all of those lose their foundation and researchers are going to have to work out which of those walls that they hit when trying to solve a problem were actually real walls and which of them just simply...
they just stopped looking because they thought it would take too long.
So I don't think it means that everything becomes easy, but basically the new algorithm is, it's still slow in absolute terms.
And I think it's not kind of the famous P versus NP problem being solved, but it means our map of what is possible and was wrong in a place that we were very confident about.
And if this is the case, then we have to redraw it.
So anyways, there's a bunch of interesting implications, but it's always impressive to me to see these new algorithms coming out of models like Open Ananthropic.
solving these hard problems.
I'm excited by OpenAI's kind of push towards solving some of these math problems, some of these like brain twisters, some of these things that we've thought about for a long time.
Because yeah, that's kind of what's exciting to me about science is making progress in these areas.
So we'll see what the actual implications are.
Maybe we revisit some problems that we previously thought were unsolvable, because it would just take way too long.
And we find new ways to solve them.
Because if an algorithm could be created for that, maybe an algorithm could be created for some of these other problems as well.
Okay, let's talk about what's going on at Verizon.
They just signed a $2 billion AI deal with AWS.
They're raising their 2026 outlook.
The AWS framework covers distributed compute for agentic and generative AI workloads.
And right now, Verizon is saying for a third quarter straight that they believe they're going to be making more money, earnings per share.
And so with this, the deal is basically scaling their distributed compute for agentic generative AI workloads.
They raised...
Their earnings per share guidance to $5.04 to $5.06.
This is a third straight guidance increase.
So Verizon believes they're going to be making more money with this.
Their Q revenue this year was $34.4 billion.
It's up $1.5.
And their adjusted earnings per share is $1.31, which is up 8.3%.
So Verizon added $406,000.
postpaid phone subscribers in the quarter, which means, you know, this Verizon is growing quickly.
And I'm excited to see some of the some of the big bets that they hopefully will take in AI.
I've actually interviewed people from Verizon on the show a few years ago from some of their AI labs and some of the projects they were working on.
So I'm excited to see them investing more in that and excited to see what those investments ultimately bring.
OpenAI has just launched GPT 6.1.
This is their newest model.
It's 6.1 Tera.
It's half the price of GPT 6.1 Sol, which is the mid-sized model.
It now costs $1 per million input tokens, and they're adding computer use and a one, replacing GPT 5.6 Tera.
I actually have personally been using 6.1 Tera for a bunch of different projects.
or sorry, 6.1 sold for a bunch of different projects.
So if, you know, 6.1 Terra is half the price, I could see that a lot of people would get a lot of really good usage out of this.
OpenAI said that 50.3% on Terminal Bench for a max effort is up from 21% for the previous model.
So it basically got twice as good on Terminal Bench, which is cool.
It's going to be inside of ChatGPT work and Codex.
for Plus Pro Business Enterprise and Education.
It's also going to be on the Enterprise and the AppCopilot.
I mean, obviously, they'll roll it out to all of those different places.
I'm excited about having it on ChatGPT work if it costs half as much.
When you get low on tokens and you need to go to a lower model, I'm always happy when they're able to do that for less.
Oracle has just launched 25 Fusion Claw eugentic apps across a bunch of different areas.
I'm excited to see Oracle making some big bets here.
I've heard a lot of rumors about projects that they're leading.
Talk to some people there about different things.
So I'm excited for the direction Oracle's going.
This project in particular was cool.
Fusion Claw is basically Oracle's execution runtime for longer running agent work.
And it has continuous replanning and deterministic execution.
So it could be halfway through a project.
It's like it's Open Claw, but it's like halfway through a project.
And then it will say, look, is the path I'm taking really necessary?
Or is it wrong?
And then it will replan what it's trying to do.
I think that's really cool.
Five of the top apps are on the Fusion Cloud CX.
So they have service health check and executive briefing documents.
They have a bunch of like cool little apps that they made basically.
Oracle Fusion Companion lets users work in Fusion applications through support AI tools like ChatGPT.
They didn't disclose the pricing for the applications, but they've created a bunch of new tools.
And honestly, whether they're, you know, frontier level or whether they're just good.
I'm excited to see Oracle jump in.
I mean, I want to see Apple get more aggressive in the space.
I want to see Oracle get more aggressive in the space.
I want to see Verizon get more aggressive in the space.
I want everyone to come up with foundational models and also just tools that build on top of it.
I think it's great for the overall industry when you have a lot of competition and a lot of people working on different projects.
So yeah, kudos to Oracle.
They are obviously raising a ton of money and making a ton of money from selling the hardware and the compute to OpenAI and everybody else.
But I think that there's a space for them to do some of the software, some of the algorithms, some of the AI as well.
So that's what I'll be excited to see them working on.
Thank you so much for tuning into the podcast, guys.
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All right.
Catch you all in the next episode.