# AI Market Shifts: Cyber Risks, Neoclouds, and IPOs

**Podcast:** The AI Daily Brief (Formerly The AI Breakdown): Artificial Intelligence News and Analysis
**Published:** 2026-10-26

## Transcript

Last week was one of those weeks where it feels like everything in AI just ratcheted up another level.
Coming into this week, we have a set of huge questions that are dominating the conversation.
From whether data center funding has dried up, to how we're going to handle the latest Chinese open-weight models, to whether this cyber apocalypse is now just with us forever, to how difficult it's going to be for Anthropic to get this IPO out of the door.
Today we're talking about the four big questions that are shaping AI this week.
The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI.
All right, friends, quick announcements before we dive in.
First of all, thank you to today's sponsors, KPMG, C-Data, Granola, and Blitzy.
To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe on Apple Podcasts.
If you want to learn more about sponsoring the show, send us a note at sponsors at aidailybrief.ai.
And lastly today, I am super excited to share that I have launched a new podcast.
You can think about this one as sort of a spiritual sibling to AIDB, but one who gets to get away from the grind of the daily and instead sit back and go bigger because we're doing it weekly.
The show is called How We Got Here, and it's a show about how modern important trends and phenomenon came to be.
To give you a flavor, the first episode is about America's 15-year history falling out of love with the tech industry and how that sets up the data center debate.
But then I've got future episodes coming about how stuff got cheap while life got expensive and prediction markets and GLP-1, so pretty much anything is fair game.
I tend to think that a lot of what we feel is inevitable is actually just a series of decisions that were made by people.
And if that's true and we want to change things, we can make different decisions in the future, but only if we understand, well, how we got here.
That show is live now.
You can find it on Apple or Spotify.
And you can get all the links you need either in the show notes of this show or go to howwegothere.com.
I would so appreciate it if you would take a moment to listen, and then if you like it, rate and review the show.
This kickoff week is where it matters most, and I think a lot of you guys are really going to like it.
Thank you in advance for checking it out.
But now, let's get into the big questions for AI this week.
Last week was one of those real heightened stakes weeks, and we almost have archetypes of these weeks in AI.
There are the everyone's freaking out about a bubble weeks, the new models are coming so fast that it's head-spinning weeks, and then there was the type of week that we had last week.
which is the most subtle but no less knowable, which is the, oh man, it just feels like the stakes of everything are getting higher kind of weeks.
We had cyber attacks, market jitters, a continued and growing recognition of the power of the most recent models, particularly Opus 5.5.
We're kicking it off with the four questions that I think are shaping the conversation heading into what's to follow.
The theme of the first set of questions is about models, and specifically, how we respond to the latest Chinese models, and what it says about our larger sense of the geopolitical stakes of the race.
But to understand that question, we gotta go back to Friday, when Moonshot released their latest version of Kimi, Kimi K3.1 Pro.
It's their first model update for the K3 family.
Now you might remember that when Kimi K3 was released in July, the media presented it as an open-source alternative to Fable.
K3 was the first ultra-large open model, weighing in at 2.8 trillion parameters.
It showed some pretty big advances in what you could do with it.
And it happened to come at the exact perfect moment when Fable was offline because of the export control issues with the White House.
As always happens, though, whenever there is a new Chinese model, everyone loses their mind about having closed the gap with the U.S.
closed models, the benefit of time tends to calm things down a bit.
Ultimately, beyond a handful of specific use cases like front-end web design, K3 never fully lived up to the hype.
Since then, new releases from OpenAI and Anthropic pushed the frontier forward and opened what has perceived to be a wider gap with the leading models from China.
On top of that, while American open-weight models have not yet gotten up to that Kimi K3 sort of level, they are for the first time in a long time starting to nip on the heels of their Chinese counterparts, meaning in short that the whole conversation around open-weight models has changed quite a bit since the summer when K3 was first released.
And yet, with the release of K3.1 Pro, we're once again seeing the comparisons to Fable and Anthropics' mid-sized powerhouse Opus 5.5.
Moonshot provided an extensive set of benchmarks for comparison, which placed K3.1 clearly ahead of Chinese peers and in the mix with the best models from the West.
On GDP Val AA, which measures agentic white-collar work, K3.1 was just behind the leader Opus 5.5 and ahead of Fable 5.1.
Likewise, on Terminal Bench 4.0, which measures agentic coding, K3 was once again less than a point behind Opus 5.5 and around 7 points ahead of GPT-6 Astra and Fable 5.1.
Terminal Bench 4.0 is notable as it is comparably not saturated and has in the past shown a clear performance difference between the latest generation of Western models and Chinese Open models.
DeepSeq V41 Flash and GLM 5.3 only managed scores in the low 40s, giving them a 15-point gap with the Western Frontier.
Moonshot emphasized cost as the big differentiator for K3.1.
When K3 was released over the summer, it wasn't a particularly cheap model, costing around the same as GPT-56 Sol for similar benchmark performance.
And despite there being lingering perception that every Chinese model that comes out is pennies on the dollar, in practice, without the cost advantage that Chinese models had been known for, there just wasn't a hugely compelling reason for most people to make the switch.
Moonshot is pricing K3.1 at around two-thirds the cost of K3 on a per-token basis, giving it the same price as Sonnet-55 and GPT-6 Sol.
That's still almost 10 times the per-token cost of DeepSeq 4.1 Flash, and about twice the price of GLM-5.3, but it now sits at least comparable with some Western alternatives.
What's more, on TerminalBench 4.0, Moonshot suggested that K3.1 was far cheaper on a cost-per-task basis than all major rivals.
It cost around $5 per task, compared to around $15 for Opus 5.5, and $10 for GPT-6 Astra on max settings.
Artificial analysis confirmed extremely solid benchmark performance in their independent testing.
K3.1 scored 55 on the Intelligence Index, ranking fourth behind Opus 5.5, Sonnet 5.5, and Muse Wonderful.
K3.1 was two points ahead of Fable 5.1, GPT-6 Astra, and Gemini 4 Argon, which are all now tied for fifth place.
Artificial Analysis wrote, Once the weights for K3.1 Pro are released, it will be the highest-scoring open-weights model, and the gap between the leading open-weights and proprietary models on the Intelligence Index will narrow from 12 points to 3.
They found that K3.1 uses 14% more tokens per task than K3.
However, because of the price reduction, it's slightly cheaper than its predecessor.
Now, the first reaction was to be pretty impressed with the benchmarks, if recognizing that this is perhaps a movie that we've seen before.
Chubby on X wrote, Just to be very clear and that everyone understands this, today we got Fable 5.1 Intelligence as a local model.
I remember the GLM founder saying in a post that he expects Fable 5 capabilities by the end of the year from China.
Well, it happened, with all its consequences.
Just think about it.
Ramez Nam from the Singularity University wrote, Based on this, Kimi K 3.1 Pro is around five weeks behind the U.S.
frontier.
That's the shortest the open-slash-closed AI model gap has ever been.
Yet unfortunately for Kimi, pretty soon all of the conversation shifted away from the model itself and towards how they were promoting it.
Elshayib on X posted a bunch of communications from the Kimi team about how they were running their influencer campaign.
Of note was the fact that they explicitly instructed those influencers to not put the paid partnership label on their posts.
Now, they also had a whole bunch of narrative instructions, like wanting those influencers to call it Opus 5.5 but five times cheaper, and focus on this narrowing gap between open source and Frontier, as well as explicitly trash-talking Astra and Opus.
But still, even with that, it's the non-disclosure thing that people were really upset about.
Chubby again summed it up, writing, This is such a shame and so sad to see.
Moonshot ruined their launch themselves.
K3.1 Pro is a great model, and it did not need people to fake good reviews.
But since they were trying to cheat the game, it makes the whole launch look bad.
Open model researcher Nathan Lambert is willing to extend a little grace, saying that there are still some cultural norms and communication issues that could have led to this type of mistake.
He wrote, This is a mistake, but I suspect they will correct.
Chinese labs are very eager to influence the Western ecosystem in X, so they likely took a step too far here.
They follow the vibes closely and I bet we'll understand, even if some cultural differences cause this.
Given the new questions, though, some people went back to more aggressively test the claims and found the model a bit lacking.
Future forward editor Alex called Kimmy K 3.1 the most Benchmax model ever released.
I think when it comes to model performance with this one, we're going to have to wait and see how it shakes out.
We have not even yet had a full business day with people using this thing, and given the dust-up around the strategy, everything is a little bit colored right now.
And going back to the idea of questions that will shape the week, If the numbers actually hold up in anywhere close to what the benchmarks suggest, how is the U.S.
policy structure going to handle an open-weight model of this level of capability?
One note from the release was that Moonshot said that they deliberately nerfed the model when it comes to cybersecurity tasks.
They wrote, We aim not to release open-weights that would extend the absolute frontier of cyber capability.
K3.1 Pro's offensive cyber capability is, by design, below what our pipeline can produce, and remains behind that of frontier proprietary models.
Basically, during their post-training, Moonshot found that cyber capabilities were scaling, so they deliberately removed cyber-related data from their training pipeline.
Moonshot explained that they tried to strike a balance, retaining capabilities for general-purpose coding, and making reasonable levels of cyber performance more widely available.
External testing from artificial analysis found that K3-1 is the most capable cyber model available to the general public.
It scored 64% on their cyber index, 11 percentage points ahead of DeepSeek v4.1 Flash and only 5 percentage points behind the trusted version of GPT-6 Sol available through their partner program, Daybreak Blue.
Eli Bakaush from Prime Intellect explained why this was a positive sign.
He wrote, One point that was under-discussed, in my opinion, is that it's the first open lab that is pacing the frontier of open-weight cyber capabilities, which makes it much easier and creates incentives for other labs to make the right decision and do the same when they reach the same level of capabilities.
The remaining question is how to set the right level.
Now, my sense is that K3-1 is not going to be the only model we're talking about this week, but let's pick up on the cyber theme to move to our next question.
Is cyber chaos, just for the time being, the new normal?
Last week, we got a PHP bug.
that purportedly threatened 70% of all websites.
On Thursday, after the dust had started to settle, Anthropics Deputy Chief Security Officer Jason D.
Clinton wrote, As bad as things have gotten in the past four days, they are going to get a lot worse before they get better.
Security leaders never let a crisis go to waste.
Please go to your boards and ask for help right now.
Not for AI, for the basics.
We need to hold on for around 18 months.
A lot of folks responded saying, hey man, you guys caused this.
Hina Ansari wrote, You and your cohorts created this monster.
Now you want us to help you clean it up.
Clinton responded crisply, Quote, It was always going to be this way.
We tried to warn the world.
I certainly don't think that from an efficacy standpoint, the blame game is particularly useful.
AI models capable of detecting and exploding vulnerabilities are clearly here.
And before society wants to decide how to apportion blame, the much more important question is how long the cyber chaos will persist and how we can help address it.
Cloudflare CEO Matthew Prince believes there's much more to come, telling Reuters he was aware of, quote, a bunch of additional vulnerabilities that are present in other pieces of critical software.
He predicts a busy few months while the world hardens the core technology that underpins the internet, adding, I think we're going to have a couple years where every single week it's going to feel like the sky is falling.
Indeed, in last week's disclosure post, OpenAI flagged that more would be coming.
So we could be in for a Black October that stretches on for several months, if not years.
Sam Altman, for his part, doesn't seem that optimistic about a swift resolution.
In an interview, he said, I don't even know a dramatic enough word to use.
Models can find bugs that are outside of the normal threat model of what we think a hacker can do.
Hardware level, insane stuff.
I no longer think we're going to be able to just patch all the bugs.
A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes.
Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs.
These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI.
Learn more about what separates AI amplifiers from everyone else at kpmg.com slash us slash AI amplifiers.
This episode is brought to you by C-Data.
Companies rolling out AI keep hitting the same wall.
Token spend is climbing, security is holding agents back, and no one knows how AI is truly being used.
The root cause?
AI is cut off from the systems and context your business runs on.
That's why C-Data built Connect AI Gateway, one control point between your AI and your systems.
Built on the connectivity layer powering Palantir, Google, Salesforce, and hundreds more, each agent gets exactly the data and tools it needs.
It governs down to the record and lets you finally see your org's complete AI usage in one place.
Accurate answers, a fraction of the token cost, and a gateway that learns your business with every interaction.
See how at cdata.ai.
You're in back-to-back meetings all day.
You're trying to stay present, but you're also worried you'll forget the decision, the action item, the important next step.
This is a scenario that we absolutely live here at the AI Daily Brief, given how fast things move and change and how quickly we're evolving everything that we do.
That's where Granola comes in.
Granola is an AI-powered notepad for meetings.
You jot down rough notes like you always do, and in the background, Granola transcribes and turns them into clear, useful notes when the meeting ends.
No bots joining your calls, no distractions, just a clean notepad that helps you focus.
During or after the call, you can chat with your notes, ask Granola to pull out action items, help you negotiate, write a follow-up email, or even coach you using recipes which are pre-made prompts.
Once you try it on a first meeting, it is very hard to go without.
Head to granola.ai slash ai daily and use code ai daily to get 100% off for three months for new users.
That's granola.ai slash ai daily and code ai daily to get 100% off for new users for three months.
Every AI coding tool on the market does the same thing first.
It starts writing code.
Blitzy does the opposite.
Before writing a single line, Blitzy spends days reverse engineering your entire code base.
Thousands of agents ingest millions of lines, mapping every dependency, every undocumented constraint, every architectural decision made over the last decade.
The result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building.
Other tools guess at context with grep searches and markdown files.
Blitzy never guesses.
It builds true understanding first, then delivers over 80% of entire software epics autonomously.
So how are people responding to this?
Well, first of all, we are very much going to be living in the fog of war, where critical thinking and skepticism is going to be extremely important.
You have all sorts of viral posts all over social media right now.
from anonymous accounts claiming to be inside labs or inside the NSA, talking about all these things going on behind the scenes that are being hid from the public.
And while it's of course not impossible that that's true, I do not think that that's a particularly useful set of posts to be engaging with.
More interesting to me are practically the people who are discussing how we shift to harden these systems as fast as actually possible.
Box's Aaron Levy wrote, Agent Swarms for Cyber Defense is about to be one of the biggest categories in AI.
We're going to have agents reviewing our code and infra, testing every permutation of risk, processing gobs of data, and more.
Security is one of the best use cases for swarms because it's an obvious area where more compute in equals more value out.
There's a wide range of testing that people can never get around to, or at least quickly enough, that agents have no problem going after.
And some think that this doesn't end well for the free and open internet.
Economics writer Noah Smith wrote, The proliferation of AI cyber attacks may signal the end of the internet as a public good.
We may only be able to use it in the future if everyone shells out big bucks for private security.
That will, he points out somewhat obviously, if that thesis is correct.
reduce the internet's economic value.
And yet already you are seeing teams posting the templates for how they're trying to get out ahead of this.
Alan Wells from Rocketable said, just spent the last seven days on the front lines of the vuln apocalypse fending off an active cyber attack on one of our products.
He then followed and shared a bunch of thoughts, including how to be working on hardening your systems even if you aren't a huge team.
Now, in terms of what all this cyber stuff means for new models, all we have to go on is what we're getting out of the labs.
OpenAI has announced that they'll resume training Frontier AI models, but will isolate them from the open internet.
In a Friday blog post, OpenAI wrote that they'd, quote, built an offline world, a vast set of indexed and cached web content in which our models can interact without risk of affecting external systems.
Moving forward, all of their Frontier reinforcement learning runs will use this quarantined mode of interaction.
This news mirrors a decision made by Anthropic earlier in the month when they announced that they had also resumed Frontier training.
On the one hand, if this works, it means that Frontier model training can continue, hopefully without endless reports of rogue agents escaping from the frontier labs.
However, it also means that neither of the big labs has much confidence in their ability to constrain their models using alignment research or other methods, needing the more blunt instrument of cutting off access to the internet.
And honestly, even the air gap setup seems like it might not be enough.
OpenAI wrote, In one experiment, we asked agents in separate micro VMs, created and managed by OpenAI sandboxing software on shared host machines, to exchange synthetic secrets without the use of network connections or shared writable files.
something that our systems are meant to prevent.
The agents found a way to communicate using the processor's cache.
Shipped Together co-founder Wise wrote, OpenAI is now building a fake internet for its AI agents to interact with.
The idea is to keep them isolated from the real internet.
I'm not sure if this is going to work.
So in fact, the better question might not be, is cyber chaos the new normal?
But instead might be, are we reaching the point, officially, where the most advanced frontier models are simply too dangerous to release.
Not because they are paperclip maximizers who are going to reduce all the atoms in our human bodies to material that helps them achieve some impersonal goal, but that they're just, at the moment, too good at breaking into our very human-created systems.
For our next question that'll shape this week, let's move over to markets, where people are asking, is data center funding drying up?
Neoclouds are feeling the squeeze from two secular trends turning against them at the same time.
The first is debt markets turning sour on data center funding, demanding a huge and growing premium for new fundraising.
The second is large AI consumers beginning to stand up their own data centers.
According to Ramp Data, more than a third of AI spend from the top 1% of firms is now going towards direct GPU rentals rather than calling models over the API.
Investor Chamath Palahapitiya said that he's already seeing a change in behavior on the ground.
He commented, ever since Palantir's Alex Karp's commentary on CNBC about data sovereignty, Buying patterns have changed, and I think people are being a lot more methodical.
They are building out their own data centers, or at least moving into VPCs, virtual public clouds.
My team yesterday was at the CIO event of, let's say, one of the largest banks in America.
It was the most incredible thing.
They said, we have decreased our spending on AWS and Google Cloud.
We now have six completely independent ground-up data centers that we've built from scratch for ourselves.
Over the weekend, meanwhile, that narrative advanced.
Yuchin Jin said that he heard that AI Neo Labs are betting on the rising cost of compute as a backup plan to building their own models.
His friend apparently said, Elon Musk, however, responded, Later in the weekend, Chamath again explained what he's seeing on the infrastructure side building on similar themes.
First up, he wrote, Neoclouds are disorganized and poorly capitalized.
Some are definitely better than others, however.
He explained that hyperscaler-backed projects are facing delays from a combination of bureaucracy, energy supply difficulties, and the data center backlash.
This could lead to neoclouds with partially stranded compute and an inference shortage hitting Anthropic and OpenAI as the hyperscalers focus on their own needs.
Chamath added, This dislocation will benefit two kinds of orgs, the non-bureaucratic big scalers who can build industrial scale quickly, and the nimble, well-capitalized players feeding them.
Elon Musk again responded, pretty much.
That means the 200-plus neoclouds are going to see guppy-level death and consolidation.
Some will make it through.
but not many.
Now, Bloomberg reported that this $30 billion deal is actually not quite closed, with Apollo and PIMCO as key investors.
In particular, the deal is not yet priced.
Interest rates in the mid-8% range were originally on the table, but with some neocloud debt heading into junk territory in recent weeks, sources said potential investors are going back to the drawing board.
Now, one type of response took Elon's comments very literally.
Finance A Lot wrote, Elon Musk just told the entire world that the AI bubble has popped.
Buckle up.
Boring business added, Elon Musk is literally telling you that most neoclouds will die or get consolidated and you are still bullish.
But then others pointed out that this is not exactly coming from an unbiased or uninterested source.
Just Another Pod Guy writes, Elon has every incentive to talk crap and worsen access to capital for his primary competitors, i.e.
neoclouds, while pumping his own roadmap to improve his purchase power when Tesla is merged into SpaceX.
Said Lisan Al-Gaib, the sharks are smelling blood in the water.
Wouldn't it be nice if a few neoclouds blew up and Elon could gobble them all up at a discount?
Ghostload reflected an entire strand of discourse, basically arguing that Elon is trying to pull up the ladder after SpaceX got out in the IPO market, writing, Elon popping the AI bubble after front-running the Anthropic and OpenAI IPOs is like watching an episode of Billions in Succession.
How the market digests this new strand of conversation is going to be one of the most interesting things to watch in the week to come.
And of course, that is going to be entirely wrapped up with our fourth question.
which is really just a proxy question for everything surrounding the Anthropic IPO and Frontier Lab funding.
All throughout last week, there had been speculation that Anthropic needed to make their prospectus public by Friday if they wanted to hit their reported target of IPO-ing the week of Thanksgiving.
Now, that prospectus did drop this morning as I was starting to record this show, and tomorrow we are going to dig into anything new that we learned as folks have a day to digest it.
Heading into that, though, it seems like some of the biggest questions are around IPO pricing.
According to reports from late last week, OpenAI's next round of fundraising is getting close at a valuation of $1.25 trillion, which some are taking as a softening, as previous reports had said that they had been looking at $1.4 trillion.
Neither number is anywhere close to the $2 trillion number that some Anthropic investors were looking for, and it makes it more likely that we'll see the price trim to $1.5 trillion as a separate group of investors were calling for.
We still haven't seen a number attributed to Anthropic or their investment bankers, but it certainly no longer feels like Anthropic is IPO-ing into strength.
Of course, the sentiment could turn around immediately once Anthropic releases Fable 5.5.
Rumormonger ChrisGPT is suggesting the new model is already deployed for top customers, including JPMorgan and Meta, and while I'm certainly taking that with a grain of salt, as I have said publicly, it's pretty clear to me that Duet as their personal agent, and Fable 5.5 as their world-leading model, feels like the inevitable one-two punch for them to claim status as definitively the top dog heading into that IPO.
That top dog status, though, is getting more interesting itself.
The information reports that OpenAI's price cuts have helped them take market share from Anthropic over recent months.
One example they gave was that Rippling, who was using Anthropic models for 57% of their coding in May, with OpenAI handling a much smaller 16%, had now seen that ratio flipped, with OpenAI now powering 52% of coding workflows to Anthropic's 18%.
Then again, that reporting might also be a slightly outdated view of the market.
Opus 5.5, released a little over a month ago, seems to have kickstarted a resurgence for Anthropic.
Open Router reports that the 7-day average token volume for Anthropic models is up 3.6x since the launch of Opus 5.5.
And it's not just that model either.
Both Sonnet 5.5 and Haiku 5.5 have added meaningful token volume to reinforce Anthropic's gain in market share.
Ramp data shows that Opus 5.5 has grabbed 35.1% of token share and 34.4% of business spend, the most successful first three weeks for any model release.
Consumption will need to stay strong for a few more weeks to match the incredible two-month run for Opus 4.6.
but the signs are there that this could be Anthropic's most commercially successful model ever.
Andrew Curran summed it up, Now here's one interesting little speculation that I haven't seen anyone else talking about.
It seems to me that this might be the one moment in perhaps the entire history of these two labs where OpenAI has a strong incentive to very temporarily not release a model that competes with Anthropic.
Let's say you're OpenAI.
No matter what, Anthropic's IPO is going out before yours.
Given that, what's a better scenario for you?
Option one, Anthropic going gangbusters, re-exciting the markets, and pushing off these bubble concerns for another couple of quarters.
Or option two, Anthropic's IPO underperforming and signaling to the rest of the market that yes, indeed, the AI bubble has burst and that they should pull back further.
The former scenario is not without its challenges, i.e.
you might have to battle for excitement from a definitive second-place position, but the scenario if the latter comes to pass is an absolute disaster that might literally not make an IPO available to you anymore.
So then, if you think the disaster scenario is in fact Anthropik's IPO failing, you don't want to do anything that could help contribute to that failure.
And so letting Anthropik, just for a few weeks, have the absolute definitive best model in the world...
might weirdly be in OpenAI's interests.
As you can see, friends, the questions surrounding AI this week are numerous, varied, and very interesting.
For now, however, that is going to do it for today's AI Daily Brief.
Appreciate you listening or watching, as always, and until next time, peace.