Grok 4.6 Shows How Fast Your AI Options Are Expanding

A year ago, if you were talking about frontier models, pretty much you were referring to a model from one of either open AI and

Thropic or Google. By a couple of months ago, you were probably referring to a model just from either open AI or

anthropic. Now however, things have changed. Over the past couple of months, any conversation about model performance

has to include a recognition of Chinese open weight models that are pushing the frontier of both efficiency and cost.

And as of this week, SpaceX AI's GROC is back in the conversation.

The just released GROC 4.6 is putting up benchmark numbers that put it in the category of a GPT 5.6 or a Fable 5

and doing so at a fraction of the cost. Although of course, as we know, AI in the benchmarks tends to be very different than AI in the real world.

After some initial testing, while users are not ready to declare GROC 4.6 a Fable or GPT class model yet,

they are ready to argue fairly definitively that GROC and SpaceX AI are back in the race.

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It's organized on both an episode and a card basis and we'll be continuing to try to improve it as time goes on.

Now with that out of the way, let's get to the headlines which are all about big money and into the change in the model landscape

that's the subject of our main episode. Welcome back to the AI Daily Brief headlines edition, all the daily AI news you need in around five minutes,

and the theme of today is big money. Cognition is seeking another funding round on the back of booming coding agent demand.

Bloomberg reports the cognition is in early talks with investors for new funding at evaluation of $40 billion.

Cognition closed their last round just three months ago, raising a billion dollars at a $26 billion valuation.

For those doing the quick math, that means that the company's valuation would be up almost 50% in a quarter.

And the revenue figures seem to back it up. Sources familiar with the fundraising effort said cognition has doubled their revenue run rate to a billion dollars since they were last seeking funding.

One source said that cognition is seeking a billion dollars in this round, giving themselves a substantial increase in resources to address the current agent boom.

The numbers also imply that the premium attached to coding agents is growing among venture investors.

Cursor is one of the closest comps and their last fundraising round in March saw them seeking a $50 billion valuation on two billion in annualized revenue.

That round of course ended with SpaceX acquiring the company in a $60 billion all stock deal.

And honestly if cognition has the ability to price their round at $40 billion, the SpaceX deal could start to look like a bargain.

Many think that the path that cursor took with SpaceX feels inevitable for cognition as well.

Wright's Richard Wu, I wouldn't be surprised if within the next six to 12 months we see one of the hyperscalers preempt cognition and offer to acquire them for $60 to $100 billion in stock.

Given the success with SpaceX acquiring cursor, the boards of these companies will put pressure on them to make a move.

Jeff Wu says Google should buy cognition for 200 billion and make Scott Wu CEO.

Sun deep from cognition responded, we aren't selling. Also 200 billion, the stock would move three times that in after hours alone.

Next up, we have Lovable who announced their $400 billion Series Sea round at a 13.3 billion dollar valuation.

What's interesting is that you can clearly see how Lovable is evolving just in the way that they describe themselves in their fundraising announcement.

In short, Lovable feels to me to be inching farther away from clawed code and closer towards something like Shopify.

They write, Lovable is building the software creation platform that gives those closest to a problem the power to solve it.

A generational opportunity that spans billions of people all over the world.

For most people, turning an idea into software once required so much capital, technical fluency, and time that many ideas never came to life.

Lovable's first chapter was about changing that.

Since our Series B in December 2025, we've been building features people need to reach customers, manage day to day operations, and run software securely.

For many builders, the product they create with Lovable is becoming the business itself.

User survey data shows us that nearly 8 and 10 are building a business or side project they hook to monetize, and more than one third of those are already earning revenue.

In CEO, CEO, Antoine Oseko's post, he absolutely emphasizes the same idea, saying that Lovable will create, quote, the most intuitive platform to build and run a business.

If you are looking for a place to see the intersection of where what was once called vibe coding meets the actual transformation of small and digital businesses, look no further than Lovable.

Now moving into public markets, businesses booming for the Neo Clouds as AI demand continues to rise.

This week saw CoreWeave and Nebius report earnings, both vastly outstripping analyst expectations.

On Tuesday night, CoreWeave reported that revenue had doubled over the past year to reach 2.6 billion for the quarter.

At the same time, Cashburn also doubled, now running at 5.7 billion per quarter.

Still, the big story for investors was a line out the door for compute.

CoreWeave reported a $104 billion backlog in demand.

In the footnotes, they added that the backlog had grown by 25 billion since they closed their books at the end of June.

The story was the same for Nebius who reported on Wednesday.

They recorded 454% revenue growth over the past year to reach 582 million.

Their Cashburn is also escalating rapidly.

But like CoreWeave, Nebius has endless demand.

With CEO Arcade Veloz telling investors, demand for what we are building continues to be enormous.

We could sell today our entire 2027 capacity if we wanted.

Supply is in fact so tight that Nebius is seeing huge profits on their available capacity.

Earnings per share beat analysts forecast by 83%.

Veloz told investors that their auctions for blackwell compute which began in Q2

cleared at 15% above their previous record price for hopper compute.

Markets rewarded both stocks with CoreWeave up 19% since reporting and Nebius gaining a staggering 34%.

Analysts believe that neoclads are some of the best indicators of marginal demand for AI as they

service the overflow from the hyperscalers.

And even during a quarter when token austerity came into vogue,

demand is showing no signs of slowing.

Meanwhile, the infrastructure boom also is coming to China as Tencent has tripled their cap-ex.

Tencent reported that they spent 7.8 billion on AI infrastructure in the past quarter,

boosting their training and inference fleet.

Now of course that spending is still relatively modest compared to the US hyperscalers,

where Meta had the slowest cap-ex in their group and spent 31.9 billion in Q2.

Still there's a pretty clear attitude shift as the Chinese tech giants commit to scaling up their

data center construction. During an earnings call on Wednesday, Chief Strategy Officer James Mitchell said,

We're allocating a very substantial portion of new compute to our own models and applications.

The company's revenue is growing at 11%, but free cash flow has dipped into the negative

with incremental earnings going toward infrastructure. Tencent President Martin Laos said that

Tencent could monetize their compute by selling to outside customers if they wanted to,

but for now they're prioritizing their own needs.

Basically just like model training, it seems like China's AI build-out and the narratives

around it are three to six months behind the US as well. It is uncanny how closely this is

following the narratives from the US in Q1. Hyperscalers flipped negative free cash flow,

folks like Zuckerberg appeasing the market by telling them that he could sell his compute,

but he doesn't want to. I'm not sure I think that US market participants have fully

accounted for a Chinese cap-ex boom and what it does for the larger global investment environment.

Meanwhile, Samsung is seeing incredible efficiency gains from their use of AI and chip design.

According to reports from a Korean outlet, the first three months of integrating

Quad Code into the software stack have been an outstanding success. Development personnel have

been able to cut down the time to complete complex tasks like system-on-chip verification from

three months to two days. In one example, a second year engineer was able to complete a month-long

task in a single day. Now of course this report doesn't claim that Quad Code produced efficiency

gains throughout the entire chip design process, but it does seem like an interesting example of

the jagged frontier of AI adoption in the enterprise. Quad Code was able to make highly customized

jobs more efficient, and able to help a junior employee contribute maybe on their expertise.

Lastly today, some reported updates coming to the Trump administration's model testing

framework. Last Tuesday, leading frontier labs were briefed on that framework, although the

rest of us didn't get to learn all the details. It was reported that the policy would cover only

state-of-the-art models, although we didn't know how exactly that was defined. What we did hear

with a fair degree of confidence was that the policy wouldn't cover open models.

Open source advocates were relieved at that decision, but there was also a contingent of China

hawks who believed that this would leave a gap. On Wednesday, Wired reported that the administration

has changed their mind. An official said that the White House is expected to expand the policy

to cover open models in the coming months. The policy they added is aimed at ensuring that as

soon as open models reach the same capabilities as Mythos or GPT 5.6, they're added to the safety

testing framework. White has official said the administration had hoped the policy would be one

and done, but the exponential development of model capabilities had forced them to iterate.

When it comes to the inclusion of open models, the thinking is that leaving them out of the

framework could actually create a two-tiered system that would be negative for those open models.

Specifically, officials are concerned that the framework could be viewed as a

stamp of approval, leaving enterprises hesitant to use open models if they don't receive the same

testing. The concern then is that leaving open models out could actually disincentivize US labs

from developing those open models. Adding some evidence to the idea that the government is pro-US

open models, Treasury Secretary Scott Besson actually retweeted Mark Zuckerberg this week, saying,

We welcome Metas release of Muse Glimmer, another win for American innovation.

Sustaining US leadership in AI means advancing both open and close-weight models, ensuring the future

is built on trusted foundations. Overall, it's still pretty clear that there's a lot of

consternation around the administration policy. President Trump himself is reportedly insisting

on keeping the framework voluntary as he believes formal regulation will help China catch up,

but by the same token, the safety-focused faction of the administration also isn't satisfied

and are reportedly still pushing for a more formal arrangement. Who the heck knows how that's all

going to turn out, but still this is a perfect segue to a broader discussion of the state of models.

So for now that's going to do it for today's headlines, next up the main episode.

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Every episode, I talk about the competition between OpenAI and Thropic, SpaceX AI, Google, and Meta.

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Welcome back to the AI Daily Brief. The big news that we are covering today is the release of

GROC 4.6, which is getting some pretty good reviews out of the gate. But what's interesting to

me is not just the model itself, but what it says about the state of the AI race and how that's

changing. Now the version of the AI race story that I am concerned with mostly here of course,

is the one that has to do not just with the achievement of some ill-defined far-flung

goal like AGI or ASI, but the practical impacts on where different labs are for what we get to do with

AI at home and in our companies. I think CNBC's Deer Drabossa summed up the vibes when she tweeted

yesterday, what a difference a year makes. A year ago, Frontier basically meant the Big Three US

Closed Labs, OpenAI, Anthropic, and Google. Now a credible list includes XAI and multiple Chinese

and open-weight labs. And while we'll get into the implications for the leading labs in a minute,

I think Nathan Lambert also gets at another part of the sentiment when he writes,

the vibes shifting from Anthropic is so far ahead to model competition back to all-time highs

took like four weeks. So let's talk GROC 4.6 first. The release appears to put SpaceX AI

squarely back in the Frontier model competition. Regular listeners will know that I take any release

benchmarks not just with a grain of salt, but with an entire bullfull. But still, GROC's reported

benchmarks are pretty hard to ignore. On GDPVAL, which is the measure of how agent AI performs

on economically valuable tasks, SpaceX AI claims to have overtaken both GPT-56 sole and Fable 5

by a small amount, yes, but taken over them nonetheless. Coding performance is improving as well,

with GROC scoring right between, but in the range of 5.6 sole and Fable 5 on cursor bench,

being a few points behind on both deep-swee and terminal bench. On the overall artificial

analysis intelligence index, GROC 4.6 jumped a full five points from GROC 4.5's 56 to achieve an

overall score of 61, that puts it ahead of Kimi K3, tied with 5.6 sole, and just a pointer to

behind Fable 5 and Opus 5. What that means is that if this was a new model from either Anthropic

or OpenAI, we'd probably be talking about how it's not quite state of the art and didn't push

the Frontier forward, but for SpaceX AI, who many had written out of the model race until fairly

recently, this is a huge achievement, summed up by the broad sense that you can see across AI

circles that we once again have three Frontier labs in the race. Also, while SpaceX AI has massively

improved GROC's performance from 4.5, it seems like they're still working from the same base model

as GROC 4.5. Pricing remains the same at $2 per million input tokens and $6 per million output

tokens, making it 60% cheaper than GPT-56 sole on a per token basis. Of course, as we know,

comparing tokens to tokens is a seductive but ultimately fraud exercise, given the massive

differences in how many tokens different models might use to solve the same problem. But once again,

artificial analysis is testing found that the model is pretty token efficient as well.

It completed the benchmark run at $0.84 per task, putting it in line with Kimi K3,

and making it 32% cheaper than GPT-56 sole and 73% cheaper than Fable.

Right's investor Daven Baker, absolute Pareto dominance for GROC and Cursor even after the OpenAI

price cuts. Now, in terms of reactions for the community, for many folks, it was just gobsmacked

at the achievement overall. Vitorio writes, so they just caught up in three years? How does Elon

do it? Ben Davis writes, GROC 4.6 feels very good on first tests, very fast and capable and cheap,

but time will tell as always. The cursor and SpaceX AI come back as glorious to watch.

On Martin Kassato from A16Z's highly technical tests, he found that it was strong. Pueville Huron writes,

tried GROC 4.6 on my bug bench an hour after release, 105 hidden bugs and two real repos judged blind.

His conclusion looks like it may be my new default model, the best combination of time,

value, and cost. And yet, some folks did not have that same experience.

Neh-Hum-Lohan writes, GROC 4.6 is not as good as GPT-56 sole in my 30 minutes of usage. It does

incomplete work, not incorrect, just incomplete. Maybe it's the GROC harness? Just in Trotor writes,

early vibes on GROC 4.6 are not great. It's fast that it's willing to do security work.

I've already seen multiple instances where it makes dangerous mistakes and later tries to cover

up poor decisions. It even gets defensive. Unfortunately, we cannot trust it.

Entrepreneur Timmy McKegan writes, GROC 4.6 is one of the most oddly-behaved models I've seen so far.

It produces many times the output tokens compared to Terra or any similar intelligence model.

It is cheap and fast, but takes everything extremely seriously and always investigates

unclear information. It values completeness above everything, including economics.

The model seems to be designed to be economically viable, but acts differently.

Now, when someone tried to clarify if this is a positive or a negative sign,

Timmy kind of shrugged and said, probably positive? Benjamin DeCracker tried to sum up,

lots of people acting like GROC 4.6 just beat Anthropic in OpenAI when really it didn't.

The GROC 4.6 numbers show that XAI is not out of the race, but also not at the top.

It's in the middle top of against models that the competition is already getting ready to update.

It shows that GROC still has a pulse, which is a good but different thing. He continues,

or in sports terms, they advance past a critical wildcard game into the playoffs,

but are mid-rank against tough competition. They prove they can still hang, not yet winning everything.

And by the way, he clarified, this is not a slight against GROC 4.6 which looks solid just to

read of the actual rankings in situation. Now, of course, what Benjamin is referring to is the fact

that 4.6 is being compared against GBT 5.6 and Fable 5. When both of those models are at this point,

several months old, and pretty much the only reason we don't have updates of them is that we're

now past the threshold where the US government is going to be involved in every big new model release.

And so state of the art for us is very different from state of the art at those top labs.

However, it sounds like GROC 4.6 is itself just a waypoint. Elon Musk tweeted,

GROC 4.7 is significantly better than 4.6 and should be ready in 3-4 weeks.

Initial training is complete and now we're adding a massive amount of SpaceX company data in

supplemental training. This will be something special. In another tweet he said,

GROC 4.7 will exceed all current models. That said, and Thropic is a great company and will

probably release improved models soon. However, the SpaceX training corpus is so awesome and unique

that I would be shocked if any model is better at real world engineering than 4.7.

Capturing the zeitgeist of credulity around these claims,

Chubby shared both those posts and said, I'm taking this seriously now. GROC 4.6 was the leap

I've been hoping for. If the 10T model is still to come, then Elon's words can be taken seriously.

It really could become the best model in general. Although of course, Anthropic already has

Fable 5.5 ready and just waiting to be released that much is clear. Nevertheless, the next few weeks

will be exciting and XAI has shown just how much potential they possess. Leo at Synthwave

XAI have made an incredible comeback. From the days of GROC 4.4 to 4.3 where they were trailing

the frontier by far, they're now arguably the third best lab in the world, behind only Anthropic

and OpenAI. So where does this leave the rest of the field? Well, first of all, there's Google,

the company that many feel, Anthropic has now overtaken as the definitive third place when it

comes to state-of-the-art models. After last week's departure of DeepMind CEO Demisisabis and

longtime product leader Jeff Dean, many are basically counting Google completely out of the

frontier AI race. The counterpoint, however, is that it appears that co-founder Sergei

Brin is back in the picture to spur a comeback for Gemini. Rotter's reported that Brin has become

a key cheerleader for Google's AI team in recent months, encouraging AI engineers to catch up in

the AI race. He reportedly addressed a town hall after the release of Mythos, telling engineers

it's time for Google to play catch-up. Sergei had of course been out of the picture for several

years after stepping down as president in 2019, however, he returned to frequent work at Google

in 2023 and stepped into his involvement with the AI team in 2024, just as they were getting back

on track ahead of the release of Gemini too. During last week's news cycle, we had already heard

that Google was relocating AI training out of the DeepMind office in London and back to the

main campus in Mountain View. That relocation would conveniently allow Brin to play a more active role

working day-to-day with key researchers. And of course, given what else we've heard about internal

Google politics, one of the big benefits to having Sergei fully engaged is that presumably he's

one of the few people that could effortlessly cut through that bureaucracy to get things done

at Google. According to the Reuters report that came out on Wednesday that has already begun.

Reuters writes, Brin has used the implicit power he holds as Google's co-founder to push resource

allocation towards specific areas such as recursive self-improvement. And to some, this is a good

enough reason all on its own to not count Google out. Nick the CS guy from Google writes,

don't mess with Sergei and definitely don't underestimate what he can do.

So others think that Google is just temperamentally ill-suited to this particular race.

Computer science professor Pedro Domingo's writes,

Hey Sundar, getting DeepMind to be an LLM lab is trying to shove a square peg into a round hole.

You're destroying them and you'll still lose the race. Let them focus on AI beyond LLMs,

which is what they're good at and create an imbal new lab to run the LLM race.

Now when it comes to what models we can expect next, I think at this point,

broad sentiment is that it would not be enough to recapture momentum by releasing a competent

Gemini 3.5 pro at this point. We're already a couple months behind when we expect it to get it,

and just catching up I think would be seen as a failure. According to Leo and some other leakers

I've seen, the reports are that teams are instead shifting to work on the scaled up Gemini 4,

which while risky I think does make sense in context.

Now as Deirdre pointed out in that tweet at the top of the show, the top model lab's question now

has to necessarily include a bunch of entrance from China. And interestingly just a few hours

after GROC 4.6 launched, we got a significant leak out of China. Specifically we got the benchmarks

for the updated version of DeepSeat V4 Pro, and they appear on paper at least to be very competitive.

For example, these leaked benchmarks claim that the forthcoming model scored 87.9% on terminal

bench 2.1, putting it just 0.1% behind Fable and 1.1% behind GPT-5.6 sold. It also claims to

beat Fable by 0.2% on CyberGym, the main cybersecurity benchmark. Now as always there's the

risk that this is just benchmark maxing and actual performance will feel a little flat.

And unfortunately, almost as soon as these leaks started appearing, other information came out,

suggesting that the model was more significantly behind than the benchmarks would have it seem.

Artificial analysis is benchmark run was pretty disappointing with V4 Pro scoring just 53.

That's only 1.1 ahead of V4 Flash, and trails behind Kimi K3 and MuSpark 1.2.

On the plus side the model is pretty cheap, even after DeepSeat delivered a substantial price

increase this morning. At a buck 32 per million input and 396 per million output, it's about

1.12th the price of Fable and slightly cheaper than MuSpark. And people's first impressions

also aren't that great. Lucky Faraday writes, DeepSeat V4 Pro is Benchmark's slop. I had high hopes

for this model but it's complete trash. This was supposed to be a Fable level model and it can't

even make a simple Minecraft clone. Even DeepSeat V4 Flash did a better job. I know a Minecraft clone

isn't a good test for a model but come on, this is complete nonsense. And before the don't compare

a less than $1 output model to Frontier model replies, they are the ones comparing themselves to

the Frontier, not me. Still others pointed out that when we're discussing models in the second

half of 2026, it is less about raw performance alone and more about where they fit in the model stack.

Dax from Opencode says DeepSeat is insanely good at inference, using about two times less GPU time.

And Augustine LeBron writes, I'm sure Kimmy K3 and GROC 4.6 and DeepSeat V4 Pro are Benchmarks

more than Fable and GPT, but it doesn't matter. These models are an order of magnitude cheaper.

As the Frontier proceeds, fuel and fewer people need the bleeding edge and need it less often.

And at first glance, Rampslatus AI Index seems to provide some evidence of that. Rampslate

economist Arakerazean writes, New from Rampai Index, Disappointing adoption of Fable 5.

We've heard several reasons from businesses, mainly Fable 5 is just too expensive.

A model so powerful it was briefly banned and yet businesses don't think it's worth the price.

Specifically Ramp found that Fable 5 has made up only 6% of tokens that businesses purchased

from Anthropic and represented only 11.4% of dollar spent on Anthropic models.

For comparison, they write, OpenAI's GPT 56 sole comprises 25% of OpenAI tokens and 23% of spend.

In fact, they say Fable 5 is less popular with businesses than GPT 5.6 overall.

Ramp argues that quote,

With Fable 5, we found a new upper bound to how much businesses are willing to spend on AI.

Here, more performance is not worth the price tag.

To encourage business adoption of the latest models, the labs will need to prove performance

beyond even what Fable 5 is able to achieve and simultaneously ensure that competitors aren't

able to come reasonably close. That seems increasingly out of reach,

especially as open source models catch up to being only a few months behind.

However, I think that story is much less clear than they're letting on.

First of all, assignment Smith points out,

Ramp data overall suffers from selection bias and this data suffers from it even more.

This data comes from their token and spend management product,

meaning users are predisposed to focus on cost control.

Fable simply isn't cost effective for most tasks.

In other words, this is an extremely enfranchised set of users

who are specifically using this in a product that is designed to manage spend

and optimize spend away from models that are more powerful than you need,

rather than being a general assessment across a wide cross-section of businesses and business use cases.

Still to me, that isn't even the most damning thing,

as perhaps one could argue that those companies in that type of spend management

are a leading indicator of where others will get.

I think the bigger and more obvious issue is that Fable 5 still comes with a 30-day data

retention policy and most businesses aren't willing to touch that with a 39-and-a-half-foot pole.

Indeed, error actually came back to Twitter and retweeted himself to add this

incredibly important detail saying,

a lot of replies from employees who say they aren't allowed to use Fable because Anthropic

is required to retain prompts for 30 days for US government safety checks.

Look, it is absolutely the case that the more sophisticated buyers get,

the less they're just going to smash on the state-of-the-art model

at the highest effort level for every single prompt.

But the data retention policy really makes this not a particularly clear comparison.

Now lurking behind everything we've discussed in today's show is the fact that Anthropic and OpenAI

both have more advanced models, more or less ready to go at this point,

that are being held back by a combination of government pressure,

internal concern, or simply the fact that because nothing else is caught up,

they don't really have pressure to move things forward faster.

Still, even if on the one hand we are seeing a slowdown,

in the speed with which Anthropic and OpenAI specifically are dropping models,

I think it's pretty hard to look around the model landscape right now,

and not feel like we have increasingly more rather than less choice.

Anyways friends, some fun new treats to try for the weekend,

but that is going to do it for today's AI Daily Brief.

Appreciate you listening or watching as always, and until next time, peace!