Nvidia Buys Hugging Face For A Rabbit?

Welcome to the Tech Brew Ride Home for Thursday, September 3rd, 2026, I'm Brian McCullough.

Today, Nvidia agreed to buy hugging face for $12.9 billion.

And what if I told you there's an argument to be made that this is a bet on a $399 robot

dock?

Meta rolled out Muse Spark 1.3 to rival Fable 5.1 and GBP 5.6 sole and Microsoft restructured

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Well it is official and video has agreed to buy hugging face for $12.9 billion.

It's second biggest purchase ever after it paid $20 billion for Grox assets at the end

of last year.

But what if I told you they were willing to pay that much for hugging face because of

a robot duck.

We'll get to that.

But firstly the headlines quoting CNBC with the deal which has been expected since the

information reported on it last week.

Hugging face will remain an open platform for the entire AI ecosystem and video CEO Jensen

Huang wrote in a blog post on Thursday.

Together we will scale hugging faces platform strength and its infrastructure and expand

access to AI for developers and institutions worldwide.

Huang wrote.

Hugging face CEO Clement DeLong told CNBC on Thursday that the company approached Huang

over the summer about a deal and a few weeks later here we are.

During the summer I think we realized that hugging face and open source AI in general was

at a turning point and that it needed more more resources more scale more visibility.

He told CNBC's Becky quick on a squawk box.

To long said he approached first because Nvidia was a quote perfect home for his company

adding that discussions went quite fast to get a deal done.

The acquisition marks Nvidia's second biggest on record following the $20 billion purchase

of assets from Shipmaker Grock in December.

Prior to that its largest deal was the purchase of Israeli Shipmaker Melanox for almost $7

billion in 2019.

Nvidia has become the world's most valuable company due to the insatiable demand for its

graphics processing units which have powered the generative AI boom.

Hugging face marks a big bet on a popular AI platform as Nvidia continues to show that

it's more than just a chips company.

Hugging face was recently at the center of a hacking incident that raised concerns about

the rapid evolution of powerful AI and cybersecurity tools.

DeLong a proponent of open source models blamed engineering mistakes for the recent attack

on Hugging face and said his company used an Nvidia version of a Chinese open model to

resolve it.

Come on told CNBC on Thursday that the breach proved the importance of open models and the

need for his company to quote double down on the proliferation of open source AI.

Long said that the open source environment can give defenders an asymmetric advantage

over attackers.

When I say asymmetric capability there are way more people who are protecting than there

are people who are attacking he explained.

And so the benefit of having the community come together with open models so that they

can collaborate all transparently with each other gives the defenders an asymmetric advantage

and quote.

Now, I forgot to tell you about this last week because I had reached out to Hugging

face to do a segment on it with some folks from Hugging face but then this deal started

to happen last week and so they were too busy to put anything together.

But Hugging face last week unveiled microduck a $400 1.7 pound 10 inch tall bipedal robot

developed in part by Paulin robotics and manufactured by a Chinese company called

Seed Studio that users can reprogram by loading small language models onto it.

And over at decoding discontinuity Raphael D. Ornano says sure this might be an AI infrastructure

play but what if the duck robot is actually a secret part of why this deal is happening.

Quote.

Relatively tiny compared to Nvidia Hugging face has established itself as the primary registry

where the world shares AI in AI registry is the distribution layer where models data

sets and increasingly behaviors are discovered versioned pooled and published nearly 3 million

models and more than a million data sets are published on Hugging face when a developer

needs a model its code pools from Hugging face by default.

But the company only generates roughly $150 million in annualized revenue at $12.9 billion

in video would be paying about 86 times sales.

That is almost double the $7 billion valuation at which Hugging face rejected a $500 million

dollar investment from Nvidia itself in late 2025.

At the time according to the financial times the company did not want a dominant investor

that could sway its decisions.

A repricing of that magnitude paid to the company that turned you down suggests Nvidia

values Hugging face as more than a model hosting platform with a modest enterprise business

attached.

That brings me back to the microduck.

The timing in terms of leaks about the deal and the microduck launch was perhaps just

coincidence but it put the acquisition and one possible explanation for its price on

the same screen.

The open source inflection in language AI happened because its training corpus already existed.

Physical AI has the opposite problem.

The models are becoming open.

Simulation makes some forms of reinforcement learning cheap.

Robot hardware has collapsed in price.

Shared data formats now exist but the essential training input of data generated by bodies

interacting with the physical world remains scarce, expensive to produce and overwhelmingly

locked inside closed fleets.

No internet of embodied experiences is waiting to be scraped.

That's what makes microduck more interesting than its specifications.

Hugging face has designed a $399 consumer robot around a loop.

Train a behavior and simulation.

Deploy it to the machine and publish it back to the hub for someone else to build on.

On the surface the product looks like a robotic duck.

Underneath that surface however the product is an attempt to turn thousands of cheap robots

into a distributed engine for producing the shared corpus of embodied data that open

physical AI still lacks.

If that conversion works the hub stops being only a registry where open intelligence is

distributed and becomes one of the places where physical intelligence is produced.

A community corpus begins to compound.

Open robot models get the equivalent of the internet that open language models inherited

for free.

And a layer that looks modest when measured against today's enterprise revenue begins to

look considerably more strategic.

That I think is the option Nvidia may be pricing.

In that scenario, microduck would become the latest in a sequence of tremors.

I have been cataloging that trace the same fault line.

Value migrating out of the model layer as intelligence is no longer the scarce resource

on which competitive advantage can rest.

With respect to physical AI, if embodied data becomes a commons, then the value begins

to migrate toward the layers that distribute intelligence, generate and verify its data

provide the bodies it inhabits and supply the compute on which it learns.

For hugging face, the specific challenge it now faces is whether it can convert its consumer

robot ownership into published training data at a rate its previous hardware could not.

If it can, microduck may mark the beginning of physical AI's open source inflection.

If it cannot, the $12.9 billion thesis must rest on the registry alone.

Either way, the duck gives us a way to understand what Nvidia might actually be bidding for.

And quote, she does go on to argue that the primary bottleneck in physical AI has shifted

from models and hardware to a shortage of real world training data while closed agents

hoard proprietary telemetry.

The open source ecosystem has assembled cheap arms simulation and shared schemas.

However, community data sets remain low quality and rarely yield useful contributions from owners.

The bet behind hugging faces microduck experiment is whether distributing affordable hardware

can trigger a self reinforcing data engine.

They want to find out if active robot deployments can successfully convert daily real world use

into an open compounding corpus of usable physical trajectories to rival closed corporate

data dabs, quoting from her piece again.

Nvidia's reported bid for hugging face is not really a bet on model hosting.

It is an option on the infrastructure of open AI.

Microduck makes that thesis measurable if thousands of $399 robots publish reusable behaviors

back to the hub.

Hugging face could become the data commons that physical AI currently lacks.

But the logic of the bid does not depend on the ducks actually succeeding.

Let's start with what Nvidia would be buying today.

Hugging faces already the default distribution layer for much of open AI.

And hugging faces libraries, a model identifier resolves to the hub by default.

Nvidia's models compete there for adoption against Gwen, Lama, and the rest of the open

ecosystem.

The platform provides an unusually early view of what developers download test and deploy.

These are demand signals that can proceed the GPU workloads they eventually create.

That makes the registry strategically valuable, even if microduck contributes nothing.

So in that framing, physical AI makes the upside larger.

If the published back conversion works at even a fraction of consumer scale, embodied

data stops being something only capital rich fleets can produce.

The company commons begins to form an a shared format and on a shared platform, growing

as more machines generate usable experience.

Open robot models can then begin compounding on community data, the way open language

models compound on the internet.

If that happens, hugging faces no longer simply the shelf where open models are distributed.

It becomes one of the places where the scarce input to physical intelligence is accumulated.

This is one way to read the $2.9 billion valuation.

Nvidia would not be buying an embodied data engine.

The reaching numbers tell us that engine does not exist yet.

It would be buying an option on whether one forms at the platform where the pieces are

already converging.

If the conversion happens anywhere in the open ecosystem, hugging faces unusually well positioned

to see it first and potentially to become the place where it compounds, that will likely

be true no matter what happens with the microduck experiment.

That helps explain in 86 times revenue price in a way that the current income statement

does not end quote.

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And Model Rollout season continues as Meta has rolled out Mews Spark 1.3 in Mews Code

and Meta Model API saying it improves performance across agentic and coding tests at the same

pricing as Spark 1.2 but with performance on some benchmarks coming in just behind only

fable 5.1 and Opus 5.

Quoting the decoder.

At an unchanged $25.25 per million input in Alput tokens, one index task costs 55 cents.

No model scoring 59 points or higher is cheaper and the rivals at the same index level run

between 94 cents and $1.23.

Mews Spark 1.3 does cost more than version 1.2 though, which ran 40 cents.

On the, I think it's called Tao Cubed Banking where agents operate tools in a simulated

banking scenario, the new model hits 52%.

That's number one right now according to artificial analysis and it's the only outright

lead the model holds.

The available X-high tier reaches 47% tying cloud fable 5.1 max and GLM 5.3 flash rather

than leading the predecessor 1.2 set at 35%.

Terminal bench 2.1 which tests coding in the terminal climbs from 80 to 85% on X-high

and 86 on max but cloud fable 5.1 still holds the top spot at 91.4% in its max tier, 91.0

at X-high and 89.9 at high end quote and quoting Silicon Angle.

The company said in a blog post today that the new model can be accessed by developers

willing to pay for it through its application programming interface.

It's also going to be rolled out to users of Meta's social media platforms, Facebook

and Instagram as well as the Meta AI application in the coming days.

In an interview with Bloomberg, Meta Chief AI officer Alexander Wang said Mews Spark

1.3 represents the company's biggest jump in model performance so far, putting it at

the same level as the most recent models created by OpenAI and Anthropic, pointing to advances

in Mews Spark 1.3's coding and agentic automation capabilities.

Wang said it is now competitive with Anthropics Cloud Fable 5.1 and better than OpenAI's GPT

5.6 sole model, especially in terms of its ability to generate code.

It also outperforms any of the current Chinese models out there Wang claimed.

According to Wang, developers will not have to pay any more to access Mews Spark 1.3 than

they were paying to use Mews Spark 1.2 which was launched in August.

It gets two predecessors, it's available through the Meta Model API which also provides

developers with various tools for building AI applications.

Wang told Bloomberg that Meta has seen rapid adoption of the Mews Spark LLM family with

some developers using trillions of tokens per week.

He said they'll be very happy with Mews Spark 1.3 because it's more efficient than version

1.2 using around 25% fewer tokens to accomplish the same tasks.

It can also support multiple workflows at once instead of requiring separate sessions

for each one and it's better at handling long and complex instructions and retaining

context across multiple tasks.

It also has more awareness of its own limitations Wang said and it is much safer.

In every case, when it's about to take an action that's irreversible, it will ask for

confirmation before it goes and doesn't.

Wang said Meta has not yet decided whether or not it will release the Mews Spark 1.3's

weights, the internal blueprint form during the training process that determines how the

model responds.

Knowing the weights would allow outside developers to download, run or build upon the model

on their own.

The company still plans to release weights for the prior version, Mews Spark 1.2 Wang said.

Meta had previously promised to release the weights for Mews Spark 1.2 but you'll note

it has not yet done so.

Meta may get around to doing this by the time it releases its highly anticipated model

called Watermelon which is said to be larger and more powerful than the Mews Spark models.

The company has been working on Watermelon for some time but Wang declined to say when

it might be released.

For now, it remains a work on progress but Wang believes it will be worth the weight.

We believe that Watermelon will be extremely competitive, he added and quote.

Finally today, this might sound a bit in the weeds until you think about what this represents.

For quarterly earnings going forward, Microsoft says it is shifting from three reporting

segments to just two agents and infra which has Microsoft 365 and Azure and devices

and consumer which will be the bucket for Windows and Xbox.

Quoting the journal.

Previously, Microsoft's reporting segments consisted of productivity and business processes,

intelligent cloud and more personal computing.

Such an Adela Chairman and Chief Executive Officer said AI represents a profound shift in

technology and business and is changing what the company builds and how it operates.

In addition, AI is blurring boundaries between the company's products and reshaping its business

models.

Adela said, the agents and infra segment will encompass apps and agents which include Microsoft

365 and GitHub, the multi model system grounded in rich enterprise context and global scale,

Azure infrastructure, Microsoft said.

Meanwhile, devices and consumer will contain search and advertising, Xbox and Windows according

to the company.

The new structure brings the company's advertising businesses together, Microsoft added.

The agents and infra segment is expected to report fiscal year 2027, first quarter revenue

of 75.15 billion to 75.75 billion.

Businesses and consumer is expected to report first quarter revenue between 14.7 billion

and 15.2 billion.

The company said, and Microsoft said, it would also update several other metrics as a result

of these changes and quote, I just want to note the difference between those two numbers.

Basically, anything consumer see, roughly 15 billion dollars in revenue a quarter of

the things that is, I don't know, AI, stuff of businesses see, all that cloud computing

stuff, 75 billion a quarter in revenue.

Nothing more for you today, talk to you tomorrow.