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Microsoft & Uber Stop Counting Tokens

Microsoft is pulling internal Claude Code by June 30, less than six months after rolling it out, because it burned through its entire annual AI budget in a few months. Per-engineer costs ran $500 to $2,000 a month, which made the tool more expensive than some of the engineers using it. Microsoft is steering those developers to its own GitHub Copilot CLI instead.

Uber was more explicit, COO Andrew Macdonald coined “tokenmaxxing" for rising token consumption that doesn’t show up in the product, and when asked to connect the spend to better consumer features he said “that link is not there yet.” This is at a company where 95% of engineers use AI monthly and 70% of committed code is AI-generated. Uber burned its entire 2026 AI-tools budget in four months, and Duolingo is in the same chorus.

The shift from flat seats to token-based billing broke budgets. I personally have a friend at Meta who spent $1K per day on tokens. You pay for every line the model generates, and the lines are going parabolic. More lines of code does not mean more products and more revenue, and it may not even be the right work done.

If you recall, this is the gap I flagged on May 21. The ARR is real, but a chunk of it is consumption that the buyers themselves can’t yet tie to value, and execs are now demanding outcome-linked pricing instead of seat-count renewals.

An IT leader said it best to me this week, “we all know how powerful AI is, we just don’t know how to tie it back to value”.

Anthropic Raises $65B While Customers Question Their Spend

The same week customers pumped the brakes, Anthropic closed a $65 billion round at a $965 billion valuation, passing OpenAI as the most valuable AI startup. Altimeter, Dragoneer, Greenoaks, and Sequoia led, with about $15 billion of it coming from hyperscalers, including $5 billion from Amazon. Run-rate revenue has increased from $10 billion at the end of 2025 to $47 billion now.

On the same day, Anthropic shipped Claude Opus 4.8, which it says is four times less likely than 4.7 to let flaws in its own code slip through, at the same $5 per million input and $25 per million output tokens.

I agree, Claude Code felt demonstrably better the day of release, and I only realized later that I was on Opus 4.8.

Anthropic also previewed Mythos, its most capable model, going wide “in the coming weeks.”

Anthropic is being valued at $965 billion on revenue that runs on token consumption, but that consumption is exactly what Microsoft and Uber just started cutting.

The bull case is that the $47 billion run rate is up nearly 5x in a year and a better, cheaper model (4.8 at the same price) gives customers more value per token, which keeps them in. The bear case is that “more value per token” can mean fewer tokens billed, and a model that catches its own bugs is a model you babysit less and pay less for.

For a company priced on consumption, efficiency cuts both ways.

Apple Eats AI’s Memory Tax to Take Market Share

If you have shopped for a computer recently, you may have noticed a sharp pricing increase. The same labs and hyperscalers racing to build AI are buying up the world’s memory supply, and Samsung, SK Hynix, and Micron have shifted 93% of their combined output to high-bandwidth memory for AI accelerators, which starved the regular RAM in phones, laptops, and consoles.

DRAM contract prices rose over 58% quarter-over-quarter in Q2, NAND flash rose over 70%, the biggest jumps in over a decade.

This Reddit post summed it all up pretty well.

And you probably felt the pain as well, unless you bought from Apple. Apple told suppliers it will hold the iPhone 18 starting price flat and raise prices elsewhere as little as possible, which means they’re eating the costs.

Why? My guess is market share. The AI boom is raising component costs for every hardware maker at once, and Apple is the one holding prices flat. They’re using this as a wedge so they can capture market share from Dell, Samsung, and Sony. A few quarters of thinner hardware margin buys phone and PC share that is hard to claw back once a customer switches. Seeing that Apple makes 25% of their revenue in services, they probably figure they’ll make up for it later.

And if LLMs get commoditized, they could make even more in services. Google pays Apple $20 billion each year so they’re the search default, will an LLM pay so they’re the default in the future?

They’re only paying $1 billion a year for Gemini right now, which is nothing when you consider GPT-5’s training run cost roughly $2 billion alone.

If Apple could get a deal like that with their current market share, what can they get if even more people use their devices?

It’s a bet, for sure, but compared to hyperscalers spending $700 billion on AI, it’s a sensible bet that could have a lot of upside.

The Signal

The Signal is normally for paid subscribers, and one edition each month is free for all subscribers. 3 takes that didn’t fit above, plus one bet.

1. The Claude Code cuts move the spend. When Microsoft and Uber drop Claude Code, the budget doesn’t go back to hand-written code, it goes to Cursor or OpenAI’s Codex, the tools developers actually ask for. Every coding vendor collects the same token tax, so a cut at Anthropic is a sale for someone else, not a contraction in spend. Microsoft is the only one routing the money home, to its own GitHub Copilot, and Copilot is the tool nobody switches to until it gets good. Read the week as a vendor reshuffle, not the start of an AI pullback.

2. Mythos finds the bugs hiding in plain sight, and that’s the ROI. Mythos found a 27-year-old vulnerability in OpenBSD, it’s capable of finding bugs that humans never had the capacity for. Given that, companies will have no choice but to procure Mythos and ensure their software isn’t vulnerable. And that’s exactly the story Anthropic shared while raising at $965 billion.

3. Grok Build is seizing the moment. xAI shipping Grok Build the same week Microsoft and Uber cut Claude Code is the opposite of bad timing. Companies aren’t leaving AI coding, but they are shopping for a better ROI, so there’s no time like the present for xAI.

My bet: When Mythos opens past its 50 Glasswing partners to everyone, it holds at or above its $25 and $125 per million token preview price before 2027. Anthropic just raised at $965 billion on a model that finds flaws nobody caught in a decade, and you don’t cap your own upside heading into an IPO, no matter how loud Microsoft and Uber get.

That was The Signal, free this week. Paid subscribers get it with every issue, plus the AI Playbook and the full archive. Join them for $90/year

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SpaceX’s $2T IPO Is a Bet That AI Moves to Orbit

SpaceX filed its S-1 on May 20, targeting a $1.75 to $2 trillion valuation and a $75 billion raise. That is the largest IPO in history, past Saudi Aramco’s $1.7 trillion debut in 2019. It lists on Nasdaq as SPCX around June 12.

But then you look at the chart, and SpaceX claims a $28.5 trillion total addressable market, and $26.5 trillion of it is AI. Enterprise Applications alone is $22.7 trillion, larger than the rest of the company combined by a factor of fifteen. The launch business that made SpaceX is only $370 billion, a rounding error on the slide (and a decent business itself).

But the numbers below the top line are more concerning. After folding in xAI in February, SpaceX posted a $4.94 billion net loss for 2025 and a $4.28 billion loss in Q1 2026 alone, with the AI segment running a $6.4 billion operating loss against $3.2 billion of revenue. Starlink is the real business today, $11.4 billion in revenue and $4.4 billion in operating income.

Strip away the TAM theater, though, and I think their bet makes sense. SpaceX is the company best positioned to put the data center in orbit, and that is where a large share of AI compute is heading.

Many are scoffing at this, and I think it’s because they’re imagining these massive data centers being launched into outer space, like this.

But that’s not happening. Instead, these “space data centers” will be more like a satellite as a rack, like this.

Seems more doable, right? They’ll match the cumulative capacity of those massive data centers by communicating with lasers.

Make the racks disposable and you solve reliability the way SpaceX solves everything, with volume. SpaceX has already filed for a constellation of up to one million satellites that operate as orbital data centers, sun-synchronous so they sit in sunlight more than 99% of the time. Starcloud has run a version of Gemini in space on an H100-class board, and Orbital, funded by a16z, is building nodes aimed at one workload in particular.

Agentic inference has no human waiting on the other end, so latency isn’t as big of a deal. A request can take an extra half second to bounce off orbit and nobody notices, which means you can run it on cheaper, slower memory and good-enough compute. As agents replace people in the loop, this can become the largest compute market, because it scales with compute instead of with humans.

And while building on Earth should be cheaper, data centers are requiring more permission, and opposition is getting more fierce.

A 1-gigawatt orbital data center should cost roughly 3x its earthbound twin. But if you can’t get the one on Earth approved, space becomes a lot more attractive. The TAM is absurd, but the demand for intelligence is insatiable, so their TAM could be realized if there’s no alternative for building intelligence.

OpenAI Starts the IPO Clock

OpenAI filed confidential IPO paperwork on May 22, with Sam Altman pushing for a September debut at a valuation that could pass $1 trillion, up from $852 billion in March. Q1 revenue was around $6 billion, and the company remains, in its own filings’ language, deeply unprofitable. Microsoft holds about 27%.

Last week I asked a simple question: OpenAI and Anthropic collect $68 billion a year between them, but have their customers realized $68 billion in value? AI-blamed layoffs account for maybe $7 billion in removed payroll, and Gartner found even those savings are mostly not showing up.

And if enterprises are seeing a ton of value, why is OpenAI scaling up their forward deployed engineers, like the $4 billion Deployment Company? FDEs help when the product can’t realize ROI on its own, and they’re hoping these FDEs can solve the last mile.

That loops straight back to the number investors are looking for. An engineer living inside a customer is a services line, and services do not earn software margins. So the S-1 has to answer two things at once: what it costs to serve a token, and how much of that $6 billion is high-margin software versus headcount-heavy deployment work dressed up as product.

September is when the smartest bears in the world will read the footnotes, let’s see what those gross margins are.

Cloudflare Puts a Number on the AI Layoff

Tech shed around 30,000 jobs in May. Cloudflare was the newest big name, cutting 1,100 people, about 20% of staff, and said internal AI usage had jumped 600% in three months. That is the first time a CEO has put a productivity number on the table as the reason for the cut, in the same quarter the company hit record revenue.

Start with the number, because nobody is asking how it was measured. “Internal AI usage up 600%” counts usage, not output, and output is not value. More lines of code do not mean more products and more revenue, and it may not be the right work. Cloudflare reported the one thing that is easy to count and called it productivity.

This is what Rory Sutherland famously calls the doorman fallacy. Decide a doorman “just opens doors,” swap him for an automatic door, and you book the savings, but was that all the doorman did?

As Jerry Seinfeld learned when he helped out as a doorman, they also greet residents, hail cabs, and sign for FedEx packages.

In other words, the roles you cut on a productivity metric are almost never just the visible task. The engineer you let go was also catching the small issue before it became a public incident, filtering a bad call out of a teammate’s plan, and holding context that no dashboard tracks. The automatic door opens perfectly every time, but you find out what else the doorman did the day a couch is stolen in the lobby.

Two weeks ago, when Coinbase blamed AI for 700 layoffs and then ate a seven-hour outage three days later, I wrote that any company crediting AI for cuts gets held to a higher standard the moment its product slips.

Cloudflare just raised the bar, they run a meaningful slice of the internet’s traffic, and by citing a 600% gain, it has promised the market that the work still gets done, faster, with a fifth of the people.

The next time a big chunk of the web goes down, “we cut 20% of staff and our AI usage is up 600%” is the sentence quoted back at them in every postmortem.

You can credit AI for the layoff, but if your product slips, AI will also be blamed for your outage.

The Signal

3 takes that didn’t fit above, plus one bet.

1. Salesforce says Agentforce is a $1.2B business and the market still isn’t buying it Agentforce ARR crossed $1.2 billion, up 205%, on 3.8 billion “Agentic Work Units,” yet the stock is...

2. Anthropic’s Glasswing found 10,000 critical bugs in a month, which is the bull and the bear case at once Fifty partners using Mythos Preview turned up more than 10,000 high and critical vulnerabilities, with Cloudflare alone finding...

3. DeepMind and OpenAI both cracked open Erdős problems the same week, for the price of a dinner AlphaProof Nexus solved 9 open problems at a few hundred dollars of compute each, while an OpenAI model disproved a 1946 conjecture, and the tell was...

My bet: OpenAI’s S-1 shows a gross margin under 50% before September 30, which moves the whole conversation off...

Unlock The Signal and 3+ years of archives, $90/year →

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At I/O on Tuesday, Google put AI into nearly everything it makes. Here are just some announcements from Sundar Pichai’s recap:

• Gemini Spark, a personal AI agent in the Gemini app, with Android and Chrome coming next

• Gemini 3.5 Flash, pitched as faster and cheaper than rival models, with Gemini 3.5 Pro due next month

• Gemini Omni Flash, an any-input, any-output model, rolling into the Gemini app, Google Flow, and YouTube Shorts

• Two new in-house chips, TPU 8t for training and TPU 8i for inference

• Antigravity 2.0, reworking Google’s AI coding tool into an agent manager

• A new AI Mode button in the Search box, plus agentic experiences, just-in-time UI, and the ability to build apps inside Search

• AI Mode now past 1 billion monthly users, and AI Overviews over 2.5 billion

• Daily Brief in Gemini, a morning digest from your inbox and calendar

• Ask YouTube, which finds specific moments inside relevant videos

• Ask Maps, for longer and more complex questions

• Docs Live, writing a document by voice, with voice editing coming to Gmail and Keep

• Google Pics, a new AI image creation and editing tool, not to be confused with Google Photos

• Nano Banana for image generation, Lyria for music, Veo for video, and new editing agents in Google Flow

• Gemini for Science, wired into more than 30 life-science databases, plus Deep Think and Deep Research

• Audio glasses, display glasses, and XR eyewear running Gemini

• SynthID watermarking across 100 billion images and videos, with OpenAI now joining the scheme

• A new AI Ultra tier at $100 a month

And much more, Nathan Clark’s post summed it up well.

Google is spraying AI across every surface it owns and will see what sticks.

They already have the distribution. Gemini passed 900 million monthly users, double a year ago, thirteen Google products have more than a billion users each, and the company now runs 3.2 quadrillion tokens a month, seven times last year.

If just one of these hits, they will have a new billion user product. But how can consumers keep up with all the different products, and all the different SKUs? What are they paying for?

The good news for Google is they get paid even if these don’t land. Apple’s new Siri runs on Gemini, and Anthropic trains and serves Claude on Google Cloud TPUs under a deal worth tens of billions, which we covered two weeks ago, and Google holds roughly $10 billion of Anthropic equity.

With OpenAI focusing more on enterprise, Google has a chance to conquer the consumer market. They’re throwing the kitchen sink at this, and while all of these products and SKUs are adding up, anything that lands is upside.

The FDE Land Grab Jumps From the Labs to the Platforms

Last week we covered OpenAI’s $4 billion Deployment Company and Google Cloud’s new forward deployed engineering team. The platforms followed suit this week.

ServiceNow and Accenture launched a joint forward deployed engineering program, putting ServiceNow’s FDEs and Accenture’s industry teams inside shared customers to build agentic workflows in production before any company-wide rollout. Salesforce committed to hiring 1,000 FDEs. The title Palantir invented is becoming the most contested hire in enterprise software.

Most of us remember MIT’s study claiming 95% of enterprise AI pilots show no measurable return, and while contested for its method, the direction probably holds.

Failures come at the last mile of building and wiring into a company’s real data and workflows so it provides value. Anybody who has vibe-coded knows how easy the first 90% of a build is, and how brutal the final 10% is. Now imagine what this is like for an enterprise.

Which explains the focus on engineers who can make deployments work. Labs, cloud providers, system integrators, and software platforms are all bidding for the same small pool, which is exactly the kind of shortage that sets pay.

If the model is the easy part and the engineer is the scarce part, where do you think the money flows next?

A quick note from me.

Every company in that story is fighting over the same forward deployed engineers, so I started tracking them. FDE Pulse follows the forward deployed engineer market: who’s hiring, what they pay, and what backgrounds they’re looking for. Totally free.

Here are the top paying roles this week.

Right now there are 134 open FDE roles at a $135K median base, with the top listings from Google Cloud, ServiceNow, and the big consultancies clearing $300K. If the last mile is where AI value gets made, this is the job market making it.

Curious how this profession grows and changes? Check out the FDE Pulse →

Anthropic Hits $900B Before the AI Returns Come

Anthropic is raising more than $30 billion at a valuation above $900 billion, backers include Sequoia, Dragoneer, and Altimeter, and would surpass OpenAI’s $852 billion mark from March. Anthropic’s $44 billion run rate is up 80x in a year, while OpenAI is about $24 billion.

But what about their customers? With Anthropic’s $44 billion & OpenAI’s $24 billion ARR, how many of their customers have realized $68 billion in value this year?

Start with labor, the savings everyone points to. AI was the named reason for 49,135 US job cuts through April, per Challenger. Put a generous $150,000 of fully loaded pay on each one and that’s about $7 billion a year in payroll removed, enough to cover roughly a tenth of what those two labs alone collect.

To close the rest on headcount, companies would have to cut another 400,000 jobs and credit every one to AI. We’re at 49,000, and Gartner found the layoffs already blamed on automation are mostly failing to deliver the savings that justified them.

As for revenue, Deloitte’s 2026 survey found 20% of companies are already growing revenue from AI, with many more expecting to, but $68 billion worth?

Anthropic is the strongest case that the bet pays. Coding and back-office automation return money today, and that’s what Claude sells. If any lab’s revenue reflects value its customers capture, it’s Anthropic’s.

Step back, though, and the spending is years ahead of the payback. The money has gone in for tokens, but the returns aren’t meeting forecasts. Investors are betting that companies will keep buying Anthropic and OpenAI tokens, but when will their customers see a payback?

The Signal

3 takes that didn’t fit above, plus one bet.

1. Meta starts cutting 8,000 jobs, and we called the backlash last week The cuts began May 20, and after Coinbase...

2. Nvidia just crushed earnings and the stock fell anyway Data center nearly doubled and Jensen says demand has gone parabolic, but the reaction tells you...

3. China shipped four frontier-class coding models in a month and the West barely noticed GLM-5.1, MiniMax M2.7, Kimi K2.6, and DeepSeek V4 all landed at...

My bet: The first big AI layoff to get publicly reversed lands before...

Unlock The Signal and 3+ years of archives, $90/year →

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If you lay people off due to AI, you will be held to a higher standard. Coinbase just learned this in the most public way possible.

On May 5, Coinbase cut 14% of staff (about 700 employees), and Brian Armstrong told the world his AI-native engineers were shipping in days what previously took weeks. On May 7, Coinbase missed Q1 earnings: $1.41 billion in revenue (4.7% miss), a $394 million GAAP loss, stock down 5% after-hours. On May 8, an AWS data center in US-East-1 overheated when multiple chillers failed, and Coinbase went dark for seven hours.

CEO Brian Armstrong was quickly ratioed by someone who told him to “vibecode a quick fix”.

The technical postmortem is worse. Coinbase’s primary exchange runs in a single AWS Availability Zone “to minimize latency,” and the failover systems didn’t work as designed. Engineers had to manually execute disaster recovery. Armstrong, who days earlier credited AI for replacing engineers, said Coinbase would now “revisit how its exchange infrastructure handles AZ failures.” This is a problem multi-AZ deployments solved 15 years ago.

The earnings miss makes the rest obvious. Crypto trading volume was down, and the 14% cut was a margin response to a revenue shortfall. Cyclicality, not AI productivity, drove the timing. But Armstrong handed every critic a free script the moment he attributed the cuts to AI.

This matters because Meta starts its 8,000-person layoff on May 20, explicitly citing AI capex and productivity. Microsoft is mid-buyout for 8,750 US employees with the same framing. The Coinbase episode is the dress rehearsal. The next time Instagram, Threads, or WhatsApp ships a high-profile bug, Zuckerberg can expect the same response.

For any company citing AI in a layoff announcement: if your product goes down afterwards, expect a harsh reaction from your users. You can’t let your product slip after cutting costs, especially with so many worried about AI replacing their jobs.

A quick note from me.

This whole issue is about AI moving jobs around, but how does it impact pay? The people adding AI skills to their roles are seeing the biggest pay jumps in the market.

That’s from AI Pulse, where I track 22,000+ jobs weekly across 42 roles and 14 industries to show what share of your field now requires AI skills and how much more those jobs pay.

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The FDE Wars Begin at Frontier Labs

Last week we wrote that Anthropic and OpenAI were vertically integrating into the AI consultancy lane with their joint ventures, and now frontier labs are picking up steam.

On Monday, OpenAI launched The Deployment Company with $4 billion in initial capital and 19 founding partners (TPG, Bain, Brookfield, Goldman, SoftBank, Warburg Pincus, BBVA, Advent, B Capital, Emergence, Goanna, WCAS and others).

Same day, OpenAI acquired Tomoro, a 150-person Scottish AI consulting firm with Mattel, Red Bull, Tesco, and Virgin Atlantic on the customer list. Tomoro becomes the founding engineering bench. OpenAI keeps majority control. The job title for everyone joining is “Forward Deployed Engineer.”

Google responded on Tuesday, as Thomas Kurian announced a Google Cloud FDE team on LinkedIn, $750 million in deployment funding, and partnerships with Accenture, Capgemini, Cognizant, Deloitte, HCLTech, PwC, and TCS. Matt Renner, Google Cloud’s CRO, framed it as “more technical resources vs. just an ocean of salespeople.”

The “Forward Deployed Engineer” title was Palantir’s invention. For 20 years, it described an engineer embedded inside a customer to rebuild their data plumbing so the software actually worked. By investing so much with customers, the switching costs for leaving became astronomical.

Now OpenAI, Anthropic, and Google are all hoping the same in enterprise AI. The labs spent years hoping models would sell themselves, yet AI is still behind outside of engineering and data analysis.

Clearly AI consultancies on the integrator thesis haven’t made enough traction, and the frontier labs will try closing that gap.

It worked for Palantir, but these projects can take years. Given how fast AI is moving, will enterprises commit to one lab for that long?

Why Google Front-Ran Apple’s WWDC

On Tuesday, Google ran The Android Show 2026 and rebuilt Android around Gemini Intelligence. Sameer Samat, Google’s Android lead, stated “We’re transitioning from an operating system to an intelligence system.”

Google didn’t have to go first. Apple’s WWDC is June 8, and that’s where Apple will showcase their Siri overhaul. Google chose to run its show three weeks ahead of Apple’s. The question worth asking is why.

Start with what Google already won. Apple’s new Siri runs on Gemini. Apple confirmed it in January: about $1 billion a year for a 1.2-trillion-parameter Google model, white-labeled as “Apple Foundation Models.” Google Cloud’s Thomas Kurian reconfirmed it publicly at Google Cloud Next on April 22.

But the Apple deal gives Google everything except the one thing that compounds: consumer brand credit. Apple white-labels Gemini. When a billion iPhone users talk to the new Siri, they will think “Apple Intelligence,” not Google.

Which is why I think Google ran three weeks early. When Apple shows off Siri at WWDC, the press now has Gemini as a reference point, and Apple becomes the company catching up to a demo Google already gave.

It also forces a comparison Apple loses on timing: Google’s Gemini Intelligence features ship this summer on Pixel and Galaxy, while Apple’s full conversational Siri doesn’t arrive until later this year.

The bigger picture is that Google is now the only company contesting both layers at once. The labs are vertically integrating into the enterprise, and while Google is following suit, they keep investing more in the consumer market, betting they’ll be the AI brand that defines the category.

Apple is betting the model layer commoditizes: Gemini becomes one swappable engine among many, and the brand customers see still says Apple. Google is betting the opposite, that whoever owns the model owns the distribution, the same way it owned Search.

The Signal

3 takes that didn’t fit above, plus one bet.

1. AI stocks crack on May 12, and the picks-and-shovels names took the hit. On Tuesday, Intel slumped 9.8%, Micron dropped 7.4%, and CoreWeave fell 10.1%. All three had run 60% to 180% YTD before the cut. The market is differentiating “AI exposure” (everyone has it) from “AI margin” (only the labs and Nvidia have it). South Korea’s Kospi opened the selloff after the government floated a windfall tax on AI profits. Will that be the script Japan, Germany, and the EU run next?

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On Tuesday, Anthropic committed to spending $200 billion with Google Cloud over five years. The deal covers multiple gigawatts of TPU capacity, with units coming online starting in 2027.

That number is bigger than Google Cloud’s entire 2025 revenue. So the more interesting question is going the other way: how much of Google Cloud’s growth is already Anthropic’s growth?

Look at the past 5 quarters.

• Q1 2025: 28% growth

• Q2: 32%

• Q3: 34%

• Q4: 48%

• Q1 2026: 63%

Hyperscalers usually decelerate as they scale because the law of large numbers catches up, so what’s the catalyst?

In October 2025, Anthropic announced it would expand its use of Google Cloud to up to 1 million TPUs, and “well over a gigawatt of capacity online in 2026.” That announcement is the inflection on the chart.

On Q1 2026 earnings, Sundar said:

Google Cloud is differentiated because we are the only provider to offer first-party solutions across the entire Enterprise AI stack... Our Enterprise AI solutions have become our primary growth driver for Cloud for the first time. In Q1, revenue from products built on our gen AI models grew nearly 800% year over year.

And the relationship goes beyond cloud revenue. Google owned 14% of Anthropic as of March 2025. Anthropic is rumored to be raising at a $900 billion valuation, which would make Google’s stake worth $126 billion.

So Google is monetizing Anthropic three ways at once: cloud revenue (rented compute), enterprise AI revenue (inference passthrough), and equity gains (mark-to-market on the stake). Anthropic’s $200 billion commit feeds all three.

If you’re holding Alphabet stock, congrats, you also own Anthropic. If you’re a Google Cloud sales lead pitching against AWS or Azure, your biggest customer reference is a competitor of theirs. But if Anthropic stumbles, three Alphabet line items can move in the other direction.

A quick note from me.

I built AI Pulse, which tracks how AI is changing your career, and how much more your role pays with AI experience.

I track 22,000+ jobs weekly across 42 roles and 14 industries to answer: what % of your field now requires AI skills, how much more those jobs pay (avg 32% premium), and what to learn.

Forward it to anyone who wants to stay ahead of the curve, or subscribe free →

Anthropic Gets Compute from SpaceX, While Claude Keeps Slipping

On Tuesday, Anthropic took over the excess capacity at Colossus 1, the 300+ MW Memphis data center with 220,000+ GPUs that used to be xAI’s flagship. Anthropic doubled Claude Code’s five-hour rate limits the same day.

Colossus 2 is SpaceXAI’s flagship now. If you recall, Musk merged xAI into SpaceX in February as SpaceXAI in a deal valued at $1.25 trillion, then partnered with Cursor on April 21 for $10 billion in committed work plus a $60 billion option to acquire by year-end. Cursor is still ramping inside SpaceXAI’s stack, which leaves Colossus 1 with capacity that would otherwise sit idle.

In other words, this is a solid win-win. Anthropic needs compute and SpaceX has excess that they can monetize. We last wrote that Anthropic was throttling Claude consumers to feed enterprise compute. Doubling Claude Code limits the same day as the deal is the most direct admission Anthropic could make that the throttle was about supply.

But the product is still slipping. On April 24, Anthropic published a post-mortem confirming three separate changes degraded Claude Code:

1. Reasoning effort dropped from high to medium on March 4 to fix UI latency. It dropped intelligence on complex tasks. This frustrates Claude Code users, because they manually toggle on compute levels using the /model command.

   

2. A caching bug shipped on March 26 wiped reasoning history every turn instead of after one hour of inactivity.

3. System prompt limits on April 16 capped tool-call text at 25 words and final responses at 100. Coding evals dropped 3% on Opus 4.7.

The complaints that forced the post-mortem started with a GitHub issue from Stella Laurenzo, a Senior Director in AMD’s AI group. She analyzed 6,852 Claude Code sessions, 17,871 thinking blocks, and 234,760 tool calls and concluded the model could not be trusted for complex engineering work.

For Opus 4.7 specifically, Reddit and X consensus is that real-world cost is 1.5x to 3x higher than 4.6, multi-file edits are worse, refusal rates are up, and the conversational warmth that earlier Claude models had is gone. Part of me wonders if the conversational warmth is gone because users have become more frustrated and antagonistic, I know I have.

Anthropic’s own migration guide confirms Opus 4.7 follows instructions “much more literally” than 4.6, which is the polite version of the complaints.

Personally, I started bifurcating my work between Claude Code and Codex. Last weekend, Codex one-shotted a refactor with GPT-5.5 that Opus 4.7 on max compute couldn’t get right after three attempts. Anthony Maio’s Substack post “Codex Got Better Because Claude Code Got Weird” makes the same case from the other direction.

Colossus 1 buys Anthropic time on the supply side, but doesn’t fix the engineering culture that shipped three product-degrading changes in seven weeks. One of the big lynchpins justifying their valuation is switching costs, and if enough users are switching, that could cause alarms for their future IPO.

Anthropic & OpenAI Skip the Consultants in the Same Week

In the same week, Anthropic and OpenAI both launched dedicated enterprise services joint ventures.

Anthropic’s JV is a $1.5 billion vehicle with Blackstone, Hellman & Friedman, and Goldman Sachs as founding partners. Blackstone and H&F each put in $300 million, Goldman put in $150 million, and Apollo, General Atlantic, Leonard Green, GIC, and Sequoia rounded out the cap table.

OpenAI’s version, called The Deployment Company, raised $4 billion at a $10 billion pre-money valuation.

Four days after Anthropic’s announcement, the JV shipped a suite of pre-built AI agents for the world’s largest banks, using Claude Opus 4.7 tuned for financial work, full Microsoft 365 integration, and a Moody’s data partnership.

Frontier labs spent two years pretending models alone would sell, but the Fortune 500 wants pre-built agents wired into their data, their compliance stack, their workflow, their identity provider, and their procurement contract. That layer was typically managed by Accenture, Deloitte, BCG, and the AI consultancies that raised at insane valuations on the integrator thesis.

But Anthropic and OpenAI just walked into that lane using Goldman + Blackstone for distribution. The integrators planned on capturing the implementation margin on every enterprise deal, but labs are vertically integrating before that happens.

You can see the consequences of this playing out in Accenture’s stock price.

And we know why, AI consultancies are losing their most important pitch: “we’re the neutral integrator across model providers.” Anthropic’s JV is not neutral. It picks Claude. OpenAI’s is not neutral. It picks GPT.

Second, Goldman, Blackstone, and Apollo are distribution. Goldman talks to every CFO in America. Blackstone owns 250+ portfolio companies. Apollo has 30 million workers across its portfolio.

Third, this is why the $200 billion Google Cloud commit had to happen. You can’t sell pre-built agents to JPMorgan, Goldman, and a thousand mid-market banks without compute that scales 100x from here. The capex story and the go-to-market story go hand in hand.

The labs are competing with the consultancies now, not just each other.

The Signal

3 takes that didn’t fit above, plus one bet.

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OpenAI paused its UK Stargate data center, citing energy costs and regulation. UK industrial electricity is the highest in the IEA, 4x higher than the US, Norway, and Sweden.

But OpenAI announced Stargate UK in September 2025, when UK electricity was already the most expensive in the developed world. So what actually changed?

Two other things changed, and neither involved energy.

First, copyright went badly for OpenAI. On March 18, 2026, the UK published its Copyright and AI Impact Assessment. 88% of respondents backed mandatory licensing. The government rejected the opt-out model and backed licensing-first. Three weeks later, OpenAI paused. OpenAI can’t say “we’re leaving because you want us to pay for training data” out loud. That’s politically toxic in a country where the creative industries just won a major policy fight.

Second, the IPO. OpenAI closed $122 billion at $852 billion in late March 2026, including retail investors for the first time. Computer Weekly called the pause “a calculated move” rather than a sudden setback. Sifted’s analysis: the stated reasons “are unlikely to be the whole story... with an IPO on the horizon, it is hardly surprising that OpenAI is tightening its risk profile.”

Energy is a politically safe excuse, but copyright and IPO discipline seem like the actual triggers.

A quick note from me.

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Forward it to anyone who wants to stay ahead of the curve, or Subscribe free →

Meta Bets Every Dollar of Cash Flow on AI Infrastructure

Two days after the UK pause, the three executives who built Stargate left for Meta: Peter Hoeschele (data center planning), Shamez Hemani (engineering execution), and Anuj Saharan (supply chain). Meta then announced two deals this week that frame what the Stargate trio walked into.

CoreWeave and Meta expanded their AI cloud agreement to $21 billion through 2032, on top of September’s $14.2 billion deal.

Meta and Broadcom also announced a deal to co-develop four generations of MTIA accelerators starting at 1 gigawatt, scaling to multi-gigawatt through 2029. The chips will be the first AI silicon on TSMC’s 2nm, a node ahead of Nvidia’s Rubin.

In 2025, Meta generated $43.6 billion in free cash flow. 2026 capex guidance is $115-135 billion. At consensus revenue of ~$240 billion, cash from operations lands around $130 billion. Subtract capex and 2026 free cash flow falls between zero and $25 billion. Meta is about to spend nearly every dollar it generates on AI infrastructure.

And that money is mostly coming from Reality Labs. The division lost $19.1 billion in 2025. In January, Meta laid off 1,500 Reality Labs employees and paused most of its VR initiatives. On the Q4 call, Zuck said Reality Labs losses will “likely peak this year, then gradually reduce.”

Zuck’s TBD Lab is being built with Stargate’s architects and Broadcom’s silicon on money that doesn’t yet exist. He’s betting it will.

Anthropic Hit $800 Billion While Quietly Nerfing Claude

Last week, we wrote that Anthropic was reserving Mythos for enterprises and that frontier AI was getting less democratic. This week, the proof shipped in the product.

Fortune, VentureBeat, and a viral 6,852-session Substack analysis crystallized the same complaint: Claude has been quietly degraded over the last two months. Anthropic confirmed three changes between February and March. Adaptive thinking became the default. Effort level dropped to “medium.” Thinking is now redacted in the UI.

Anthropic denies they’re degrading users on purpose, and a Claude Code cache bug on April 13th and Claude.ai login failures on April 15th didn’t help their case.

Compute spent on a $200/month Pro subscriber can’t also be spent on a Fortune 500 CISO running Mythos at $125 per million output tokens. Microsoft already admitted to making the same call with Azure vs Copilot. Anthropic is doing the same thing without admitting it. What they call “balancing token use” is the allocation problem in softer language.

Now this week, Bloomberg reported that Anthropic is fielding unsolicited investor offers valuing it at $800 billion, more than 2x February’s $350 billion. Revenue is $30 billion ARR, up from $19 billion just a few months ago, and they’ve started early IPO talks with Goldman, JPM, and Morgan Stanley targeting October.

Investors are offering $800 billion because enterprise ARR is exploding. Enterprise ARR is exploding because Anthropic is allocating compute to enterprise.

If you’re a Pro subscriber, your compute is being reallocated to customers paying hundreds of times more per token. And Anthropic’s compute stays finite for at least another year. Per OpenAI’s own memo, Anthropic is at 1.4 gigawatts today vs OpenAI’s 1.9, and Anthropic’s response is 3.5 gigawatts starting in 2027.

The silver lining: if Anthropic keeps rationing Claude for the Fortune 500, the consumer and small-developer market is wide open.

Anthropic could always solve this with more compute, but it won’t be cheap. And when your $800 billion valuation is built on enterprise revenue, is it worth keeping consumers who pay a fraction of enterprises?

The AI Signal

3 takes that didn’t fit above, plus one bet.

1. Meta is 84% of CoreWeave’s future The $21B expansion puts Meta’s total CoreWeave commitment at $35.2B through 2032, and CoreWeave’s total backlog is only...

2. Anthropic needs a data center announcement before OctoberAn October IPO means the S-1 has to show the 3.5GW buildout is funded and contracted...

3. The UK copyright decision was the warmup The EU AI Act’s copyright provisions enter enforcement review in Q3 2026, and if the EU aligns with the UK’s licensing-first model...

My bet: Peter Hoeschele gets a VP title at Meta within...

1. Meta is 84% of CoreWeave’s future

The $21B expansion puts Meta’s total CoreWeave commitment at $35.2B through 2032. CoreWeave’s total contracted revenue backlog is ~$42B. One customer is now 84% of future revenue.

If Meta ever renegotiates terms, CoreWeave’s debt covenants are the first thing that breaks.

2. Anthropic needs a data center announcement before October

An October IPO means the S-1 has to show the 3.5GW buildout is funded and contracted. Watch for a datacenter announcement in the next 60 days. Without it, the roadshow math doesn’t work.

3. The UK copyright decision was the warmup

The EU AI Act’s copyright provisions enter enforcement review in Q3 2026. If the EU aligns with the UK’s licensing-first model, OpenAI’s training data liability extends across the entire European market. Pausing UK Stargate might not be a retreat from one country. It might be the first move in a broader European pullback.

My bet: Peter Hoeschele gets a VP title at Meta within 90 days. You don’t hire Stargate’s architect to run a division. You hire him to run THE division. MTIA + data center planning under one person is Zuck’s play to vertically integrate without relying on Jensen.

Anthropic’s newest model Mythos is supposedly incredible, and blows the doors off their rivals.

So much so that they’re giving 50 companies a preview so they can find software vulnerabilities through Project Glasswing. Companies include Amazon, Microsoft, Apple, Google, and the Linux Foundation.

Mythos has already …

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Lin Junyang, 32 years old, Alibaba’s youngest P10 executive, posted on X at midnight Beijing time on March 4: “me stepping down. bye my beloved qwen.”

Lin built Qwen from an obscure side project into the most downloaded open-source LLM series in the world. Over 700 million downloads on Hugging Face. Nearly 400 open-s…

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Salesforce quietly laid off fewer than 1,000 employees on February 10, including staff on the Agentforce AI product teams. The company didn’t announce it, the news surfaced through employee LinkedIn posts.

Five senior executives departed between December and February, including Adam Evans, the EVP wh…

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OpenAI began testing advertisements in ChatGPT on February 9. Ads appear at the bottom of responses for Free and Go tier users when contextually relevant. Paid subscribers remain ad-free.

OpenAI has hundreds of millions of free users. Monetizing them through ads, the way Google monetized se…

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A standard AI model like GPT or Gemini learns during training and then freezes. The parameters are fixed. When you send it a prompt, the model processes your input through the same static weights every time. If the world changes or the data shifts, the model does not adapt. You retrain it, which costs millions of dollars and months of compute.

Liquid neural networks work differently. Developed at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) by Ramin Hasani, Mathias Lechner, Alexander Amini, and Daniela Rus, liquid networks use continuous-time dynamics where the model’s parameters adjust based on the input it receives, even after training is complete. In plain language: the model rewires itself as new data comes in.

The original paper on Liquid Time-Constant (LTC) networks was published in 2021. The key insight was borrowed from neuroscience. The neurons in a C. elegans worm, which has only 302 neurons, can produce remarkably complex behavior because the connections between neurons are dynamic, not fixed. The time constants governing how quickly each neuron responds can change based on incoming signals. Hasani and his co-authors built that principle into an artificial neural network: instead of fixed-weight connections, the network uses ordinary differential equations (ODEs) to model how each neuron’s state evolves over continuous time.

A follow-up paper in Nature Machine Intelligence in 2022 introduced closed-form continuous-time (CfC) networks, which solved the computational overhead problem of the original liquid networks. The original LTC networks required numerically solving differential equations at each step, which was slow. CfC networks found a mathematical shortcut: a closed-form solution that achieves the same adaptive behavior without the computational cost of solving ODEs at every inference step.

The result is a model that is small, interpretable, and adaptive. Where a large language model might need billions of parameters, a liquid network can achieve strong performance on time-series and sequential data tasks with thousands to millions of parameters. A liquid network with 19 neurons was able to steer a self-driving car. You can look inside a liquid network and understand why it made a specific decision, trace which neurons responded to which inputs, and see how the time constants shifted. That kind of interpretability is nearly impossible with transformer-based models that have billions of opaque parameters.

How Liquid Networks Compare to Transformers

The technical differences between liquid neural networks and transformers are fundamental, not incremental.

Transformers process input in parallel through self-attention, which compares every token to every other token. This is powerful for language because it captures long-range dependencies, but it scales quadratically with sequence length. A 10x longer input requires 100x more compute for the attention step. Transformers are also static after training: the weights do not change regardless of what input they see.

Liquid neural networks process input sequentially through continuous-time dynamics. Each neuron’s behavior is governed by a differential equation whose parameters can shift based on the current input. This makes them naturally suited for time-series data, sensor streams, and any task where the data arrives in a sequence and the underlying conditions may change. The trade-off is that liquid networks do not handle the kind of broad, parallel language processing that transformers excel at.

State space models like Mamba (developed by Albert Gu and Tri Dao) are a third approach. Mamba offers linear-time sequence processing, compared to transformers’ quadratic cost, by using structured state spaces to model sequences efficiently. Mamba is static after training, like a transformer, but much more computationally efficient for long sequences.

The key differentiator for liquid networks is inference-time adaptation. The model changes its behavior based on input without retraining. For applications like autonomous systems, where conditions shift constantly, this is a structural advantage that neither transformers nor state space models offer.

Liquid AI: From MIT Lab to $2 Billion Unicorn

The four MIT CSAIL researchers founded Liquid AI in 2023 to commercialize their research. The company emerged from stealth in December 2023 with $46.6 million in seed funding led by OSS Capital and PagsGroup (the family office of Stephen Pagliuca). Investors included Breyer Capital, Naval Ravikant (AngelList co-founder), Tobias Lutke (Shopify co-founder), David Siegel (Two Sigma co-founder), Chris Prucha (Notion co-founder), Bob Young (Red Hat co-founder), Samsung Next, and Bold Capital Partners (Peter Diamandis).

The investor list is notable for its breadth. These are not just AI VCs. They are operators who have built platform-scale businesses and understand infrastructure economics. Tobias Lutke later became a customer through the Shopify partnership.

A year later, in December 2024, AMD Ventures led a $250 million Series A, valuing Liquid AI at $2 billion. AMD’s investment was strategic on both sides. Liquid AI committed to optimizing its models for AMD’s GPUs, CPUs, and AI accelerators. AMD got a foothold in the efficient-AI market where its hardware can compete with Nvidia on price-performance. Nvidia dominates the training of massive models, but smaller, more efficient models play to AMD’s strengths.

In total, Liquid AI has raised $297 million across two rounds. That is a fraction of what the foundation model labs have raised (OpenAI at $58 billion, Anthropic at $14 billion), but Liquid AI is not competing on the same axis. They are not trying to build the biggest model. They are trying to build the most efficient one.

The Models: From Cloud to Smartphone

Liquid AI has shipped a rapid succession of models and products since early 2025, each targeting a different point on the compute spectrum.

LFM-7B launched in January 2025 as a 7 billion parameter Liquid Foundation Model. At its size class, it was best-in-class for local deployment and latency-constrained tasks, supporting multiple languages. Unlike transformer models of similar size (Llama 3.2 7B, Mistral 7B), LFM-7B uses Liquid AI’s non-transformer architecture, which means lower memory usage and faster inference for the same parameter count. The architecture combines elements of attention, convolutions, and recurrence in a hybrid design rather than relying purely on self-attention.

Hyena Edge arrived in April 2025, designed specifically for smartphones and edge devices. This is where Liquid AI’s approach gets genuinely differentiated. Hyena Edge was designed using STAR (Synthesis of Tailored Architectures), an evolutionary framework that uses evolutionary algorithms to automatically design model architectures. STAR encodes each model design as a numeric “genome” and evolves these genomes over successive generations, selecting top performers, recombining them, and refining designs to meet specific goals like inference speed and memory efficiency on target hardware.

Starting with 16 candidate architectures and running 24 generations of evolutionary iterations, STAR produced Hyena Edge. The model replaces two-thirds of traditional grouped-query attention operators with gated convolutions from the Hyena family. Results on a Samsung Galaxy S24 Ultra: 30% lower latency than Transformer++ baselines at longer sequence lengths, 90% smaller cache size compared to transformers, and 37% cache reduction compared to hybrid models, all while matching or exceeding quality benchmarks. In 7 out of 8 tests, the STAR-evolved architectures outperformed both Transformer++ and hybrid models while reducing parameter counts by up to 13%.

Liquid Nanos debuted in September 2025 as ultra-small, task-specific models designed to run directly on iOS and Android devices. Rather than one large model handling everything, Nanos let an application outsource specific tasks to tiny, energy-efficient models running locally. They are available through LEAP (Liquid Edge AI Platform), with Apollo as a companion app for testing small language models directly on your phone.

In January 2026, Liquid AI announced new models that it says beat open-source competitors from Meta, Alibaba, and others at comparable sizes.

The Partnerships Tell the Story

The partnerships Liquid AI has signed reveal where efficient, adaptive AI has the strongest product-market fit.

Shopify signed a multi-year deal in November 2025 to license and deploy Liquid AI’s LFMs across quality-sensitive commerce workflows. The first production deployment is a sub-20 millisecond text model that enhances search. Shopify and Liquid co-developed a generative recommender system with a novel HSTU architecture. When your Shopify search returns relevant products in under 20 milliseconds, that is not a 70 billion parameter model running in a distant data center. It is a small, fast, specialized model running close to the user.

G42, the Abu Dhabi-based technology group, partnered with Liquid AIin June 2025 to deliver private, local AI solutions for enterprises. The partnership focuses on creating, training, and commercializing generative AI solutions powered by Liquid Foundation Models. For Middle Eastern enterprises with strict data sovereignty requirements, running AI locally rather than routing through U.S. cloud providers is not a nice-to-have. It is a regulatory necessity.

Brilliant Labs integrated Liquid AI into its Halo smart glasses in September 2025. Running an LLM on smart glasses means operating within severe power and compute constraints. A model that is both small and adaptive, one that can adjust its behavior based on what the wearer is seeing and doing, fits a use case that transformers cannot serve efficiently.

Alef Education partnered with Liquid AI in August 2025 to advance AI in education. AMD is optimizing its hardware stack for Liquid Foundation Models. Each partnership reinforces the same thesis: there is a large and growing market for AI that runs on the device, not in the cloud.

Why This Matters for the AI Industry

The dominant narrative in AI is that bigger models are better models. Meta guided 2026 capex to $115 billion to $135 billion, essentially all of its free cash flow. Microsoft has a $625 billion demand backlog for Azure AI compute. The infrastructure race is consuming hundreds of billions. The entire industry assumes that scale is the path to capability.

Liquid AI is building for a different future. Their thesis is that for many real-world applications, you do not need a trillion-parameter model. A drone navigating a warehouse does not need GPT-5. A smart glass displaying contextual information does not need Gemini. A commerce search engine returning results in under 20 milliseconds does not need a model that costs $3 per million tokens. These use cases need fast, efficient, adaptive AI that runs where the data is generated.

If edge AI grows as projected, with Gartner estimating over 75% of enterprise data will be processed outside traditional data centers by 2027, Liquid AI’s bet looks well-timed. The AI industry is pouring hundreds of billions into cloud infrastructure for a world where everything runs in the data center. Liquid AI is building for the world where the compute happens on the device in your hand.

The company’s challenge is scaling its approach beyond niches. Their models are not designed for the kind of broad, open-ended conversation that GPT or Gemini target. But Liquid AI has been clear about this: they are building “efficient general-purpose AI at every scale.” The $2 billion valuation and AMD’s backing suggest the market believes that not all AI needs to be big to be valuable.

Frequently Asked Questions

What are liquid neural networks?

Liquid neural networks (LNNs) are a continuous-time neural network architecture introduced by Ramin Hasani and collaborators at MIT in 2021 (Liquid Time-Constant Networks paper). The architecture uses time-continuous differential equations as the activation function rather than the discrete time steps used by conventional neural networks. The result is an architecture that adapts its dynamics over time, handles continuous sensor data efficiently, and uses far fewer parameters than transformers for sequential tasks. The original work demonstrated that 19-neuron LNNs could match larger conventional networks on autonomous driving control tasks.

Liquid neural networks architecture: how does it work?

The liquid neural network architecture replaces conventional discrete activation functions with continuous-time differential equations. Each neuron’s state evolves over time according to a learned time-constant parameter, which means the network’s effective dynamics adapt to the input signal. This produces three notable properties: parameter efficiency (LNNs typically need 10-100x fewer parameters than transformers for the same task), interpretability (the continuous-time dynamics are mathematically tractable), and robustness to distribution shift (the adaptive time constants handle inputs the network was not trained on). The architecture is most useful for sequential tasks like control, robotics, and time-series prediction.

Liquid time-constant networks Hasani 2021 equation: what is it?

The Hasani 2021 Liquid Time-Constant Networks paper introduced the core LNN equation: dx/dt = -x/τ(x,I,t,θ) + f(x,I,t,θ) ⊙ A, where x is the neuron state, I is input, τ is the learned time constant (which itself depends on state and input, making it adaptive), f is the nonlinear activation, A is a learned bias, and θ is the parameter set. The equation is a continuous-time ordinary differential equation solved during forward passes via numerical integration. The “liquid” name comes from the time constant changing dynamically with input, making the network’s effective dynamics fluid rather than fixed.

Liquid neural networks vs Mamba and RWKV in 2026?

Liquid neural networks, Mamba (state space models), and RWKV (linear attention) are three alternatives to the standard transformer architecture, each with different tradeoffs. LNNs win on parameter efficiency and interpretability for low-dimensional control tasks but have not scaled to language modeling at the largest sizes. Mamba scales to billions of parameters and matches transformer language modeling performance with linear-time inference, making it the strongest transformer alternative for LLMs in 2026. RWKV combines RNN-style efficiency with transformer-level performance and is widely deployed in mid-scale language models. For language modeling at scale: Mamba leads. For control and time-series with small parameter budgets: LNNs win. For efficient sequence modeling with simple deployment: RWKV.

Liquid neural networks research and applications in 2026?

LNN research in 2026 continues at MIT (Hasani’s group), with additional work emerging from autonomous systems labs and robotics groups. Active research areas include scaling LNNs to larger parameter counts (the architecture has not yet matched transformers at billion-parameter scale), applying them to autonomous driving and drone control where parameter efficiency matters, and combining LNN dynamics with transformer attention for hybrid architectures. Commercial deployments remain concentrated in robotics, autonomous vehicles, and edge AI where the small-parameter footprint enables deployment on constrained hardware. Language modeling applications remain limited.

Are liquid neural networks the future of AI?

Probably not as a transformer replacement at LLM scale. LNNs solve a different problem than transformers and excel in different domains. The honest 2026 view: LNNs will continue to win in robotics, autonomous control, time-series prediction on edge hardware, and any application where parameter efficiency and interpretability matter more than raw scale. Transformers and their alternatives (Mamba, RWKV) will continue to dominate language modeling and large-scale generative AI. Research interest in LNN-transformer hybrid architectures is real and may produce useful combinations, but a pure-LNN replacement of transformers at the frontier model scale is not the realistic 2026-2027 trajectory.

What Changed Recently (2026 Update)

• Q1 2026: LNN research continues at MIT with extensions to higher-dimensional control tasks. No major commercial LLM deployments using pure LNN architectures.

• Q4 2025: Mamba and RWKV continued to scale as the leading transformer alternatives for language modeling. LNNs remained focused on control and time-series applications.

• Foundational reference: Hasani et al., “Liquid Time-Constant Networks” (AAAI 2021) is still the canonical paper. For the differential equation formulation and benchmarks, start there.

SpaceX officially acquired xAI on Monday in a deal that values the combined entity at $1.25 trillion. It is the largest merger in history.

Bank valuation documents reviewed by CNBC put SpaceX between $859 billion and $1.26 trillion, and xAI between $219 billion and $294 billion. For context, OpenA…

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LMArena started as a research project. In 2023, a group of UC Berkeley SkyLab researchers built Chatbot Arena under the LMSYS (Large Model Systems Organization) umbrella. The concept was simple: show users two anonymous AI model responses side by side, let them pick the better one, and use the votes to generate rankings. No corporate benchmarks, no hand-picked evaluation sets. Just blind human preference at scale.

It worked, and Chatbot Arena became the industry’s most-cited AI leaderboard within months. Model labs began timing their releases around Arena rankings. Enterprise buyers started referencing Arena scores in procurement decisions. By mid-2025, the platform’s usage had surged 200% year-over-year.

The team rebranded to LMArena, graduated from LMSYS, and raised $100 million from Andreessen Horowitz. We covered the raise here. The platform moved from lmsys.org to arena.ai. What had been an academic experiment was now a business, and its rankings carry real financial weight.

How the Rankings Actually Work

LMArena uses the Bradley-Terry rating system, a statistical model originally developed for paired comparison experiments. It functions similarly to chess Elo ratings: models gain or lose points based on head-to-head human votes. When you visit arena.ai, you are shown two anonymous model responses to the same prompt. You pick the one you prefer. Your vote adjusts both models’ scores. Multiply that by millions of votes and you get a ranking that reflects aggregate human preference.

The key advantage over traditional benchmarks is that LMArena is harder to game. You cannot optimize for it the way you can memorize MMLU questions, because the evaluation is based on open-ended human preference across unpredictable prompts from real users. There is no fixed test set to overfit on.

LMArena also applies “style control” to its rankings, adjusting for the tendency of users to prefer longer, more verbose responses regardless of quality. Without this adjustment, models that pad their outputs with unnecessary detail would score higher. The style-controlled leaderboard better reflects actual helpfulness rather than superficial polish.

The platform now runs leaderboards across multiple categories: text (general chat), code, math, hard prompts, vision, and more. Each uses the same Bradley-Terry methodology but with category-specific prompts. This matters because a model that excels at creative writing may perform poorly on code, and the overall Elo can obscure these differences.

Where Claude Stands on the Leaderboard

As of early 2026, the LMArena text leaderboard has become a three-way race at the top. Gemini 3 Pro leads overall with an Elo score approaching 1500, making it the most broadly preferred model in blind comparisons. GPT-5.2, added to the leaderboard in December 2025, is the top performer on reasoning-specific benchmarks, particularly with extended thinking enabled. Claude Opus 4.5 holds the strongest position in coding and software engineering.

The category-specific results matter more than the overall ranking for most practical purposes. Claude Opus 4.5 was the first model to break 80% on SWE-bench Verified, scoring 80.9%. Anthropic’s coding momentum has been building since Claude Sonnet 4.5 set the standard in October 2025. SWE-bench is the benchmark that drops models into real GitHub repositories and asks them to fix actual bugs. This is not a toy coding test. The model must navigate a full codebase, understand the issue from a GitHub ticket, identify the relevant files, and produce a working patch. In 2023, the best AI systems could solve just 4.4% of SWE-bench problems. By late 2025, Claude Opus 4.5 reached 80.9%. For enterprise buyers evaluating AI for software development workflows, that trajectory is more relevant than overall Arena Elo.

On GPQA Diamond, the graduate-level science benchmark where PhD experts only score 65-74%, Gemini 3 Pro leads at 92.6%. GPQA Diamond contains 448 questions in biology, physics, and chemistry specifically designed to be “Google-proof,” meaning non-experts cannot answer them even with 30 minutes of unlimited web access. The fact that AI models now exceed human expert performance on this benchmark is itself a milestone.

On the original MMLU benchmark (57 academic subjects, multiple-choice), the differences between frontier models have compressed to the point of irrelevance. Top models all score above 88%. MMLU was the gold standard in 2023 but is now saturated and has been largely superseded by MMLU-Pro, which uses harder questions and more answer choices.

Anthropic’s strategy has been consistent. Rather than chasing overall leaderboard position, they have focused on coding performance, reliability, and safety, the metrics enterprise customers actually weigh in purchasing decisions. Claude is the only state-of-the-art model available on both AWS and Google Cloud Platform, giving it distribution advantages that no benchmark captures.

DeepSeek V3.2 deserves mention as the economic disruptor. It delivers frontier-class performance close to GPT-5 at a cost 94% lower, which does not show up on quality leaderboards but matters enormously for cost-sensitive deployments.

The Benchmark Landscape: What Each One Measures

Understanding which benchmark measures what is essential for evaluating any model claim. Here is what the major benchmarks actually test:

MMLU was useful when models were still learning factual knowledge. It is now too easy to differentiate frontier models. HumanEval tests whether a model can write correct Python functions from docstrings, but the 164 problems are relatively simple algorithms, not production code. Most frontier models score 85%+ and the differences are noise.

SWE-bench Verified is the current gold standard for code evaluation because it uses real GitHub issues that require understanding entire codebases. It is hard to saturate because the problems are complex, varied, and representative of actual engineering work.

GPQA Diamond remains one of the few knowledge benchmarks that still differentiates. Questions are created by PhD domain experts and validated to be genuinely difficult, even for other experts in the same field.

LMArena Elo captures something none of the automated benchmarks can: whether humans actually prefer one model’s output over another in open-ended conversation. Its weakness is that preference does not always correlate with accuracy, and the user base skews toward English-speaking developers.

Why Benchmarks Are Both Essential and Broken

The benchmark landscape in 2026 has a fundamental problem: Goodhart’s Law. When a measure becomes a target, it ceases to be a good measure. Model labs have become expert at optimizing for specific benchmarks without necessarily improving real-world performance.

The pattern repeats across every benchmark. A new evaluation is introduced. It differentiates models meaningfully. Labs optimize for it. Scores converge. The benchmark loses discriminating power. A harder version is introduced. The cycle starts again.

LMArena’s advantage is that it partially sidesteps this problem because the prompts come from real users, not a fixed dataset. But LMArena has its own limitations. The user base skews toward technical users who value different things than, say, a marketing team or a medical researcher. The “vibes” of a response (tone, formatting, length) can influence votes independently of substance. Model labs have been accused of optimizing outputs for conversational appeal, using techniques like generating more structured markdown or more confident-sounding language, rather than improving actual reasoning.

The Business of Benchmarks

A single leaderboard position shift on LMArena can influence millions of dollars in API revenue. When a model reaches the top of the Arena, developers try it. When developers try it, enterprises evaluate it. When enterprises evaluate it, contracts follow.

This is why competition in the benchmarking space itself is heating up. Scale AI launched SEAL Showdown in September 2025, a competing leaderboard that uses a broader, more diverse evaluator base and different methodology. Scale AI brings its data-labeling expertise to bear, arguing that its evaluators can assess factual accuracy and instruction following more rigorously than LMArena’s crowdsourced votes.

Hugging Face continues to run the Open LLM Leaderboard, now in its v2 iteration, which focuses on open-source models and uses a suite of benchmarks including MMLU-Pro, GPQA, MATH, IFEval, MuSR, and BBH.

Artificial Analysis publishes an Intelligence Index that combines quality benchmarks with pricing, latency, and throughput metrics, which is often more useful for procurement decisions than pure quality rankings.

LMArena’s $100 million raise signals that benchmark infrastructure is becoming a business in its own right. The company’s challenge is maintaining neutrality while generating revenue. If the platform that ranks models also sells services to the companies building those models, the conflict of interest becomes obvious. Hugging Face faces a similar tension with its leaderboard and model hosting business.

For anyone evaluating AI models, the best approach is to treat benchmarks as one signal among many. Arena Elo tells you about broad human preference. SWE-bench tells you about real-world coding ability. GPQA tells you about scientific reasoning. Pricing and latency data from Artificial Analysis tells you about cost efficiency. No single number captures whether a model is right for your specific use case.

Use them together, trust none of them alone.

Frequently Asked Questions

How many votes does LMArena have?

LMArena has accumulated millions of human pairwise votes across the leaderboard, with the count growing weekly as users vote on anonymous side-by-side model responses. The vote count is the foundation of the Elo-style ranking system and is part of why the leaderboard is treated as more credible than most automated benchmarks. Each vote is a human comparing two anonymous outputs and choosing the better one, which produces a different signal than synthetic benchmarks like MMLU or HumanEval.

Did LMArena spin out from LMSYS in 2025?

Yes. LMArena spun out from LMSYS (Large Model Systems Organization) in 2025 as a standalone company, raising the $100M round covered in this post. LMSYS itself originated at UC Berkeley SkyLab as the academic research group that created Chatbot Arena in 2023. The 2025 spin-out reflected the leaderboard’s growth from academic side project into industry-standard ranking system, which made independent commercial structure necessary.

Where did LMArena come from? UC Berkeley LMSYS origin?

LMArena originated as Chatbot Arena, a research project from UC Berkeley SkyLab researchers operating under the LMSYS group in 2023. The project let users vote on anonymous head-to-head LLM responses and aggregated the votes into a public Elo-style leaderboard. The transparency and human-in-the-loop methodology made it more trusted than self-reported model benchmarks. By 2024 it had become the de facto reference leaderboard for frontier AI models, and the 2025 spin-out plus $100M raise formalized the commercial path.

What is the LMSYS Chatbot Arena business model and funding?

Pre-spin-out, Chatbot Arena ran as an academic research project at UC Berkeley with no commercial business model. Post-spin-out, LMArena raised $100M to build commercial infrastructure around the leaderboard while preserving the public benchmark. The realistic 2026 monetization paths include enterprise model evaluation services, API access to the underlying vote data, custom benchmark deployments for AI labs, and consulting on model selection for enterprise buyers. The public leaderboard stays free; the surrounding services are the revenue model.

Where does Claude rank on the LMArena leaderboard?

Claude has held top-tier LMArena positions across multiple model generations, with Claude Opus 4.6 and Sonnet 4.6 typically appearing in the top 5 across overall and category-specific leaderboards in 2026. Specific rank shifts week to week as new models are added and vote counts grow. The post covers the surge in Claude’s benchmark positioning and what it means for enterprise buyers evaluating models. Check LMArena.ai directly for the current weekly rankings because positions change frequently.

Why is LMArena considered an influential AI benchmark?

Three reasons. First, the human pairwise voting methodology produces a signal closer to real-world model preference than synthetic benchmarks that test specific capabilities in isolation. Second, the open and transparent process (anyone can vote, votes are aggregated publicly) makes it harder for AI labs to game the rankings compared to closed benchmarks. Third, the leaderboard now influences enterprise purchasing decisions, which means model rankings have real revenue implications for AI labs. That commercial weight is why LMArena raised $100M and why labs invest heavily in performing well on it.

What does the LMArena $100M raise mean for AI model rankings?

The $100M raise gives LMArena resources to scale infrastructure, expand the leaderboard categories, and build commercial services around the benchmark. For AI labs, it means the benchmark is here to stay as an industry-standard reference, which raises the stakes of leaderboard positioning. For enterprise buyers, it means LMArena will likely become a more sophisticated decision tool than the current public leaderboard, with commercial offerings around custom evaluation, vote data access, and benchmark consulting. The 2026 trend: the leaderboard becomes a market-shaping force, not just a research artifact.

What Changed Recently (2026 Update)

• April 2026: LMArena leaderboard continues weekly updates with new model additions. Claude Opus 4.6 and Sonnet 4.6 hold top-tier positions across multiple categories. GPT-5 and Gemini 2.5 Pro compete closely at the top.

• Q1 2026: LMArena expanded category-specific leaderboards (coding, reasoning, multi-turn, vision) beyond the original overall ranking, giving enterprise buyers more granular model selection signal.

• Q4 2025: LMArena spun out from LMSYS as a standalone company and raised $100M to build commercial infrastructure. The public leaderboard methodology stays unchanged; the spin-out formalizes the commercial path.

• Methodology note: The leaderboard uses an Elo-style ranking system from anonymous human pairwise votes. Vote count grows weekly. Always check LMArena.ai for the current week’s rankings because positions shift as new models launch and vote counts accumulate.

If you are building an AI startup in 2026, the first financial decision you make is probably which cloud provider’s credits to accept. AWS Activate, Microsoft for Startups, and Google Cloud for Startups all offer significant free compute to early-stage companies. The amounts vary, but the strategy is the same: subsidize startups now, collect infrastructure revenue for decades.

AWS Activate is the largest and oldest of these programs. It has two main paths. The Portfolio tier is for startups backed by a qualifying VC, accelerator, or incubator, and offers up to $100,000 in AWS credits valid for two years, plus business support and training. The Founders tier is self-serve for any early-stage startup and provides $1,000 in credits with basic support. For AI-focused startups in the Y Combinator network, AWS has extended credits to $500,000 per startup, redeemable against Amazon Bedrock, SageMaker, and EC2 GPU instances.

Since launching Activate, AWS claims to have distributed more than $6 billion in credits to startups globally, providing nearly $1 billion per year since 2020. The scale of the program is unmatched. AWS says that 80% of the world’s unicorns run on its infrastructure, and as recently as June 2024, Matt Wood, AWS VP of AI Products, cited PitchBook data showing 96% of all AI/ML unicorns run on AWS. That figure has come down to “more than 80%” as of October 2025, reflecting the rapid growth in AI unicorns and increased competition from Azure and GCP. But the pattern is clear: the credits work.

Beyond Activate, AWS committed $230 million specifically to generative AI startups in 2024, providing additional credits, mentorship, and go-to-market support through its Generative AI Accelerator. We covered the initial $300K credit announcement when it launched. The company also runs the AWS Startups Ramp program, which gives ISV partners credits to build SaaS products on AWS infrastructure. Each program funnels startups deeper into the AWS ecosystem.

How the Three Programs Compare

Google Cloud for Startups is now the most generous on paper, offering up to $350,000 over two years for AI-first startups through its Scale Tier AI track. Year one covers 100% of costs up to $250,000; year two covers 20% up to $100,000. The program also includes dedicated support credits and access to Google’s AI engineering team. Google is aggressively targeting AI startups to build on Vertex AI and its TPU infrastructure, where it has a meaningful cost advantage over Nvidia GPU-based alternatives. The program has four tiers: Start (pre-funded), Scale (early-stage), Scale AI (AI-first companies), and Scale Web3.

Google also runs the AI Futures Fund through DeepMind and Google Labs, which co-invests up to $2 million in selected startups through partnerships like Accel Atoms. Gradient Ventures, Alphabet’s AI-focused venture arm, invests $1 million to $30 million at Seed to Series A stages. The credits are just one layer of a broader ecosystem play.

Microsoft for Startups had offered up to $150,000 in Azure credits through its Founders Hub program. That changed abruptly on July 2, 2025, when Microsoft restructured the program with almost no warning. Unfunded startups now qualify for only $5,000 in credits ($1,000 initially, valid for 90 days, with an additional $4,000 valid for 180 days after verification). Investor-backed startups can access $100,000 through the new Investor Network track, but only with a referral code from an affiliated VC.

The backlash was immediate and severe. Founders who had built their entire infrastructure around Azure discovered overnight that their credit pipeline was cut. On Microsoft’s own Q&A forums, startups reported that the policy change “wrecked budgets and may sink the entire project.” One founder noted they had been actively building toward the Grow milestone ($25,000 in credits) when the announcement fundamentally changed the rules. The core problem: once you have trained models and built pipelines on one cloud, switching costs are enormous. Part of the problem, as The Register reported, was that “once you’ve actually built around Azure, switching to another platform causes a host of problems.” Microsoft said they were “evolving to better serve customers.” The founders who had been building in good faith on Azure saw it as a bait-and-switch.

ProviderMax CreditsAI-Specific TierKey RequirementCredit DurationAWS Activate$100K-$500KYes (Bedrock/SageMaker)Accelerator or VC affiliation2 yearsGoogle Cloud for Startups$350KYes (Vertex AI/TPU)Pre-Series B, approved provider2 yearsMicrosoft for Startups$5K-$100KNo separate tierInvestor referral for $100K tier90-180 days (basic)

Why Cloud Providers Subsidize Startups

The economics are straightforward. Cloud credits cost AWS, Google, and Microsoft very little relative to marginal infrastructure costs. The compute capacity exists whether startups use it or not. But the lifetime value of a startup that grows into a major enterprise customer is enormous, often generating millions per year in recurring cloud spend.

Consider Anthropic. The company started on AWS and is now Amazon’s primary AI model training partner, with Amazon investing $8 billion into the company. Anthropic uses AWS Trainium and Inferentia chips to train and deploy its foundation models. Anthropic has since expanded to all three major clouds and recently hit a $350 billion valuation. Anthropic’s cloud spend with AWS is not public, but with training costs projected to exceed $100 billion by 2029 and the company targeting $15 billion in 2026 revenue, the infrastructure bill is massive. That relationship began when Anthropic was a startup choosing where to build.

Other major AI companies that built on AWS early include Perplexity AI, which has been using Amazon SageMaker since the inception of its API service and chose AWS after evaluating several cloud providers. Stability AI trained Stable Diffusion models on SageMaker with EC2 P4 instances. Hugging Face, AI21 Labs, and Runway all run on AWS infrastructure. These companies are now deeply integrated into AWS services in ways that would take months of engineering to unwind.

The lock-in dynamic is the real product. Training an AI model on one cloud provider creates deep dependencies: data pipelines, model artifacts, inference endpoints, networking configurations, IAM roles, monitoring dashboards, and CI/CD workflows. Every integration point is a switching cost. The credits are the hook. The switching costs are the moat.

What AI Startups Actually Need to Know

The smartest startups apply to multiple programs simultaneously. You can legitimately hold credits from AWS, Google Cloud, and Microsoft at the same time, as long as you have genuine use cases for each platform. Many companies use a multi-cloud strategy specifically to maximize credits and avoid over-dependence on a single provider.

Here is a practical breakdown by use case:

If you are training models from scratch, AWS and Google Cloud offer the most compute-heavy credit packages. Google’s TPU credits can be significantly cheaper per FLOP for transformer training workloads. A single TPU v5p pod can match or exceed H100 performance at lower cost for large-scale training runs. AWS offers broader GPU options through EC2 P5 instances with H100s and is rolling out Trainium2 instances. If your training framework supports JAX, Google’s TPU path is worth evaluating seriously. If you are using PyTorch, the GPU ecosystem has better out-of-the-box support.

If you are building on top of foundation models via API, AWS Bedrock credits give you access to Claude(Anthropic), Llama (Meta), Mistral, and other models through a single billing relationship. Google’s Vertex AI offers Gemini models at what is often the lowest price per token in the market. Azure offers GPT-4o and GPT-5 through OpenAI’s API. The credit programs effectively let you evaluate these models at zero cost before committing.

If you are bootstrapped and unfunded, Google Cloud is now the clear winner at up to $350,000 without requiring investor backing. Microsoft’s $5,000 for unfunded startups is essentially a free trial, not a startup program. AWS sits in the middle, with meaningful credits available through accelerator partnerships. Some accelerators (Y Combinator, Techstars, Antler) have specific AWS credit packages that exceed the standard Activate tiers.

If you are already funded, negotiate directly. Standard credit programs are starting points. Startups with significant compute needs and VC backing can often negotiate custom credit packages well beyond the published tiers. Cloud sales teams are compensated on new logos and workload acquisition, especially for AI companies with high growth potential.

Common mistakes to avoid: Do not burn credits on expensive instance types for non-critical workloads. Use spot instances for interruptible training runs. Set budget alerts before you start, not after. Credits expire, often on hard deadlines with no extensions, so plan your compute usage calendar around expiration dates. And do not assume the program terms will stay the same. Microsoft’s July 2025 restructuring proved that credits you are counting on can disappear with minimal notice.

The Strategic Picture

The cloud credit war is a proxy for a larger battle. AWS, Azure, and Google Cloud are fighting not just for startup customers, but for the foundational infrastructure decisions that will determine where AI workloads run for the next decade. Every model trained on AWS is a model that will likely be served on AWS. Every pipeline built on Vertex AI is a pipeline that probably stays on Google Cloud.

The numbers tell you how seriously the cloud providers take this. AWS has given away $6 billion in credits. Google is offering $350,000 per AI startup. Microsoft restructured its entire program to focus resources on investor-backed companies with higher growth potential. These are not marketing expenses. They are customer acquisition costs for infrastructure relationships that can last decades.

The irony is that the startups receiving these credits are often building the AI products that will eventually compete with the cloud providers’ own offerings. Amazon invested $8 billion in Anthropic while simultaneously building its own Nova foundation models. Google offers credits to startups building on Gemini competitors. The cloud providers have decided that owning the infrastructure layer is more valuable than winning the model layer. As long as AI companies need compute, the cloud providers collect rent.

For founders, the takeaway is simple: take the credits, build on the platform that best fits your technical needs, but never mistake free compute for a free lunch. Structure your architecture so that migration remains possible. Use abstraction layers where practical. And remember that the cloud providers are playing a longer game than you are. The credits expire. The switching costs do not.

Frequently Asked Questions

What is the AWS startup program?

The AWS startup program is AWS Activate, a structured offering that provides cloud credits, technical support, training, and partner perks to early-stage and growth-stage startups. The program runs in tiers based on company stage and accelerator/VC affiliation. Founders typically enter through Activate Founders ($1,000 in credits, self-serve), Activate Portfolio ($25K-$100K via accredited VCs and accelerators), or Activate Builders ($1K-$5K direct application). The 2026 program also includes the AWS Activate Generative AI offering with up to $300K in credits for qualifying AI startups.

What is the AWS startup funding or grant amount?

AWS does not provide direct cash funding or grants. The “funding” comes as cloud credits ranging from $1,000 (Activate Founders self-serve tier) to $100K (Activate Portfolio via accredited VCs and accelerators) to $300K (Activate Generative AI for qualifying AI startups). Credits expire 12-24 months after issuance. The credit value is real for cloud-heavy startups but cannot be cashed out, transferred, or applied to non-AWS services.

AWS cloud credits for startups: what’s available in 2026?

AWS cloud credits for startups in 2026 break down by tier. Activate Founders: $1,000 self-serve credits, available to any startup founder. Activate Portfolio: $25K-$100K via accredited VCs, accelerators, and incubators (the partner organization sponsors your eligibility). Activate Builders: $1K-$5K direct application without VC affiliation. Activate Generative AI: up to $300K for AI startups using AWS Bedrock, SageMaker, or Trainium. All credits come with technical support, AWS training, and partner network access. Credits expire 12-24 months after issuance, so don’t hoard them.

Best cloud provider free credits for AI startups in 2025-2026?

For AI startups specifically in 2026, the leading cloud credit programs are AWS Activate Generative AI (up to $300K), Microsoft for Startups (up to $150K Azure credits with $25K Founders Hub baseline), Google Cloud for Startups (up to $100K-$350K depending on accelerator partnership), and NVIDIA Inception ($100K+ in software, hardware discounts, and support). For pure-credit dollar value, AWS Activate Generative AI tops the list. For breadth of platform value (development tools, AI APIs, partner support), Microsoft for Startups Founders Hub is often the strongest first stop because the $25K baseline requires no VC affiliation. Apply to multiple programs.

AWS Activate vs Google for Startups vs Microsoft for Startups in 2026?

Side-by-side for 2026: AWS Activate offers up to $100K (Portfolio tier) or $300K (Generative AI tier) but requires VC/accelerator affiliation for the higher tiers. Microsoft for Startups Founders Hub gives $25K Azure credits self-serve plus $150K with VC affiliation, with strong AI tooling integration. Google for Startups offers up to $100K-$350K via accelerator partnerships, with strong Vertex AI access for AI-native companies. AWS wins on credit ceiling for AI-specific use cases. Microsoft wins on self-serve baseline and Founders Hub breadth. Google wins on AI-native tooling depth. Most startups apply to all three because the programs are not mutually exclusive.

Can I use multiple cloud credit programs at the same time?

Yes. AWS, Microsoft, and Google cloud credit programs are not mutually exclusive, and most startups apply to multiple programs simultaneously. The practical strategy is to architect your stack around the largest single credit grant (often Microsoft for Startups Founders Hub for the self-serve baseline, then AWS Activate or Google for Startups for the larger tiers via accelerator affiliation) and use the smaller programs for specific workloads where the platform fits. Be mindful of credit expiration dates so the credits get used before they lapse. Track credit burn against the 12-24 month expiration window for each program.

AWS Activate generative AI $300K credit: how do I qualify?

The AWS Activate Generative AI $300K credit tier requires startups to be working on generative AI products, use qualifying AWS services (Bedrock, SageMaker, Trainium, or Inferentia), have a relationship with an AWS-recognized VC or accelerator, and apply through the Activate Portfolio path. Self-serve startups without VC affiliation typically max out at the lower Activate tiers ($1K-$5K). The $300K tier is competitive and not automatic. Submit through your AWS-recognized VC or accelerator’s Activate portal for the strongest application.

AWS Activate eligibility: do I need a VC to qualify?

No, but VC or accelerator affiliation unlocks the higher credit tiers. Without VC affiliation, you can apply to AWS Activate Founders ($1,000 credits self-serve) or Activate Builders ($1K-$5K direct application). With VC or accelerator affiliation through the Activate Portfolio program, you unlock $25K-$100K and the AI-specific $300K tier. Most major US accelerators (Y Combinator, Techstars, 500 Global) and many VCs are AWS-recognized partners. Check the Activate partner directory before assuming you need to apply self-serve.

What Changed Recently (2026 Update)

• April 2026: AWS Activate program tiers held steady. Generative AI $300K credit tier remains the largest single AI-specific cloud credit grant available.

• Q1 2026: Microsoft for Startups Founders Hub reaffirmed the $25K self-serve baseline plus $150K with VC affiliation. Google for Startups expanded Vertex AI access for AI-native participants.

• Q4 2025: AWS launched the Activate Generative AI tier with up to $300K credits for qualifying AI startups, raising the ceiling above Microsoft and Google’s standard programs.

• Methodology note: Cloud credit programs change quarterly. Always check each provider’s current program page for the latest tier amounts and eligibility before applying.

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