Artificial Intelligence

The AI compute gap: Enterprises are buying infrastructure faster than they can measure what it costs

Published

3 months ago

July 18, 2026

Enterprises are spending big on AI infrastructure — but they can barely see where the money goes

New research from VentureBeat paints a stark picture: the vast majority of enterprises are pouring money into AI compute while flying nearly blind on cost. The report, drawn from a Q2 2026 survey of 107 organizations with over 100 employees, identifies what it calls an AI compute gap — the widening distance between how aggressively companies invest in AI hardware and how poorly they track its economics.

Only 21% of respondents run AI in production at scale. Yet spending intentions are already racing ahead of that maturity. The single largest area enterprises plan to evaluate over the next year is AI-specialized clouds (45%) — a category almost none of them use today. Meanwhile, the compute they already own sits mostly idle: 83% report GPU utilization of 50% or less. Fewer than half (44%) rigorously track what their AI compute actually costs.

“Enterprises are buying more infrastructure faster than they can account for what they already own,” the report states. That gap is the central tension of the moment.

GPU utilization is abysmal — and largely unmeasured

Perhaps the most striking number in the study: 83% of enterprises that operate GPUs report utilization at or below 50%. Nearly half (49%) run at 25% or below. Only 12% clear the 50% mark. Another 8% don’t measure utilization at all.

Idle accelerators are expensive accelerators. A single Nvidia H100 can cost tens of thousands of dollars. Let whole clusters sit half-empty, and the waste compounds fast. The report calls this the clearest single measure of the compute gap: enterprises plan to buy more GPUs and specialized compute while the capacity they already own sits substantially unused.

The measurement problem runs deeper than utilization. Fewer than half of enterprises (44%) rigorously track the cost and return of their AI compute. Another 39% track only partially. Twenty percent cannot quantify it yet, and 6% have not prioritized it at all.

That’s a problem because total cost of ownership (TCO) is the second-most important factor when enterprises choose an AI infrastructure provider — cited by 35% of respondents. Integration with the existing stack ranks first (41%). Headline price? Cost per million tokens matters to just 8%, dead last. “Enterprises are choosing providers on an economic basis they mostly cannot yet measure,” the report notes.

A switching wave is building — most within the year

Enterprises are not loyal to their current infrastructure vendors. A clear majority (64%) plan to switch or add an infrastructure provider within twelve months. Even more striking: 38% intend to do so within the next quarter. That is unusually high churn intent for a category as foundational as compute.

Where does that interest point? Mostly at the incumbents. Microsoft Azure and Google Cloud each draw 33% switching consideration, followed by OpenAI (30%) and Gemini (22%). The report suggests much of the near-term movement is reshuffling among the majors and consolidating spend — not defecting to new entrants. The neocloud interest is a 12-month evaluation thesis; the switching in the next quarter is mostly incumbents trading share.

The next dollar goes to infrastructure they don’t yet run

Here is the report’s sharpest tension. The single most-cited planned evaluation area — AI-specialized clouds, at 45% — is the very category almost none of these enterprises use today. The specialized “neocloud” GPU providers that dominate AI-infrastructure headlines — CoreWeave, Lambda, Crusoe, Nebius and peers — register at or near zero among these enterprises today.

Nearly a third (32%) intend to evaluate non-Nvidia accelerators. Twenty-eight percent plan to look at next-generation Nvidia silicon. Even decentralized compute networks (16%) and sovereign compute (11%) draw meaningful interest. Read against current usage, this is not incremental — it is the leading edge of a re-platforming.

The direction-of-travel question tells the same story: every infrastructure approach is net-expanding, but specialized AI clouds carry the highest net momentum (+24), edging out even the hyperscalers (+22). Enterprises are preparing to move a meaningful share of AI compute off the general-purpose cloud.

The next bottleneck: memory, not compute

The report also flags a frontier constraint that is barely on most enterprises’ radar. As large-scale inference scales, the binding constraint shifts from GPU compute to memory bandwidth — specifically KV-cache capacity. Asked how they would address this shift, enterprises scatter: Dell leads at 31%, Nvidia follows at 16%, and the rest fragments across storage vendors, open-source tooling, and model-level efficiency techniques.

Most telling: roughly one in five (18%) either do not recognize the constraint or have not begun to address it. “For a shift that will reshape inference cost and architecture, this is an early and unsettled market,” the report notes. It is the next chapter of the compute gap, arriving before most have closed the current one.

What this means for enterprise AI strategy

The report’s bottom line is blunt: the compute gap is not a capacity problem that more hardware will solve on its own. It is, first, a problem of seeing what the hardware already costs.

For enterprises, the implications are concrete. Before committing to specialized clouds or alternative accelerators, organizations should invest in instrumentation — utilization monitoring, cost allocation, TCO modeling. Without that visibility, the next round of spending risks repeating the same inefficiencies at a larger scale.

Satisfaction with current infrastructure is moderately positive (4.0 on a five-point scale) but softest on value for money — the dimension hardest to judge without measurement. That softness is a signal. Enterprises that build cost visibility now will be better positioned to evaluate the specialized clouds and alternative accelerators they plan to assess. Those that don’t will be buying the next layer of infrastructure as blind to its economics as the last.

The open question for later waves is whether enterprises build that visibility before the re-platforming arrives — or after.

Artificial Intelligence

XDOF, three months out of stealth, is already closing in on a $1.2B Series B

Published

1 month ago

September 6, 2026

Social Media Admin

From stealth to unicorn talk in record time

Three months. That’s how long XDOF has been out in the open. And already, the robotics data startup is in late-stage conversations to raise a Series B at a valuation hovering around $1.2 billion, according to multiple sources familiar with the negotiations. The round would be led by 8VC.

Not bad for a company that didn’t even exist publicly until June.

XDOF was co-founded in 2024 by UC Berkeley researchers Philipp Wu (CEO) and Fred Shentu (CTO). Their origin story traces back to a research project called GELLO — a low-cost teleoperation system that lets a human operator control a robotic arm from a distance. The goal? Generate training data for robots. That work produced an influential paper in robotics and, eventually, a company.

The startup’s Series A, a $70 million round announced in June, drew participation from Thrive Capital, Andreessen Horowitz, Lux, and Spark Capital. At the time, XDOF wasn’t planning to raise again so soon. But the market had other ideas.

Why investors are knocking on XDOF’s door

The reason for the sudden interest? Growth. Real, measurable growth.

XDOF’s annualized revenue is approaching $50 million, sources say. That kind of traction, so soon after a Series A, tends to make venture capitalists sit up and take notice. It also tends to make them pick up the phone.

“They weren’t out raising,” one person familiar with the situation told TechCrunch. “The VCs came to them.”

Terms aren’t final, and the total capital being raised remains unclear. TechCrunch couldn’t confirm whether the $1.2 billion valuation includes the new funding or sits on top of it. Both XDOF and 8VC declined to comment.

The Scale AI for physical robots

XDOF’s pitch is straightforward: it builds the data pipelines, collection tools, and annotation systems that frontier AI labs and robotics companies would rather not build themselves. Think of it as an outsourced data-supply chain for the robotics industry.

Investors describe XDOF as the Scale AI or Mercor of physical robotics — a nod to the data-labeling giants that powered the AI boom. The comparison makes sense. Large language models trained on the entire internet. Physical robots? They don’t have that luxury. There’s no massive, ready-made dataset of real-world robot interactions sitting online. That scarcity makes data collection the critical bottleneck on the road to general-purpose machines.

Wu felt that bottleneck firsthand as a PhD student. His research on how robots learn from large datasets kept hitting the same wall: “large-scale data to work with” simply didn’t exist, he told TechCrunch in June.

Building the ABC dataset

XDOF is tackling that problem head-on. The startup is partnering with UC Berkeley’s AI Research lab to release what it believes is the largest collection of high-quality robot training data ever assembled. The dataset is called ABC.

Collecting that data requires a hybrid approach. XDOF combines remote robot teleoperation with human collectors who wear sensors to record everyday tasks. Think folding clothes. Flattening boxes. The mundane, physical chores that robots still struggle to master.

The company plans to hire and train teams of data collectors around the world. Two main roles are emerging:

Teleoperators who steer robots remotely to demonstrate tasks
Egocentric operators who wear body sensors to capture natural movement data

Early traction and the competitive landscape

XDOF has already signed up 20 customers, including several frontier AI labs, according to previous statements to TechCrunch. That customer base, combined with the revenue trajectory, helps explain the valuation chatter.

But XDOF isn’t alone in this niche. Other startups chasing real-world data for robot training include Mecka AI. And the human-data platforms that started with LLMs — like Scale AI and Micro1 — are expanding beyond text and images into physical domains.

The race to build the data infrastructure for physical AI is heating up. Whoever wins it will effectively control the fuel supply for the next generation of robots. That’s a position worth paying up for.

Whether the $1.2 billion valuation holds remains to be seen. Deals at this stage can shift. But the fact that XDOF is even in this conversation — three months after emerging from stealth — says something about the demand for what it’s building.

For more on how data is shaping the future of AI, check out AI data labeling trends and robotics funding rounds in 2024.

Artificial Intelligence

New York City Pulls AI From Younger Classrooms—Here’s Why It Matters

Published

1 month ago

September 6, 2026

Social Media Admin

New York City Just Hit Pause on AI in Classrooms

New York City Public Schools is drawing a hard line: no generative AI for students from 2-K through eighth grade during the 2026-2027 school year. That’s over half a million kids walking into classrooms next week without access to AI-powered tools.

The district says it will remove software with student-facing AI features and block AI companion chatbots. High schoolers? They’re exempt. This isn’t a blanket ban—it’s a targeted move to decide when kids should actually start using the technology.

Mayor Zohran Mamdani put it bluntly: “The tech industry wants us to believe that AI-powered early education is not only inevitable, but necessary. We do not see it that way.”

Why NYC Is Worried About AI for Younger Students

The fear isn’t just that a kid will ask ChatGPT to write an essay. City officials want younger students to build core skills—critical thinking, creativity, communication—without leaning on AI as a crutch. They’re also pushing for stronger human connections in classrooms, not another screen.

Schools Chancellor Kamar Samuels said the city refuses to assume that innovation automatically equals more technology in front of students. It’s a deliberate slowdown, and it follows last year’s bell-to-bell cellphone ban that already limits device use during school hours.

What Happens When Kids Reach High School?

AI doesn’t vanish once students hit ninth grade. Instead, the district plans to roll out AI literacy classes twice a year. The goal? Teach teenagers how to think critically about the technology before they become dependent on it.

That’s a different approach from just saying no. It’s about timing—letting younger minds develop without AI, then giving older students the tools to question it.

The National Battle Over AI in Education

NYC’s decision sits at the center of a much bigger fight. The White House has pushed educators to embrace AI responsibly. Some teachers already use it to craft lesson plans, give feedback, or break down tough subjects. But not everyone’s on board.

The Department of Health and Human Services recently gathered childhood experts to talk about excessive screen time. Officials have also called for tougher safeguards around social media and AI. The message? Kids are spending too much time in front of screens, and AI might make it worse.

A Bold Experiment With Zero AI

Here’s what makes NYC’s move so interesting: instead of asking how much AI younger students should use, the largest school district in the country is starting with none. For one academic year, they’re testing whether classrooms are better off with AI kept outside the door.

That’s a radical stance, and it’s not without critics. Some educators argue AI can personalize learning or help struggling students catch up. But NYC is betting that a year without AI will reveal what kids actually need—not what tech companies think they need.

What This Means for Parents and Teachers

If you’re a parent in NYC, expect changes. AI-based apps may disappear from your child’s school day. Teachers will need to plan lessons without generative AI tools. And students in grades 2-K through 8 will rely more on traditional methods—paper, pencils, and human interaction.

For teachers elsewhere, this could be a signal. NYC is the biggest district to take this stance, and its findings could shape policies nationwide. The next year will be watched closely by educators, policymakers, and tech giants alike.

What Happens Next?

The district will spend the year studying how generative AI affects students before deciding what comes next. That research could lead to a permanent ban, a partial rollout, or something entirely different.

For now, NYC is making a statement: childhood shouldn’t be an AI beta test. Whether that’s the right call or a step backward, we’ll know more in 2027. Until then, the debate over AI in schools just got a lot more interesting.

If you’re curious about how AI is shaping other areas, check out our take on AI in education trends or classroom technology policies.

Artificial Intelligence

Meta’s Muse Spark 1.3 takes on GPT-5.6 and Claude — but can it really win?

Published

1 month ago

September 5, 2026

Social Media Admin

Meta just fired a serious shot in the AI arms race

The company quietly unleashed Muse Spark 1.3, its most advanced AI model to date, and it’s aiming straight at the top dogs. Developers can already access and pay for the update, which Meta says represents a massive leap forward in performance.

It won’t stay confined to the developer sandbox for long. Over the coming weeks, the model will roll out across Instagram, Facebook, and the Meta AI assistant — putting it in front of billions of everyday users.

What makes Muse Spark 1.3 actually different?

Meta’s Chief AI Officer, Alexandr Wang, didn’t mince words. He called this “the biggest jump so far on model performance,” pointing to serious gains in two areas: coding and agentic tasks — the kind where the AI acts on your behalf rather than just answering questions.

But here’s the catch. Wang told Bloomberg that Muse Spark 1.3 is competitive with Anthropic’s Claude Fable 5.1 and even beats OpenAI’s GPT-5.6 Sol at coding. That’s a bold claim, especially with OpenAI’s upcoming Astra model lurking in the wings.

Take those comparisons with a grain of salt, though. Benchmarks can be gamed, and a model that crushes one test might stumble on a totally different task. Real-world performance is what actually matters.

Efficiency gains under the hood

Wang broke down a few upgrades that set Muse Spark 1.3 apart:

25% fewer tokens needed to complete the same job — meaning lower costs and faster responses
Multi-workflow handling — it can juggle several tasks at once instead of forcing separate sessions
Better context retention across long, complicated instructions
Self-awareness of limits — the model now pauses to ask for clarification before taking any irreversible action

That last point is quietly important. AI that knows when it doesn’t know is a big step toward trustworthiness, especially for agentic use cases.

Pricing stays flat, adoption explodes

Here’s something developers will appreciate: Meta isn’t raising prices. Muse Spark 1.3 costs the same as its predecessor, Muse Spark 1.2. That’s a smart move when rivals are hiking rates.

Wang told Bloomberg that adoption on Meta’s developer platform has been strong — some users are burning through trillions of tokens every week. Those numbers suggest real usage, not just hype.

What about open-source fans? Meta hasn’t decided whether it will release the model’s weights — the blueprint that lets outside developers build on top of it. The older Muse Spark 1.2 weights are still headed for release, but the new model’s future remains unclear.

The bigger picture: Meta’s spending spree continues

Meta is still pouring billions into AI infrastructure, and Muse Spark 1.3 is the clearest signal yet that the company believes it’s closing the gap with OpenAI and Anthropic. Whether that’s true or just corporate bravado will play out in the benchmarks and real-world deployments over the coming months.

For now, the model is available to developers, and the app rollout is imminent. If you’re building on Meta AI tools, this update is worth a serious look. And if you’re just a curious user, you’ll likely meet Muse Spark 1.3 in your Instagram feed sooner than you think.

One thing’s certain: the AI race just got a lot more interesting.