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Amazon and Google are winning the AI capex race — but what’s the prize?

Sometimes, it can seem like the AI industry is racing to see who can spend the most money on data centers. Whoever builds the most data centers will have the most compute, the thinking goes, and thus be able to build the best AI products, which will guarantee victory in the years to come. There are limits to this way of thinking — traditionally, businesses eventually succeed by making more money and spending less — but it’s proven remarkably persuasive for large tech companies.

If that is the game, Amazon does seem to be winning.

The company announced in its earnings on Thursday that it projects $200 billion in capital expenditures throughout 2026, across “AI, chips, robotics, and low earth orbit satellites.” That’s up from the $131.8 billion in capex in 2025. It’s tempting to attribute the whole capex budget to AI. But unlike most of its competitors, Amazon has a significant physical plant, some of which is being converted for use by expensive robots, so the non-AI expenses aren’t so easy to wave away.

Google is close behind. In its earnings on Wednesday, the company projected between $175 billion and $185 billion in capital expenditures for 2026, up from $91.4 billion the previous year. It’s significantly more than the company spent on fixed assets last year, and significantly more than most of its competitors are spending.

Meta, which reported last week, projected $115 billion to $135 billion in capex spending for 2026, while Oracle (once the poster child for AI infrastructure) projects a measly $50 billion. Microsoft doesn’t have an official projection for 2026 yet, but the most recent quarterly figure was $37.5 billion, which pencils out to roughly $150 billion, assuming it keeps up. It’s a notable increase, and one that has led to investor pressure on CEO Satya Nadella — but it still puts the company in third place.

From within the tech world, the logic here is simple. The revolutionary potential of AI is going to turn high-end compute into the scarce resource of the future, and only companies that control their own supply will survive. But while Google, Amazon, Microsoft, Meta, Oracle, and others are frantically prepping for the compute desert of the future, their investors aren’t convinced. Each company saw its stock price plummet as investors balked at the hundreds of billions of dollars being committed, and companies with higher spends tended to drop more.

Crucially, this isn’t just a problem for companies like Meta that haven’t figured out their AI product strategy yet. It’s everyone — even companies like Microsoft and Amazon with a robust cloud business and a straightforward take on how to make money in the AI era. The numbers are simply too high for investor comfort.

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Investor sentiment isn’t everything — and in this case, it may not do much to change the industry’s mind. If you believe AI is about to change everything (and the argument is pretty compelling at this point), you’d be a fool to change course just because Wall Street got jumpy. But going forward, Big Tech companies will be under a lot of pressure to downplay how expensive their AI ambitions really are.

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ElevenLabs CEO: Voice is the next interface for AI

ElevenLabs co-founder and CEO Mati Staniszewski says voice is becoming the next major interface for AI – the way people will increasingly interact with machines as models move beyond text and screens.

Speaking at Web Summit in Doha, Staniszewski told TechCrunch voice models like those developed by ElevenLabs have recently moved beyond simply mimicking human speech — including emotion and intonation — to working in tandem with the reasoning capabilities of large language models. The result, he argued, is a shift in how people interact with technology. 

In the years ahead, he said, “hopefully all our phones will go back in our pockets, and we can immerse ourselves in the real world around us, with voice as the mechanism that controls technology.”

That vision fueled ElevenLabs’s $500 million raise this week at an $11 billion valuation, and it is increasingly shared across the AI industry. OpenAI and Google have both made voice a central focus of their next-generation models, while Apple appears to be quietly building voice-adjacent, always-on technologies through acquisitions like Q.ai. As AI spreads into wearables, cars, and other new hardware, control is becoming less about tapping screens and more about speaking, making voice a key battleground for the next phase of AI development. 

Iconiq Capital general partner Seth Pierrepont echoed that view onstage at Web Summit, arguing that while screens will continue to matter for gaming and entertainment, traditional input methods like keyboards are starting to feel “outdated.”

And as AI systems become more agentic, Pierrepont said, the interaction itself will also change, with models gaining guardrails, integrations, and context needed to respond with less explicit prompting from users. 

Staniszewski pointed to that agentic shift as one of the biggest changes underway. Rather than spelling out every instruction, he said future voice systems will increasingly rely on persistent memory and context built up over time, making interactions feel more natural and requiring less effort from users. 

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That evolution, he added, will influence how voice models are deployed. While high-quality audio models have largely lived in the cloud, Staniszewski said ElevenLabs is working toward a hybrid approach that blends cloud and on-device processing — a move aimed at supporting new hardware, including headphones and other wearables, where voice becomes a constant companion rather than a feature you decide when to engage with. 

ElevenLabs is already partnering with Meta to bring its voice technology to products, including Instagram and Horizon Worlds, the company’s virtual-reality platform. Staniszewski said he would also be open to working with Meta on its Ray-Ban smart glasses as voice-driven interfaces expand into new form factors. 

But as voice becomes more persistent and embedded in everyday hardware, it opens the door to serious concerns around privacy, surveillance, and how much personal data voice-based systems will store as they move closer to users’ daily lives — something companies like Google have already been accused of abusing.

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Substack confirms data breach affects users’ email addresses and phone numbers

Newsletter platform Substack has confirmed a data breach in an email to users. The company said that in October, an “unauthorized third party” accessed user data, including email addresses, phone numbers, and other unspecified “internal metadata.”

Substack specified that more sensitive data, such as credit card numbers, passwords, and other financial information, was unaffected.

In an email sent to users, Substack chief executive Chris Best said that the company identified the issue in February that allowed someone to access its systems. Best said that Substack has fixed the problem and started an investigation.

“I’m reaching out to let you know about a security incident that resulted in the email address and phone number from your Substack account being shared without your permission,” said Best in the email to users. “I’m incredibly sorry this happened. We take our responsibility to protect your data and your privacy seriously, and we came up short here.”

It’s not clear what exactly the issue was with its systems, and the scope of the data that was accessed. It’s also not yet known why the company took five months to detect the breach, or if it was contacted by hackers demanding a ransom. TechCrunch asked the company for more details, and we will update our story if we hear back.

Substack did not say how many users are affected. The company said that it doesn’t have any evidence that users’ data is being misused, but did not say what technical means, such as logs, it has to detect evidence of abuse. However, the company asked users to take caution with emails and texts without any particular indicators or direction.

On its website, Substack says that its site has more than 50 million active subscriptions, including 5 million paid subscriptions — a milestone it reached last March. In July 2025, the company raised $100 million in Series C funding led by BOND and The Chernin Group (TCG), with participation from a16z, Klutch Sports Group CEO Rich Paul, and Skims co-founder Jens Grede.

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Fundamental raises $255M Series A with a new take on big data analysis

An AI lab called Fundamental emerged from stealth on Thursday, offering a new foundation model to solve an old problem: how to draw insights from the huge quantities of structured data produced by enterprises. By combining the old systems of predictive AI with more contemporary tools, the company believes it can reshape how large enterprises analyze their data.

“While LLMs have been great at working with unstructured data, like text, audio, video, and code, they don’t work well with structured data like tables,” CEO Jeremy Fraenkel told TechCrunch. “With our model Nexus, we have built the best foundation model to handle that type of data.”

The idea has already drawn significant interest from investors. The company is emerging from stealth with $255 million in funding at a $1.2 billion valuation. The bulk of it comes from the recent $225 million Series A round led by Oak HC/FT, Valor Equity Partners, Battery Ventures, and Salesforce Ventures; Hetz Ventures also participated in the Series A, with angel funding from Perplexity CEO Aravind Srinivas, Brex co-founder Henrique Dubugras, and Datadog CEO Olivier Pomel.

Called a large tabular model (LTM) rather than a large language model (LLM), Fundamental’s Nexus breaks from contemporary AI practices in a number of significant ways. The model is deterministic — that is, it will give the same answer every time it is asked a given question — and doesn’t rely on the transformer architecture that defines models from most contemporary AI labs. Fundamental calls it a foundation model because it goes through the normal steps of pre-training and fine-tuning, but the result is something profoundly different from what a client would get when partnering with OpenAI or Anthropic.

Those differences are important because Fundamental is chasing a use case where contemporary AI models often falter. Because Transformer-based AI models can only process data that’s within their context window, they often have trouble reasoning over extremely large datasets — analyzing a spreadsheet with billions of rows, for instance. But that kind of enormous structured dataset is common within large enterprises, creating a significant opportunity for models that can handle the scale.

As Fraenkel sees it, that’s a huge opportunity for Fundamental. Using Nexus, the company can bring contemporary techniques to big data analysis, offering something more powerful and flexible than the algorithms that are currently in use.

“You can now have one model across all of your use cases, so you can now expand massively the number of use cases that you tackle,” he told TechCrunch. “And on each one of those use cases, you get better performance than what you would otherwise be able to do with an army of data scientists.”

That promise has already brought in a number of high-profile contracts, including seven-figure contracts with Fortune 100 clients. The company has also entered into a strategic partnership with AWS that will allow AWS users to deploy Nexus directly from existing instances.

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