Tech
Pacific Fusion finds a cheaper way to make its fusion reactor work
Fusion power’s biggest question remains unanswered: How do you ensure the cost to start the fusion reaction isn’t higher than the price at which you can sell the power?
Plenty of people have ideas, but no one has cracked it yet. Commonwealth Fusion Systems, for example, is confident enough that it’s building a massive reactor that costs several hundred million dollars. But the device won’t be turned on until next year, leaving the question unanswered for now.
Other companies that were founded more recently think they have a shot at building a fusion power plant for less, including Pacific Fusion. Today the company announced the results of a series of experiments it performed at Sandia National Laboratories that it says will eliminate some costly parts of its approach. The company exclusively shared the results with TechCrunch.
Fusion power promises to generate large amounts of electricity 24/7 and deliver it in a way that’s familiar to today’s grid operators. Most fusion startups are targeting the early to mid-2030s to switch on their first commercial fusion power plant.
Pacific Fusion is chasing an approach known as pulser-driven inertial confinement fusion (ICF). At its core, it’s similar to the experiments carried out at the National Ignition Facility (NIF). The company compresses small fuel pellets in rapid succession, and that compression causes atoms inside the fuel to fuse and release energy.
But where NIF uses lasers to kick off the compression, Pacific Fusion wants to use massive pulses of electricity. Those pulses will create a magnetic field that encircles the fuel pellet — about the size of a pencil eraser — causing it to compress in less than 100 billionths of a second.
“The faster you can implode it, the hotter it’ll get,” Keith LeChien, co-founder and CTO of Pacific Fusion, told TechCrunch.
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One of the challenges with pulser-driven ICF is that the process has typically needed a bit of a kick-start to work properly. To create conditions in the fuel pellet hot enough for fusion, researchers have been using both lasers and magnets to warm it up beforehand. “It’s just a little bit of energy just to give it a little bit of a boost before you compress it,” LeChien said, on the order of 5% to 10% of the total energy.
But the added lasers and magnets add upfront complexity, cost, and maintenance requirements to the machine, making it that much harder to sell power at competitive prices.
So in the experiments at Sandia, Pacific Fusion tweaked the design of the cylinder encasing the fuel pellet and adjusted the electrical current delivered to it. Before the big pulse of electricity that ignites the fusion reaction, the company allowed a bit of the magnetic field to leak through to the fuel before compressing it, warming it in the process.
“We can make very subtle changes to how this cylinder is manufactured that allow the magnetic field to leak or to seep into the fuel before it’s compressed,” LeChien said.
Pacific Fusion’s fuel is loaded in a plastic target that’s wrapped in aluminum. By varying the thickness of the aluminum, the company can adjust how much of the magnetic field makes its way to the fuel. The casing needs to be manufactured with some precision, but nothing crazy, LeChien said — something on the order of what’s required for a .22 caliber bullet casing. “That’s a process that’s been honed and manufactured and perfected over 100-plus years,” he added.
The tweaks don’t significantly change how much energy Pacific Fusion needs to deliver to the target. “It doesn’t take much energy to actually allow that magnetic field into the center of the fuel,” he said. “It’s a tiny fraction, much less than 1%. It’s a very, very, very small fraction of the overall energy in the system, so it’s effectively unnoticeable.”
Eliminating the magnetic system would simplify the system and its maintenance requirements, which would have a modest effect on overall cost, he said. But getting rid of the laser would cut costs significantly. “The scale of laser [needed] to preheat these types of systems at high gain is north of $100 million.”
LeChien said experiments like this also help refine the company’s simulations to ensure they’re in agreement with what happens in the real world. “A lot of people have simulated things and said, ‘Oh, this will work or that will work,’” he said. “It’s a very different game to simulate something, build it, test it, and have it work. Closing that loop is hard.”
Tech
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.
Tech
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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Tech
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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