Power, rather than compute, is fast becoming the limiting factor in scaling AI data centers. That shift has prompted Peak XV Partners to back C2i Semiconductors, an Indian startup building plug-and-play, system-level power solutions designed to cut energy losses and improve the economics of large-scale AI infrastructure.

C2i (which stands for control conversion and intelligence) has raised $15 million in a Series A round led by Peak XV Partners, with participation from Yali Deeptech and TDK Ventures, bringing the two-year-old startup’s total funding to $19 million.

The investment comes as data-center energy demand accelerates worldwide. Electricity consumption from data centers is projected to nearly triple by 2035, per a December 2025 report from BloombergNEF, while Goldman Sachs Research estimates data-center power demand could surge 175% by 2030 from 2023 levels — the equivalent of adding another top-10 power-consuming country.

Much of that strain comes not from generating electricity but from converting it efficiently inside data centers, where high-voltage power must be stepped down thousands of times before it reaches GPUs. This process currently wastes about 15% to 20% of energy, C2i’s co-founder and CTO Preetam Tadeparthy said in an interview.

“What used to be 400 volts has already moved to 800 volts, and will likely go higher,” Tadeparthy told TechCrunch.

Founded in 2024 by former Texas Instruments power executives Ram Anant, Vikram Gakhar, Preetam Tadeparthy, and Dattatreya Suryanarayana, along with Harsha S. B and Muthusubramanian N. V, C2i is redesigning power delivery as a single, plug-and-play “grid-to-GPU” system spanning the data-center bus to the processor itself.

By treating power conversion, control and packaging as an integrated platform, C2i estimates it can cut end-to-end losses by around 10% — roughly 100 kilowatts saved for every megawatt consumed — with knock-on effects for cooling costs, GPU utilisation and overall data-center economics.

“All that translates directly to total cost of ownership, revenue, and profitability,” Tadeparthy said.

For Peak XV Partners (which split from Sequoia Capital in 2023), the attraction lies in how power costs shape the economics of AI infrastructure at scale. Rajan Anandan, the venture firm’s managing director, told TechCrunch that after the upfront capital investment in servers and facilities, energy costs become the dominant ongoing expense for data centers, making even incremental efficiency gains highly valuable.

“If you can reduce energy costs by, call it, 10 to 30%, that’s like a huge number,” Anandan said. “You’re talking about tens of billions of dollars.”

The claims will be tested quickly. C2i expects its first two silicon designs to return from fabrication between April and June, after which the startup plans to validate performance with data-center operators and hyperscalers that have asked to review the data, according to Tadeparthy.

The Bengaluru-based startup has built a team of about 65 engineers and is setting up customer-facing operations in the U.S. and Taiwan as it prepares for early deployments.

Power delivery is one of the most entrenched parts of the data-center stack, long dominated by large incumbents with deep balance sheets and years-long qualification cycles. While many newer companies focus on improving individual components, redesigning power delivery end-to-end requires coordinating silicon, packaging, and system architecture simultaneously — a capital-intensive approach that few startups attempt and one that can take years to prove in production environments.

Anandan said the real question now is execution, noting that all startups face technology, market, and team risks when betting on how industries evolve. In C2i’s case, he said, the feedback loop should be relatively short. “We’ll know in the next six months,” said Anandan, pointing to upcoming silicon and early customer validation as the moment when the thesis will be tested.

The bet also reflects how India’s semiconductor design ecosystem has matured in recent years.

“The way you should look at semiconductors in India is, this is like 2008 e-commerce,” said Anandan. “It’s just getting started.”

He pointed to the depth of engineering talent — with a growing share of global chip designers based in the country — alongside government-backed design-linked incentives that have lowered the cost and risk of tape-outs, making it increasingly viable for startups to build globally competitive semiconductor products from India rather than operate only as captive design centers.

Whether those conditions translate into a globally competitive product will become clearer over the coming months, as C2i begins validating its system-level power solutions with customers.

Something strange happened at University of California campuses this fall. For the first time since the dot-com crash, computer science enrollment dropped. System-wide, it fell 6% this year after declining 3% in 2024, according to reporting this past week by the San Francisco Chronicle. Even as overall college enrollment climbed 2% nationally — according to January data from the National Student Clearinghouse Research Center — students are bailing on traditional CS degrees.

The one exception is UC San Diego — the only UC campus that added a dedicated AI major this fall.

This all might look like a temporary blip tied to news about fewer CS grads finding work out of college. But it’s more likely an indicator of the future, one that China is much more enthusiastically embracing. As MIT Technology Review reported last July, Chinese universities have leaned hard into AI literacy, treating AI not as a threat but instead as essential infrastructure. Nearly 60% of Chinese students and faculty now use AI tools multiple times daily, and schools like Zhejiang University have made AI coursework mandatory, while top institutions like Tsinghua have created entirely new interdisciplinary AI colleges. In China, fluency with AI isn’t optional anymore; it’s table stakes.

U.S. universities are scrambling to catch up. Over the last two years, dozens have launched AI-specific programs. MIT’s “AI and decision-making” major is now the second-largest major on campus, says the school. As reported by the New York Times in December, the University of South Florida enrolled more than 3,000 students in a new AI and cybersecurity college during its fall semester. The University at Buffalo last summer launched a new “AI and Society” department that offers seven new, specialized undergraduate degree programs, and it received more than 200 applicants before it swung open its doors.

The transition hasn’t been smooth everywhere. When I spoke with UNC Chapel Hill Chancellor Lee Roberts in October, he described a spectrum — some faculty “leaning forward” with AI, others with “their heads in the sand.” Roberts, a former finance executive who arrived from outside academia, was pushing hard for AI integration despite faculty resistance. A week earlier, UNC had announced it would merge two schools to create an AI-focused entity — a decision that drew faculty pushback. Roberts had also appointed a vice provost specifically for AI. “No one’s going to say to students after they graduate, ‘Do the best job you can, but if you use AI, you’ll be in trouble,’” Roberts told me. “Yet we have faculty members effectively saying that right now.”

Parents are playing a role in this rocky transition, too. David Reynaldo, who runs the admissions consultancy College Zoom, told the Chronicle that parents who once pushed kids toward CS are now reflexively steering them toward other majors that seem more resistant to AI automation, including mechanical and electrical engineering.

But the enrollment numbers suggest students are voting with their feet. According to a survey in October by the nonprofit Computing Research Association — it members include computer science and computer engineering departments from a wide range of universities — 62% of respondents reported that their computing programs saw undergraduate enrollment declines this fall. But with AI programs ballooning, it’s looking less like a tech exodus and more like a migration. The University of Southern California is launching an AI degree this coming fall; so are Columbia University, Pace University, and New Mexico State University, among many others. Students aren’t abandoning tech; they’re choosing programs focused on AI instead.

It’s too soon to say whether this recalibration is permanent or a temporary panic. But it’s certainly a wake-up call for administrators who’ve spent years wrestling with how to handle AI in the classroom. The debate over whether to ban ChatGPT is ancient history at this point. The question now is whether American universities can move fast enough or whether they’ll keep arguing about what to do while students transfer to schools that already have answers.

The Department of Homeland Security has been increasing pressure on tech companies to identify the owners of social media accounts that criticize Immigration and Customs Enforcement (ICE), according to The New York Times.

This echoes other recent reporting, with Bloomberg pointing to five cases in which Homeland Security sought to identify the owners of anonymous Instagram accounts, with the department withdrawing its subpoenas after the owners sued. And a Washington Post story described Homeland Security’s growing use of administrative subpoenas — which do not require the approval of a judge — to target Americans.

Now the NYT says a practice that was previously used sparingly has become increasingly common in recent months, with the department sending hundreds of these subpoenas to Google, Reddit, Discord, and Meta. The subpoenas reportedly focused on accounts that did not have a real name attached and either criticized ICE or described the location of ICE agents.

Google, Meta, and Reddit have reportedly complied in at least some cases. Echoing past comments, Google said that it informs users of these subpoenas when it can, and that it pushes back when the subpoenas are “overbroad.”