As AI data centers hit power limits, Peak XV backs Indian startup C2i to fix the bottleneck

A tiny power problem for a GPU can look like a catastrophe for an AI business. One Bengaluru startup hopes to change that calculus.

A row of humming servers in a rented data-hall, a facilities manager on the phone with the utility, and an engineer staring at a redlined power budget is the new normal for AI infrastructure teams. The scene repeats at hyperscalers and smaller colo rooms alike as GPUs demand more concentrated electricity and cooling than older servers ever did.

Most coverage treats this as a capacity story about grids and generators, and that is true. The overlooked business consequence is subtler: marginal improvements in power conversion translate directly into months of runway for startups and meaningful margin rescue for midmarket cloud customers. That is the lens through which C2i Semiconductors and its backers must be judged.

Why power is now the bottleneck nobody liked to talk about

AI growth has shifted the bottleneck from raw compute to electricity and the logistics of getting clean, stable power into chips. BloombergNEF shows the sector’s energy appetite ballooning and colliding with grid realities, especially where new megawatt-class sites are being sited. (about.bnef.com)

Goldman Sachs projects power demand from data centers could rise roughly 175 percent by 2030 versus 2023, an increase that changes build economics and forces architects to rethink how conversion and delivery work. (goldmansachs.com)

The obvious interpretation—and the part people miss

The obvious story is that bigger substations, more renewables, and behind the meter generation will solve the problem. That is helpful but incomplete. The underreported fact is that while generation scales slowly, improvements in end-to-end power conversion inside servers can be deployed faster and with much less permitting pain, unlocking utilization and cooling gains that cascade through clients’ PnL.

Who is trying to redesign power delivery for AI hardware

Incumbent power-electronics firms and module makers have long supplied parts of the chain, but few have attempted a full grid-to-core system design that unifies silicon, packaging, and controls. The space now sees startups and semiconductor design teams targeting system-level solutions that reduce conversion losses and improve GPU stability in dense racks. This is fertile ground for outsiders because the qualification cycle for server suppliers is long but predictable, and hyperscalers are actively soliciting new options.

The C2i bet and why Peak XV wrote a check

C2i, founded in 2024 by former Texas Instruments power executives, raised a $15 million round led by Peak XV to pursue a plug-and-play grid-to-GPU power platform that bundles conversion, control and packaging. TechCrunch reported the funding and quoted C2i’s claim that integrated delivery could cut end-to-end losses by roughly 10 percent, which the startup says translates to significant megawatt-level savings for operators. (techcrunch.com)

Local reporting adds that C2i plans aggressive tapeouts in April to June 2026 and will split fabrication between Tower Semiconductor and GlobalFoundries as it readies the first silicon. Those concrete timelines help explain why investors moved quickly. (m.economictimes.com)

How real are the efficiency claims in production terms

C2i and others estimate about 8 to 10 percent recovered energy through integrated conversion; an industry shorthand is about 100 kilowatts saved per megawatt consumed. That sounds small unless an operator is leasing dozens of megawatt-class pods, in which case it becomes tens of thousands of dollars and a meaningful cooling delta. BloombergNEF’s forecasts about data center expansion make those per-megawatt improvements multiply into systemic effects on grid demand. (about.bnef.com)

“Fixing how electricity gets to the GPU is an operating system change for data centers, not a sticker on the box.”

Why now for investors and for Indian chip design

Hyperscalers are announcing larger facilities, and policy plus supply-chain improvements have made high-complexity tapeouts cheaper and faster than in past eras. Peak XV’s move signals belief that a credible system-level power play can graduate from lab to rack within quarters, not years. Goldman Sachs’ work on the scale of power demand puts a time pressure on vendors to deliver practical gains now. (goldmansachs.com)

Practical implications for businesses with 5 to 50 employees

For a small AI shop that colocates a single rack of GPU-dense servers, the math is immediate. C2i’s public materials and industry reporting suggest a 1 kilowatt saving per server tray on a 10-tray rack, producing a 10 kilowatt reduction overall. At an electricity price of $0.10 to $0.15 per kilowatt hour, that saves roughly $720 to $1,080 per month in energy costs for that single rack, plus lower cooling overhead and potentially higher GPU uptime that reduces cloud or retraining bills. Those dollars buy additional development time or one to two months of cloud credits for a typical seed-stage model training cycle. (m.economictimes.com)

If a 20-person startup moves from pure cloud inference to a small colo deployment, the same 10 percent improvement compounds against scale and predictable utilization, and the payback period for hardware that includes smarter power delivery can fall into the range investors actually like to hear—measured in months, not years.

The cost nobody is calculating

People price the chip but not the delivery. Server vendors calculate parts cost and test cycles, but many procurement teams neglect the long tail of losses across racks, trailers, and facility-level inefficiencies. Multiply a 1 percent delivery improvement across tens of thousands of GPUs and the “small” difference becomes a multi-million dollar item on the income statement. This is the sort of spreadsheet-based excitement that keeps CFOs awake, and also the reason Peak XV is betting on system gains rather than incremental modules.

Risks and open questions that matter

Execution risk is high. Redesigning power delivery means synchronizing silicon, packaging, thermal management and firmware with customers who have long supplier lists. Qualification cycles with hyperscalers can stretch, and adoption depends on measured performance in operational environments, not lab slides. Policy and regional grid developments can shift where hyperscalers build, which changes latency and power economics and could deflate part of the value proposition.

Another open question is competition from large incumbents that can bundle power innovations into existing server designs. A big supplier with factory scale could undercut a startup on price if the startup’s advantage is not uniquely protected.

What founders and operators should watch next

Monitor C2i’s tapeout results and any public validation from a major data center operator in the months after June 2026. Watch hyperscalers’ procurement signals for any requests for integrated grid-to-core solutions. If early independent benchmarks match the claimed efficiencies, expect a wave of design wins and a tightening of supplier ecosystems.

Forward look with practical insight

If C2i and peers deliver validated gains, the immediate industry impact will be reduced marginal energy demand for new sites and a new category of server specification that treats power delivery as a first class design constraint rather than an afterthought.

Key Takeaways

• C2i raised $15 million to pursue grid-to-GPU power delivery that could cut conversion losses and lower total cost of ownership.
• Data center power demand is projected to surge, creating urgency for system-level efficiency innovations.
• Small AI teams can see real monthly savings with modest on-prem deployments, shortening payback time for smarter hardware.
• Execution risk and incumbent response remain the main obstacles to rapid, wide adoption.

Frequently Asked Questions

How much could a small company save by using improved power delivery hardware?
Using the startup’s cited figures, a 10-tray rack saving 1 kilowatt per tray yields about 10 kilowatts saved. At $0.10 to $0.15 per kilowatt hour, that is roughly $720 to $1,080 monthly in energy savings plus reduced cooling and higher GPU uptime.

Will these power innovations reduce cloud GPU costs for startups?
Indirectly yes. Better on-prem efficiency reduces the need to spin expensive cloud training and inference cycles. For hybrid setups, improved power can lower peak demand charges and make colo or private cloud options more competitive with public cloud list prices.

Is this a long term monopoly for C2i or can incumbents catch up quickly?
The market favors those who ship validated silicon and system integration first, but incumbents with scale can replicate some improvements. The decisive factor will be customer-validated performance and the ability to move through server qualification quickly.

Should smaller companies wait to buy new servers that include these power advances?
That depends on workload. If local inference or consistent model training is material to the business and energy costs are a line item, buying or negotiating for improved power delivery makes sense now. If workloads are sporadic, cloud remains attractive until benchmarks exist.

How will this affect the broader grid and energy policy debates?
If system-level gains roll out, they reduce near-term grid strain by lowering demand growth per compute unit. That buys time for larger generation and transmission projects, but it does not remove the need for new capacity and smart policy.

Related Coverage

Readers may want to explore stories on the rising role of behind-the-meter generation for data centers, the economics of hyperscaler campus builds, and how semiconductor design in India is shifting from captive work to global product plays. The section on hardware procurement strategy would also be useful for procurement teams and CTOs planning 2026 to 2028 budgets.