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🛠️ Everything. All at Once. The Infrastructure Stack Is Converging

The clearest signal from AIS5 wasn't any single technology shift — it was how tightly coupled those shifts have become. Power constraints are reshaping hardware choices. Hardware density is forcing cooling and networking changes. And all of it is being stitched together by software. The data center isn't a collection of independent layers anymore. It's one system.

Power Sets the Constraints

AI demand is growing faster than new power infrastructure can be delivered. That's pushing operators toward stranded power — existing capacity that often sits underutilized — rather than waiting years for new generation and transmission projects.

But using that power effectively isn't just an electrical problem. Software orchestration now manages power envelopes dynamically, increasing the compute supported by existing infrastructure. And as the industry pushes toward gigawatt-scale AI campuses, power planning requires coordination across utilities, developers, operators, and governments.

The conversation has moved past adding capacity. It's about redesigning how capacity is delivered — and that redesign ripples into every other layer.

Networking, Cooling, and the Density Problem

Higher-density deployments driven by new silicon require liquid cooling to manage thermal loads. Co-packaged optics reduce networking overhead at the rack level. These aren't independent upgrades — they're responses to the same pressure. When you pack more compute into less space, everything around it has to adapt.

The result is that facility design, cooling architecture, and network topology are now downstream of hardware and power decisions in a way they weren't five years ago.

What This Means

No single layer can be planned in isolation anymore. Power shapes hardware selection. Hardware density drives cooling and networking. Software ties it all together. The organizations building AI infrastructure at scale aren't optimizing one layer at a time — they're designing the stack as a single, interdependent system.

The infrastructure underneath has to keep up.
That's what AI INFRA SUMMIT exists to support.

See you at AIS 6 December 4th, San Francisco.
Secure your spot with Super Early Bird Tickets below

Bay Area Startups Collectively Secured $79B+ in May

After closing the month with a $36B funding week, June got off to a quiet start with just under $2B in funding, and five megadeals, including Generalist AI, Tripo AI and ZutaCore. Despite that, 2026 startup investment continues to set new monthly records.

Following Alphabet(Google)'s $85B stock sale to fund their AI plans, Meta is now considering a stock sale to raise tens of billions to fund their AI capital expenditures, per the Financial Times. Meanwhile, to ensure adequate compute supply, Google will pay SpaceX $920M monthly for accesss to Nvidia GPUs.  And Apollo and Blackstone copmpleted a $35B package for Anthropic to lease TPUs.

For startups raising capital: Stay on top of who's raising, who's closing and who's investing with the Pulse of the Valley weekday newsletter. Founders get the newsletter, database and alerts for just $7/month ($50 value). Check it out and sign up here.

Follow LinkSV on LinkedIn to stay on top of SV funding intelligence, and the companies, investors and executives impacting the startup ecosystem.

Early Stage:

• Collate Software closed a $95M Series A, uses AI to create and streamline paperwork for diagnostic, medical device, and drug development companies.

• Town AI closed a $55M Series A, building tools that make it easy for anyone to create and use software in their everyday work.

• Terra AI closed a $20M Series A, solves the world's most critical subsurface problems with cutting-edge artificial intelligence.

• Trajectory closed a $15M Seed, the platform for continual learning, turning real product usage into AI that continuously improves.

• Drafted closed a $1M Seed, designs your dream home instantly with AI.

Growth Stage:

• Supabase closed a $500M Series F, the open source Postgres development platform and leader in agentic infrastructure.

• NewLimit closed a $435M Series C, developing medicines to treat age-related diseases by reprogramming the epigenome, a new therapeutic mechanism to restore regenerative potential in aged and diseased cells.

• Generalist AI closed a $400M Series B, an AI robotics company with the mission to make general-purpose robots a reality.

• ZutaCore closed a $100M Series C, waterless, direct-to-chip liquid cooling, removes it at the source by turning liquid into vapor, keeping processors consistently cool.

• Advanced Nanotherapies closed a $31M Series B, is developing a differentiated functionalized nanoparticle platform for local, dual-drug delivery during catheter-based interventions.
  

AI infrastructure is becoming harder to model as compute demand, power constraints, and deployment economics shift simultaneously.

Training clusters are scaling. Inference workloads are multiplying. Hyperscalers continue investing heavily in AI infrastructure while power availability, GPU supply chains, and data center construction timelines become increasingly important variables.

For operators, investors, and infrastructure leaders, understanding where those pressures are building has become a competitive advantage. Small shifts in one part of the stack can create significant downstream impacts across deployment strategies, capital allocation, and long-term planning.

SemiAnalysis is an independent research and analysis firm focused on semiconductors, AI infrastructure, hyperscaler economics, GPU markets, data centers, and the systems powering modern AI.

What they deliver

They produce deep technical and economic research across the AI infrastructure stack. Their work covers GPU supply chains, hyperscaler capital expenditure, inference economics, networking, power infrastructure, cloud pricing models, and AI data center forecasting.

Their research is widely referenced by infrastructure operators, investors, semiconductor companies, cloud providers, and organizations seeking a deeper understanding of where AI infrastructure is heading.

Who they serve

Hyperscalers, infrastructure operators, semiconductor companies, investors, cloud providers, and teams making long-term decisions around AI deployment and infrastructure strategy.

As AI adoption accelerates, understanding the economics and physical realities underneath the stack is becoming increasingly important. Questions around power availability, infrastructure utilization, deployment costs, and capacity planning are now shaping strategic decisions across the industry

Explore SemiAnalysis to follow deeper research on the systems, markets, and economics shaping the future of AI infrastructure.

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Logan Lemery
Head of Content // Team Ignite

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