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💡 When the Lights Flicker: AI's Real Bottleneck

The pressure underneath AI infrastructure has become difficult to ignore. Compute keeps scaling, model demand keeps rising, and the physical systems underneath it are being pushed into conditions they were never designed to handle.

That tension showed up repeatedly at AI INFRA SUMMIT 5, particularly around one issue: power.

Compute Is No Longer The Constraint

The industry spent years optimizing around compute availability. Now the limiting factor is whether enough electricity can be delivered to the right place, at the right density, on the right timeline.

You can see it at the rack level first. Traditional facilities were built around workloads that look modest compared to modern AI systems. Higher-density deployments are pushing cooling, electrical routing, and facility design beyond the assumptions most existing infrastructure was built around.

Retrofitting Stops Making Economic Sense

That creates a difficult position for operators with older footprints. Some facilities can support incremental upgrades, but large-scale AI deployments often require major redesigns. In many cases, retrofitting stops making economic sense once the gap between legacy infrastructure and AI demand becomes too large.

Power timelines increasingly move slower than AI timelines. In major markets, grid interconnection delays stretch across years while demand for compute moves almost immediately. Capacity gets claimed early, projects move forward before power is fully secured, and operators are pushed toward whatever infrastructure can come online fastest.

Stranded Power Becomes Strategic

That pressure is driving more attention toward stranded and underutilized power. One of the more interesting discussions at AIS5 focused on how unused grid capacity could support AI infrastructure far sooner than waiting for entirely new power buildouts. Instead of sitting in interconnection queues for years, operators look for existing capacity and build around it.

The challenge is that AI workloads run continuously while grid conditions shift constantly around them. Weather events, regional demand spikes, and infrastructure stress all affect how much power can realistically stay allocated at any given moment.

Operators are starting to think about infrastructure that can adapt dynamically to available power conditions, scaling workloads up or down depending on what the surrounding system can support in real time. Once energy availability becomes variable, orchestration starts carrying much more weight across the stack.

The advantage increasingly goes to the teams that can turn available power into usable compute efficiently under constraint, not simply the teams deploying the most hardware.

The buildout continues, but the assumptions underneath it are changing fast. AI infrastructure now expands inside a system where power access, cooling capacity, and grid responsiveness all shape what can realistically scale.

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

// UPCOMING

Bay Area Startups Collectively Secured $1.9B in May Week 2

The second week of the month closed with $1.9B in fundings, 74% of that in just four megadeals. The largest was a $650M Series A to Recursive Superintelligence, an AI research lab.

The Cerebras IPO beat expectations, pricing at $185 and opening the first day of trading at $350, valuing the chipmaker at $100B+ in the largest US tech IPO since Uber's in 2019. This may finally break the ice for a long line of late stage tech companies who have avoided going public since the end of 2022, preferring to close additional rounds of capital. Databricks is the poster child for this group, having closed a $5B Series L and tender offer for their employees in January.

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. Click through to get more detail on investors and executives, including email addresses for both. Founders get the newsletter, database and alerts for just $7/month ($50 value). Check it out and sign up here.

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Early Stage:

• Recursive Superintelligence closed a $650M Series A, an AI research lab focused on AI that recursively improves itself.

• Gridcare closed a $64M Series A, uses physics-based AI to identify and activate near-term capacity on today's grid.

• Judgment Labs closed a $32M Series A, the platform for improving agents from production data.

• Config closed a $27M Seed, building the data infrastructure that enables general-purpose bimanipulation.

• Wirestock closed a $23M Series A, supplies datasets of images, videos, design assets, gaming and 3D content to AI labs.

Growth Stage:

• Mind Robotics closed a $400M Series B, building the full-stack platform of foundation models, purpose-built robotics, and deployment infrastructure.

• Cowboy Space Corporation closed a $275M Series B, our satellites will collect sunlight, convert it to electricity, and transmit power through infrared lasers to ground stations.

• Exaforce closed a $125M Series B, our AI-native platform combines a real-time security knowledge graph with AI agents.

• Cala Health closed a $50M Series D, our wearable neuromodulation therapies deliver individualized peripheral nerve stimulation.

• Vapi closed a $50M Series B, lets enterprises deploy human-like voice agents in minutes.

Most companies are sitting on the same problem. Their data lives across disconnected SaaS tools, reporting systems, ad platforms, CRMs, spreadsheets, and operational dashboards. The result is slow decision-making, conflicting numbers, broken attribution, and AI initiatives that never make it past the prototype phase.

Teams are not looking for “more dashboards.” They want clearer visibility into the business, faster decisions, cleaner workflows, and AI systems that actually work in production. That is where Latticework Insights fits.

Who they are
Latticework Insights is an AI and data infrastructure firm focused on building custom AI stacks, analytics systems, and modern data platforms for consumer and retail brands.

What they deliver

They build custom AI agents, workflow automation systems, centralized data infrastructure, predictive analytics models, and operational reporting environments designed to unify fragmented business data into usable systems.

Who they serve
Retail, ecommerce, and DTC brands operating across fragmented SaaS and analytics environments.

Latticework Insights joined us at AI Infra Summit 5, bringing a perspective grounded in what happens after the infrastructure is deployed. As more companies race to operationalize AI, the bottleneck is increasingly shifting toward orchestration, data quality, workflow integration, and production-ready systems that teams can actually trust.

Explore Latticework Insights to see how brands are building AI and analytics systems that move beyond demos and into real operational use.

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Logan Lemery
Head of Content // Team Ignite

Where to Invest $100,000 Right Now, According to Experts

Investors face a dilemma. When the S&P 500 finished its worst quarter since 2022 last month, diversifiers like bonds and bitcoin fell too.

Even with the turnaround in mid-April, analysts at Goldman Sachs and Vanguard have projected low-single-digit annualized returns from 2024-2034.

Bloomberg asked where experts would personally invest $100,000 for their March monthly edition.

One answer that surfaced for a second time? Art.

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Thanks to the world's premier art investing platform, now anyone can invest in works featuring legends like Banksy, Basquiat, and Picasso, without needing millions.

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