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⚠️Why Most AI Pilots Stall Before Production

A large percentage of AI pilots still never make it into production.

Usually, the model works. The demo lands well internally. A team proves that a workflow can be automated or accelerated, leadership gets interested, and momentum starts building around the idea that AI can create a meaningful operational advantage.

Then the rollout slows down.

Ownership becomes unclear. Nobody defined what success actually meant beyond proving the model could function. Budgeting never moved beyond experimentation. What started as an engineering initiative never connected cleanly into procurement, operations, legal, security, or the teams expected to support it long term.

That pattern is becoming common across enterprise AI deployments.

The organizations moving faster tend to approach AI differently from the start. Projects are treated more like operational systems than isolated technical experiments. There is usually a named executive sponsor, clear accountability around deployment, and some level of centralized oversight to keep adoption from fragmenting across teams.

Many are building dedicated groups around deployment and governance, whether formal AI Centers of Excellence or smaller cross-functional teams responsible for standards, rollout strategy, and measurement. The goal isn’t centralization for its own sake. It’s reducing duplication, preventing disconnected implementations, and creating a repeatable process for moving projects into production.

The infrastructure side creates another layer of difficulty.

Most enterprise environments still operate across disconnected systems, inconsistent datasets, and workflows that were never designed around AI-driven access patterns. That creates friction long before compute becomes the limiting factor. Models depend on stable retrieval, clean data flow, and systems capable of supporting continuous interaction between applications and inference layers. Without that foundation, adding more GPUs rarely fixes the underlying issue.

This is where a lot of enterprise deployments start breaking apart.

The technical side of the model may work perfectly, but the surrounding organization and infrastructure can’t support it cleanly once usage expands beyond a small pilot group. Integration becomes difficult. Measurement becomes inconsistent. Different teams begin deploying overlapping systems without shared standards or visibility into how they interact.

What enterprises increasingly need is operational structure around deployment itself. Governance, integration, orchestration, measurement, and organizational alignment are becoming just as important as model performance.

That changes where the opportunity sits for builders and vendors.

A growing number of companies are no longer searching for another standalone AI tool. They are trying to figure out how to move existing systems into production without creating more operational complexity than they remove.

The technology continues moving quickly. Most organizations don’t.

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

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Bay Area Startups Collectively Secured $36B+ in May Week 4

The last week of the month closed with more than $36B in fundings, with most of that coming from the last $35B of Anthropic's $65B Series H at a post-money valuation of $965B. That number puts them ahead of rival OpenAI's most recent $852B valuation. In the valuation space, Corgi Insurance caught attention this week when they announced a $106M Series B1 round with $2.6B valuation – just three weeks after their Series B at a $1.3B valuation.

2026 YTD SV funding is now over $300B, and money continues to be invested at unprecedented levels.

LinkSV-WITI webinar coming up: for a deeper dive on SV fundraising, valuation trends and impacts on the startup and venture ecosystem, join us next Friday, June 5, at noon for the monthly LinkSV-WITI webinar, Tech, Talent & Investment Trends. Register and grab the link here.

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:

• Reactor closed a $59M Series A, the developer platform for real-time generative video.

• Countable Labs closed a $26M Series A, transforming how biology is measured.

• Tensormesh closed a $20M Seed, the leader in caching-accelerated inference optimization for enterprise AI.

• Itera closed a $12M Seed, enabling engineers to test real hardware changes in seconds instead of weeks through breakthrough fluid circuit technology

• Canyon Code closed a $5M Pre-Seed, gives enterprises the controls to optimize, manage and govern their multi-agentic apps at scale

Growth Stage:

• Anthropic closed a $35B Series H, an AI safety and research company developing AI systems that are helpful, honest and harmless.

• Cognition AI closed a $1B Series D, an applied AI lab building end-to-end software agents.

• Corgi Insurance closed a $106M Series B, an AI-native, full-stack insurance carrier built for startups.

• Triomics closed a $22M Series B, a generative AI platform purpose-built for oncology workflows.

• Signos closed a $20M Series B, on a mission to eliminate obesity by igniting the metabolic health movement

Most AI content online is still focused on the surface layer. Product demos, chatbot conversations, and recycled commentary about adoption curves.

Meanwhile, the people building the infrastructure underneath AI are dealing with a completely different set of problems.

Power constraints. Rack density. GPU orchestration. Cooling limits. Inference economics. Data movement. Energy timelines. Multi-tenant infrastructure. The operational reality of deploying AI systems at scale.

That is the value of our Ignite YouTube library.

The channel now contains more than 293 videos, including 40+ videos from AI INFRA SUMMIT 5 and a growing archive of shorts, operator clips, fireside conversations, infrastructure discussions, and full-length talks from across the AI infrastructure ecosystem.

The library features founders, hyperscalers, GPU cloud operators, infrastructure providers, investors, and enterprise builders discussing the bottlenecks, tradeoffs, and deployment realities shaping the next era of AI.

This is less about “AI trends” and more about understanding the physical and operational systems now driving the market forward.

Explore the IgniteGTM YouTube library to hear directly from the teams building AI infrastructure in real time.

Your Feedback Matters!

Your feedback is crucial in helping us refine our content and maintain the newsletter's value for you and your fellow readers. We welcome your suggestions on how we can improve our offering. [email protected] 

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.

It's what billionaires like Bezos and the Rockefellers have privately used to diversify for decades.

Why?

1. Appreciation. The ArtPrice100 Index outpaced the S&P 500 overall from 2000 to 2025

2. Low-correlation. The postwar contemporary segment has moved independently of traditional investments like stocks since ‘95.*

3. Resilience. A scarce, physical, and global asset class with decades of demonstrated demand.

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.

Shares in new offerings can sell quickly but...

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^{*According to Masterworks data. Investing involves risk. Past performance is not indicative of future returns. See important Reg A disclosures at masterworks.com/cd.}