AI has attracted unprecedented levels of capital and attention. And questions are growing about the so-called AI bubble: Are too many startups chasing the same ideas? Are valuations running ahead of real adoption? And will all this investment pay off — or pop?
GeekWire polled a handful of Seattle-area venture capitalists about whether they think an AI bubble exists, and how startups should prepare as they plan for 2026.
Taken together, the investors paint a picture of a market that is overheated in places, but far from broken. They see clear signs of excess in AI — especially in early-stage private companies where valuations often outpace real traction. But they largely reject the idea of a catastrophic bubble, and most argue that the technology itself is already delivering real value.
They differ on the details: Some see the biggest excess in data center buildouts. Others point to narrative-driven startups raising at huge valuations without real customer traction. One investor puts AI’s full impact 10 to 20 years out. Another sees immediate opportunity as companies rethink their software spending, making longtime vendors vulnerable.
Their advice to startup founders: ignore the hype, focus on real customer problems, build durable revenue and efficient businesses, and be ready for some market cooling.
Read their full responses below.
Sabrina Albert (Wu), partner at Madrona
“There’s clear froth in parts of the AI market, especially in early-stage private valuations where companies are priced well ahead of fundamentals, which fits a classic ‘bubble’ definition. In the public markets, the strongest AI companies are backing valuations with outsized earnings and growth, so it doesn’t look like a traditional bubble there.
The most pronounced exuberance is in the private markets, particularly at seed and Series A, where many investors are trying to get in earlier on AI exposure. As a result, capital is chasing startups with limited traction and valuations that price in outcomes that may take years of execution to justify.
Startups should focus on durable business fundamentals early on. Build repeatable revenue through annual or multi-year contracts, solve real customer problems, and differentiate by integrating deeply into the customer tech stack to create real product and company flywheels. Long-term success comes from delivering measurable value and defensible growth over time.”
Cameron Borumand, general partner at Fuse
“Many factors are at play here. You have a new and genuinely transformative technology in AI. Over the long term, it will radically reshape how nearly every industry operates. At the same time, history tells us that new technologies tend to be overestimated in the short term and underestimated in the long term. The most profound, fully realized impacts of AI may still be 10-to-20 years away.
In the near term (the next few years), I expect some pullback in the public markets as investors come to terms with the fact that true ‘enterprise readiness’ for AI will take time. This doesn’t suggest anything catastrophic — just that the roughly 21 percent year-over-year growth we’ve seen in the Nasdaq is unlikely to be sustainable and may revert closer to the 30-year average of around 10 percent. After a few meaningful pullbacks, pundits will inevitably claim that AI is overhyped. In reality, this would simply represent a normalization after an extraordinary, AI-fueled run in the public markets.
Late-stage private markets will see some overly hyped companies — this happens in every boom cycle. The winners will be bigger than ever, but the losses will also be bigger than ever. When you have companies like Anthropic growing from $1 billion to a projected $9 billion of revenue in 2025, it’s clear that AI is already delivering real, material impact in the world.
For startups, there’s no better time to be building than now. M&A markets are back, customers have budget, and talent wants to work on interesting projects. With that said, there is a lot of noise, so it’s best to go deep and really focus on a core customer problem. Most of the growth we’ve seen to date is in the infrastructure layer — the next few years will be about the next generation of AI-powered applications.”
Chris DeVore, founding managing partner at Founders’ Co-op
“Yes, a significant amount of capital being deployed globally in AI (and particularly in the data center buildout) is almost certainly being misallocated. Specifically in startups, outside a few presumed winners (OpenAI, Anthropic, Cursor), the concern is less overcapitalization and more the prices at which financings are being done relative to the actual cash flows and margin potential of the companies being financed.
That said, unlike some recent bubbles I can think of (crypto, metaverse, etc.) there are actual babies in the bathwater this time. LLMs are remarkably capable tools even at their current state of development, and will remain core to many software development and knowledge work tasks long after rationality has returned to the financial landscape.
The founder and investor challenge in moments like the current one is how to make decisions that will look smart ten years from now, not just in the current moment. Are there ways to apply LLMs to create durable business value in segments of the economy that are not likely to be overcapitalized or competed to zero by the near-term flood of dollars? The only alternative strategy is to try to pick winners in the capital wars and pay whatever the market demands for those assets, but history suggests that’s a very low odds proposition for even the best players.
The recipe for success in times like this is not that different from any other time: pick a customer segment that you understand better than anyone else, engage deeply with those customers to understand what problems you can uniquely solve with LLMs that were too hard or expensive to solve previously, build quickly and iteratively to show value to those customers, and maintain that pace of shipping and learning for as long as you can.
That may sound simple, but it’s remarkable how few founding teams are able to pull it off, and that why startups are so hard, and so fun.”
Sheila Gulati, managing director at Tola Capital
“Broadly, I don’t think we’re in an AI bubble right now. Similar concerns existed when we launched the Azure platform about fifteen years ago. Back then, people were initially worried about racing to a zero-margin business.
Today’s massive AI infrastructure buildouts will shape the operational software layers that drive real-world performance — compute orchestration, data pipelines, memory systems, and large-scale inference efficiency. Value is shifting toward packaging and deploying intelligence across enterprise workflows.
Enterprise software startups should position themselves in the growing TAM of delivering full, end-to-end solutions and new ways of doing things where humans collaborate with AI agents. Winning startups will encompass both the growing IT TAM and economics of a portion of the labor market as well.
We are now seeing unprecedented malleability of CIO budgets. The deeply entrenched application stack can now shift to new players which are built with AI from the ground up. The market opportunity is massive, and companies should set their sights on building the new megacaps, not minor feature companies.”
Andy Liu, co-founding partner at Unlock Venture Partners
“Yes, we are in an AI bubble, but not in the way most people think.
Capital and valuations are running well ahead of fundamentals, particularly for companies without clear customer pull, durable differentiation, or credible/reasonable paths to profitability. We’re seeing a growing gap between narrative-driven AI companies where ‘AI’ is largely a positioning exercise, and value-driven AI companies that use the technology to deliver measurable, repeatable value for customers.
The bubble seems most pronounced at the early and growth stages where AI storytelling can temporarily substitute for traction and raise capital at lofty valuations. Some strong companies will emerge from this cycle, but there will be meaningful drawdowns, recaps, or shutdowns as many startups fail to grow into those expectations.
Looking ahead to 2026, my advice to founders is straightforward:
- Build real businesses, not decks. Products today can be built quickly with real revenue before raising capital.
- Prioritize efficiency, customer ROI, and unit economics.
- Use AI to create real leverage, not excuses for burning capital.
2026 is going to be an incredible moment to build. The cost of experimentation and building products has collapsed, and founders no longer need educational credentials (CS degrees or an MBA) to create real products and revenue. The next generation of durable AI companies will be built by small teams who focus less on hype and more on efficient execution. We’re definitely excited to see more teams building incredible products this upcoming year.”
Annie Luchsinger, partner at Breakers
“From my perspective, what we’re seeing is less an AI bubble and more a classic venture cycle playing out around a genuinely transformative platform shift. Venture has always adapted to new normals alongside major technology inflections (cloud, mobile, social), and AI is the fastest-moving one we’ve seen to date.
The difference this time is speed, scale, and capital availability. AI adoption is happening at a faster clip and at a much larger scale than prior platform shifts, all while private-market capital has reached historic highs. As those forces collide, pricing, timelines, and investor behavior evolve.
Capital moving ahead of fundamentals is not new. There will be some shakeouts, but that doesn’t mean underlying value creation isn’t happening. Companies with real technology, real distribution, and real customers will endure.”
