AI Is Creating a New Wave of Opportunities in Industrial Tech

Last month, I joined a webinar hosted by the City of Yokohama alongside Maria Ichizawa of Morgan Stanley and Rayfe Gaspar-Asaoka of Geodesic Capital. The discussion gave me a chance to reflect on how AI is reshaping Climate and Industrial Tech—and where I see new opportunities emerging.

Here are five takeaways.

1. AI is no longer just about software

The rapid adoption of AI is driving demand not only for electricity, but across industries including semiconductors and advanced manufacturing. One of the major themes from our discussion was that this is creating a new set of investment opportunities across Climate and Industrial Tech.

At Impact Science Ventures (ISV), where I currently work, we have seen this trend firsthand. This year, we had two exits: the IPO of next-generation geothermal company Fervo Energy and the acquisition of a semiconductor startup by a major AI company. These developments reinforce my view that industrial innovation and decarbonization continue to be compelling areas for venture investment.

The conversation also touched on a broader shift in venture capital. Rayfe pointed out that over the past decade, areas such as enterprise software, the sharing economy, and marketplaces dominated venture investing, while hard tech and deep tech often remained on the sidelines. In recent years, however, these areas have increasingly moved into the mainstream.

In some ways, that evolution mirrors my own career in tech—from enterprise software at Quid, to the marketplace business at DoorDash, and now hard tech at ISV. It has been interesting to experience that shift firsthand.

2. AI is creating new challenges, too

The discussion wasn't only about the opportunities created by AI. We also talked about the new challenges that come with it.

In the U.S., community opposition to data centers has become an increasingly visible issue. Rising electricity demand and power prices, pressure on water resources, and other local impacts are becoming part of the conversation around AI infrastructure.

I live in Virginia, one of the largest data center hubs in the world, so debates around electricity demand and power costs have become much more tangible to me.

We are also seeing startups hire CFOs and community engagement leaders earlier in their development. Similarly, for investors, the social and community context surrounding data centers and other large infrastructure projects is becoming a more important consideration.

3. The narrative around Sustainability and Climate Tech is changing

Another important takeaway was how the narrative around Sustainability and Climate Tech is evolving—both for large corporations and startups.

Maria noted that many companies are moving away from broad sustainability commitments toward more concrete conversations about value creation and risk management. Among the startups we work with, we are seeing a similar shift: their value propositions are increasingly framed around competitiveness, energy demand driven by AI, and other business imperatives that go beyond sustainability alone.

Critical materials are a good example.

As AI and electrification increase demand for materials, and geopolitical risks force countries to rethink their supply chains, there is growing interest in bringing more production back home. But simply moving yesterday's manufacturing processes back onshore isn't enough. To make domestic production economically viable, we need new technologies that can make manufacturing more competitive.

Still Bright, a copper refining startup, is one example. Demand for copper is increasing with AI and electrification, while the quality of available ore continues to decline and much of today's environmentally intensive refining takes place overseas. Still Bright is developing an electrochemical process designed to refine lower-grade feedstock domestically, with the potential to do so more cleanly and at lower cost.

Since the new administration took office, people have often asked me whether the opportunity in Climate Tech has disappeared. I believe the opportunities remain strong. The narrative is changing, but significant opportunities exist for Industrial Tech startups that can solve fundamental economic and industrial challenges.

4. Two questions I'm watching

While AI is already creating demand for Industrial Tech, its impact on hard tech itself is still at an early stage. There are two questions I'm particularly interested in watching.

How will AI change hard tech products themselves?

Across ISV's portfolio, we are already seeing companies integrate AI into their products and technical development. Fervo, for example, uses data analysis and AI to help reduce drilling costs, while Copernic Catalysts uses AI to accelerate catalyst development.

I'm interested to see how much further this goes—and where AI becomes a meaningful source of technical or economic advantage for hard tech companies.

Will AI accelerate hardware development the way it has accelerated software?

In software, AI is dramatically accelerating product development. Even very early-stage startups can now build remarkably sophisticated products in a short period of time. As a result, traditional Series A/B/C fundraising stages don't always line up neatly with how quickly companies are actually developing.

Will we see something similar in hardware?

If AI begins to meaningfully shorten hardware development cycles as well, relationships with early-stage startups may become even more important for both investors and corporate partners.

5. Early collaboration can unlock better corporate–startup partnerships

In Japan, much of the AI conversation understandably focuses on how companies can adopt and use AI. But an equally important question is how companies can help build the hard technologies needed to solve the new industrial challenges AI is creating.

One path is collaboration with U.S. startups. Combining startup innovation with the experience, technical expertise, and scale of large corporations can help bring new technologies to market.

Early-stage collaboration can be particularly powerful because companies have an opportunity to help shape solutions around real business problems rather than waiting for a finished product.

One recent collaboration that stood out to me involved a Japanese company working with an early-stage startup. Many members of the company's innovation team also worked within business units and had engineering backgrounds. As a result, they understood both the business problem and the technical constraints.

What impressed me most was how they approached the startup.

Instead of asking, “What technology do you have?” they started with: “Here is the business challenge we are trying to solve. How can we solve it together?”

They brainstormed with the startup from the beginning and treated the company not simply as a technology vendor, but as a problem-solving partner that could extend their own R&D capabilities.

Of course, these collaborations are not always easy. One of the biggest challenges is the difference in operating cadence between startups and large corporations.

When I joined my first startup, I was told that startups operate in “dog years”—one year at a startup can feel like seven years at a traditional company because things change so quickly. Startups operate on extremely fast cycles, while large companies with long histories often have longer planning and decision-making processes.

Recognizing that difference—and agreeing early on how decisions will be made and how the teams will communicate—can make a significant difference.

Ultimately, successful early-stage collaboration isn't about waiting to find a finished technology. It's about sharing the problem early and building the solution together.