Schneider Electric’s VC Arm: The AI Buildout Is Creating A New Industrial Investment Cycle

This is an ongoing series on investors focused on rebuilding the physical layer. Previous interviews in the series were with ex-Meta CTO Mike Schroepfer, founder of Gigascale Capital, and Peter Barrett, a decade-long investor at Playground Global.

Schneider Electric has spent nearly two centuries adapting to successive industrial revolutions — evolving from a 19th century steel and heavy machinery company into a global leader in energy management and automation. Now, through its $1 billion venture arm, SE Ventures, the company is betting that the next transformation will be driven by AI’s collision with the physical world, from data centers and power grids to robotics and industrial automation.

For Amit Chaturvedy, who joined SE Ventures in 2022 after leading corporate investments at Cisco, AI’s biggest opportunities extend well beyond software. As demand for compute strains energy infrastructure and accelerates reindustrialization, the firm is backing startups building the technologies that underpin the AI economy — investing in everything from data center infrastructure and grid resilience to robotics and industrial AI.

Crunchbase News spoke with Chaturvedy about where those opportunities are emerging, why energy has become AI’s defining constraint, and how industrial technology is being reshaped by the AI era. “We were set up with the intent to figure out where the market is headed,” he said.

The significance of the energy and industrial sectors has grown with AI, and that has led even traditionally tech-focused venture investors to rush into the space. “Today, the scarce resource in this entire space is the capacity to build — building, real estate, energy, power and electrification gear,” Chaturvedy said.

SE Ventures counts eight unicorns in its portfolio, and has notched 12 exits including its most recent, Fabric8Labs, a 3D metal printing technology acquired by Tokyo-based electronic manufacturer TDK Corp.

The firm will often take board seats or board positions and work to bring value to its portfolio companies.

Around 80% of the startups in its portfolio have some level of commercial relationship with a business unit of Schneider Electric. Most often that’s as a partner servicing Schneider’s customers, which is the holy grail, according to Chaturvedy. Sometimes it’s as a vendor, although that remains a smaller set of use cases.

In our conversation, we spoke about power scarcity, the electrical grid, workforce training, reindustrialization and notable portfolio companies.

The interview has been edited for length and clarity.

Gené Teare: Which sectors or investments are you focused on? Where there is a lot of drive or interest because of what is happening in AI?

Chaturvedy: Three things come to mind, especially in terms of the areas we invest in versus the broader construct of the market.

First, AI is getting embedded, and you need to train models, whether open source or proprietary. Model training has upleveled to inference so you need AI infrastructure. Together AI is a great example of that.

Five years out, when this CapEx cycle starts to come down and new data centers are perhaps not getting created, data center efficiency will become a hot topic. We are also investors today in a company called Hammerhead AI, which focuses on that problem. That will come three, five or seven years out. It is going to come. It is not a problem today because we are on the upswing of the CapEx cycle.

Together AI and Hammerhead AI are very interested in partnering with Schneider Electric, because Schneider Electric is a leading electrification player in the data center space. It makes a lot of gear and equipment that go into these data centers. Today, the scarce resource in this entire space is capacity to build: buildings, real estate, energy, power and electrification gear.

The other market impacted by the emergence and growth of AI is the interplay with the grid. There are more demands on the grid beyond the electrification of vehicles, and it is 100x or 1,000x bigger than what we saw with vehicles needing to get charged from inside houses. The grid could not keep up with that capacity in the past, and it certainly cannot keep up with these demands today.

More project developers are coming in and setting up renewables or other types of capacity, but again, the interplay is still with the grid. Anything that helps with grid resilience is clearly an area for us to invest in.

The third thing is the transformative impact of AI on the world of industrials. That is where we are quite excited. Robotics is one clear area where a general-purpose model can allow the same robotics hardware to do multiple different tasks that were not possible in the past, because cognition and inference were not possible at the edge before the advent of large language models.

Companies like Skild AI in our portfolio — which is one of the most exciting companies at the intersection of robotics and AI — are market-leading indicators of where this world is headed.

There is also an element of using AI to deliver better use cases in the field. Companies like Axion in our portfolio essentially capture warranty data, analyze it and feed results back to design engineers in big corporations. There are a lot of OEMs and hardware companies looking for select use cases where AI can actually be very transformative.

That is what customers are looking for: How can AI be transformative for my business? Whichever startup is working with me in that transformation journey is the startup that will move from POC to adoption overnight. That is essentially the world of successful startups.

Overlaying on top of this is a confluence that we see and watch from our vantage point. When you think about the energy efficiency that needs to happen in these industrial worlds, energy technologies and industrial technologies have to collaborate and deliver those use cases while being energy efficient. That was not the case in the past. Energy was cheaper and more readily available.

Now industrial is taking off. There is more AI adoption. The workforce is getting older, and there is no way to overnight train a workforce in America, so you have to rely on AI. You are going to consume more and more AI for industrial use cases, which was never a business imperative in the past.

This is where the worlds of enterprise and industrial are colliding very quickly in the world of AI.

Increasingly, what I hear is that the bottleneck for AI at this point is energy. Are you seeing some short-term solutions that help with this? What about longer-term technologies?

Chaturvedy: It’s very clear that from a short-term basis — and this isn’t quite an energy-related solution — it’s more about tokens. If you think about the unit economics of an AI data center, it’s the tokens. To generate a token, it costs electricity. To train your model, or infer from a model, you need a lot of tokens. The bigger the model, the bigger the data set, and the more complex the use cases, the more tokens.

Ultimately, it’s a battle of producing tokens cheaply and also consuming fewer tokens through the models that exist today. That’s where optimization is happening, but that’s more in the enterprise space: How can I write clever versions of software that allow me to do essentially that?

The longer-term solution is going to be about — actually, maybe there is a middle layer also — beyond the tokens: When I’m running my data center, can I push inference to a different point in time so I’m not consuming peak electricity rates? Can I manage my HVAC better? You need cooling systems to cool your data center environment, and there are techniques that work really well there. Schneider has also bought some assets in the past.

Then the longer-horizon cycle is really about creating new generation capacity, largely through renewables, hopefully. That’s where I think the whole renewable story, at least in the U.S., becomes very interesting going forward. Related to renewables is storage, which we haven’t touched upon, but BESS — battery energy storage systems — is another space that we look at very closely.

There is a huge discussion in Europe and in America around reindustrialization. How do you see that playing out, given the sectors you’re focused on, industrialization and energy?

Chaturvedy: I don’t think America or Europe really have a choice other than to reindustrialize, given the geopolitical situation and a variety of other factors that I’m sure you fully track as well.

We know that technologically, the U.S. has a competitive advantage. We produce great software engineers, we move fast, and innovation is the lifeblood of U.S. society. There is a lot of innovation happening here, whether it’s robotics, newer models, setting up data centers, energy generation, and so on. That’s where the U.S. is going to lead as we think about reindustrialization: training the workforce, doing things more automatically, with the holy grail being AI startups that result in lights-out manufacturing facilities.

That becomes more of a possibility now. We are never going to be able, in my opinion, in the next three to five years, to replace an aging workforce and expect them to be trained to the same level that a technician with 30 or 50 years of experience was at. But it is now possible that every blue-collar worker with AI in their hands as an assistant becomes a knowledge worker.

Earlier, we used to think about knowledge workers as IT people or white-collar jobs. I think that’s changing. Everybody will be a knowledge worker. AI will be such an equalizer in that sense. There will be different use cases in different environments, but that doesn’t change the business reality. Everybody becomes a knowledge worker.

The second thing to note is that not every job will come back. There is a reality of inflation, cost of living, and the quality of living in America that people are used to, whether it’s base pay, hazardous environments, number of shifts or what have you. As a society, we’ve made certain choices. We’ll be smart about how we leverage more AI and more robotics to get what we want, and not try to emulate other manufacturing-heavy geographies.

But as an economy, as more AI comes, we will move to a different level in terms of what constitutes the GDP of America and the goods and services underneath.

How long do you think that takes to play out?

Chaturvedy: There are certain industries where it’s already happening, data centers being at the forefront. Mainly because the need is very urgent, and there are significant dollars at play today in the data center space, where people are willing to spend the money. In a capitalistic society, everybody is going to chase money. The data center happens to be that today.

But in the next three to 10  years, depending on the CapEx refresh cycle of different industries, we will see more greenfield projects emerge that are natively robotics-oriented and natively industrial automation-oriented, because AI has already caught up.

Right off the bat, every new factory that gets online in the next seven to 10 years will have a basic level of productivity that is way higher than a new factory set up 30, 20 or 15 years ago. The ROI from that factory would be so strong that you would have to expand more capacity there. And by the way, capacity would also be more scalable.

On reimagining or thinking through the data center stack: is there anything you want to say about that as we close out?

Chaturvedy: We specifically invest in AI for energy and industry, looking across the full stack from data infrastructure to training and inference, through to AI agents solving real-world use cases. We also consider the enabling layers around that stack, like multi-cloud, multi-LLM, cybersecurity, and data governance.

Ultimately, every industry is going to build its own version of this stack, and we believe the most compelling companies will be the ones who drive tangible outcomes in enterprise and industrial environments.

Related Crunchbase queries:

• SE Ventures Investments
• SE Ventures Portfolio Companies
• SE Ventures Portfolio Unicorn Companies

Illustration: Dom Guzman