Woodchuck started with a pretty simple observation: we generate an enormous amount of wood waste, particularly from construction, and much of it is treated as a disposal problem rather than a valuable resource. We saw an opportunity to change that by combining waste infrastructure with AI.

Today, Woodchuck helps construction companies understand what is actually happening with their waste and then gives them a better path for it. We use AI-powered cameras, sensors, and image recognition to provide waste intelligence at the jobsite—identifying materials, spotting contamination, tracking volumes, and creating data around where those materials ultimately go. Clean wood can then be processed into biomass and used for renewable energy or converted into products such as biochar rather than being buried in a landfill.

What makes us different is that we’re not simply another waste hauler or recycling company. We’re building intelligence into a part of the construction process that historically hasn’t had much intelligence at all. Instead of handing a contractor a dumpster and sending them a report weeks later, we can give them much greater visibility into their waste stream and help them make better decisions while the project is happening.

That matters because our customers aren’t looking for sustainability at any cost. They need solutions that make economic sense, work on an active construction site, and give them credible data they can share with project owners. Our goal is to make waste diversion more measurable, more efficient, and ultimately more valuable—turning something companies used to pay to throw away into a resource.

Most of our new business today comes from large construction projects where waste is already a significant operational and financial issue. We work with major contractors and project owners building everything from data centers and advanced manufacturing facilities to EV battery plants and other large-scale developments. Once teams see that better waste management can reduce hauling and disposal costs while also helping them meet diversion and sustainability goals, the value proposition becomes pretty straightforward.

A lot of that growth also comes through relationships and word of mouth. The construction industry is very results-driven. If you can demonstrate on one project that you’re saving money, keeping material out of landfills, and providing useful data without making the superintendent’s job harder, people talk. Where I’d like to see more business come from is earlier in the project lifecycle. Waste management is still too often treated as something you figure out after construction is underway. I’d like owners, developers, architects, and general contractors to start thinking about material recovery when they’re planning the project, just as they think about energy, water, logistics, and other major resources.

That’s where AI and waste intelligence become especially valuable. If we know what materials are likely to be generated, when they’ll be generated, and what markets exist for those materials, we can design a much more efficient system from day one. The long-term opportunity isn’t simply getting better at hauling waste away. It’s designing projects where far less material becomes waste in the first place.

One thing we’ve genuinely handed over to AI is a lot of the repetitive monitoring and analysis that happens around construction waste. Our systems can use images and data from jobsites to identify materials, flag contamination, track what’s going into containers, and help us understand waste streams at a scale that would be incredibly difficult to manage manually. That’s exactly where AI is strongest: processing large amounts of information continuously and identifying something that deserves attention.

What we haven’t handed over is judgment. We’ve experimented with using AI more aggressively in decision-making, but we’ve learned that there are points where a human needs to be in the loop. A model can flag an anomaly or recommend an action, but if that decision affects a customer relationship, a major operational change, safety, or something strategically important, we want a person making the final call.

That has shaped how I think about AI more broadly. I don’t believe the goal should be to remove humans from a business. The goal is to remove the repetitive work that keeps people from doing what they’re actually good at. At Woodchuck, we’re increasingly designing AI systems with that distinction built in: automate the routine, flag the exception, and put consequential decisions in front of a human.

Absolutely. Customers are coming into conversations much better informed than they were even a year or two ago. They’ve already researched the basics, they understand that construction waste can have value beyond disposal, and many are asking more sophisticated questions about diversion rates, contamination, carbon impact, renewable energy, and what kind of data they can get from their waste streams.

AI is accelerating that shift. Search used to be largely about finding a list of companies and then visiting their websites. Now people can ask ChatGPT or another AI assistant a very specific question—“How can I reduce wood waste costs on a data center project?” or “Can construction wood waste be converted into renewable energy?”—and get enough context to begin evaluating solutions before they ever contact a company.

We are starting to see AI assistants become part of that discovery process, although I think we’re still early. What’s interesting to me is what that means for businesses. It’s no longer enough to rank for a keyword. You need to be associated with a problem and have enough credible information available that an AI system understands what you do, why you’re different, and when you might be relevant.

For Woodchuck, that’s particularly important because we’re creating a category that sits at the intersection of construction, waste intelligence, AI, renewable energy, and increasingly biochar. Someone may not know to search for Woodchuck, but they absolutely know the problem they’re trying to solve. If AI can connect that problem to what we’re doing, that’s a very powerful new path for discovery.

I would tell anyone in construction or waste management to stop thinking about AI as a separate technology initiative and start with the operational problems they actually need to solve. Pick two or three areas where people are spending too much time gathering information, monitoring routine activity, or making repetitive decisions, and see whether AI can take that work off their plate.

For us, that means applying AI to real-world problems: understanding what’s going into a waste container, identifying contamination, measuring material flows, and turning thousands of images and data points into information a project team can actually use. Over the next 12 months, I think the companies that get the most value from AI will be the ones that connect it directly to measurable outcomes—lower costs, less waste, better productivity, faster decisions, or more reliable information.

I would also tell them to keep humans involved where judgment matters. AI can analyze, recommend, and automate an enormous amount of routine work, but decisions involving safety, customer relationships, unusual circumstances, or company strategy still benefit from human context and accountability.

What would I ignore? Most of the pressure to adopt whatever AI product or trend happens to be getting attention that month. You don’t need an AI strategy just so you can say you have one, and you certainly don’t need to automate everything. Start with the business problem, determine whether AI is actually the best tool for solving it, and measure whether it produces a better outcome.

The companies that do that will probably look less exciting in the short term than the companies announcing an AI initiative every few weeks. But 12 months from now, they’ll have something much more valuable: AI that is actually doing useful work.