Podcasts

Chris Young has never been shy about providing his thoughts about the future of cooking.

Whether it was on stage at the Smart Kitchen Summit, on his YouTube channel, or a podcast, he’s got lots of thoughts about how technology should and eventually will help us all cook better.

So when I caught up with him last week for the Spoon Podcast, I asked him how he saw things like generative AI impacting the kitchen and whether it was necessary for big appliance brands to invest in building out their internal AI competencies as part of their product roadmaps for the next decade. You can listen to the entire conversation on The Spoon podcast.

I’ve excerpted some of his responses below (edited slightly for clarity and brevity). If you’d like to listen to the full conversation, you can click play below or find it on Apple Podcasts or wherever you get your podcasts.

On the resistance by some to using advanced technology to help us cook better:

Young: “A lot of people are focused on going backward in the kitchen. They want to go back to cooking over charcoal and cooking over fire. That can be fun, but if you look back at what it was really like in the 19th century, the kitchen was not a fun place to be.”

“The modern kitchen is much healthier and much safer. And it does a better job of cooking our food. But we’ve kind of stalled, in my opinion, for the last couple of decades of really innovating and creating a compelling vision of what the future of the kitchen can be. I think the idea that our appliances are too stupid to know when to turn the temperature up or down to cook my food correctly is bizarre in the modern world where sensitive, high-quality sensors are cheap. And we have unlimited compute and AI now to answer a lot of these questions that humans struggle with, but I don’t see the big appliance companies or the incumbents doing this on their own. So, my small contribution was to create a tool that measures temperature and makes it very easy for people to do things with those measurements.”

On why it’s important to create a vision for the future of a technology-powered kitchen:

Young: “My criticism with a lot of people in this space is they haven’t sold a vision of what the future of that your kitchen could be like that resonates with people, that feels human, that makes it a place I want to go that is forward-looking rather than backward-looking. The kitchen of the 1950s, the kitchen of the 1920s, feels more human, feels more relatable, and I think people want that. It’s not to say you can’t create a forward-looking vision of a kitchen where it’s easier to cook food, it’s easier to bring people together and have everything work out right, but nobody’s really creating that vision.”

Combustion’s thermometer runs its machine-learning calculations on the chip within the thermometer rather than in the cloud where many AI compute happens. Young explains how – and why – they made that possible:

Young: “One of the crazy challenges was this is some pretty hardcore math. I think even we initially thought, ‘Oh, we’re gonna have to run this on the cloud, where we essentially have unlimited compute to run these fairly sophisticated algorithms.’ But we have some very clever software and firmware people on our team who have a lot of experience doing these kinds of hardcore machine-learning algorithms. And we were able to basically figure out some clever trick techniques to get the stuff running on the thermometer. The benefit is that it means the thermometer is always the ground truth; if you lose a connection, if you walk too far away, or if Bluetooth gets interrupted, or if any of that happens, the thermometer doesn’t miss a beat. It’s still measuring temperatures, it’s still running its physics model. So as soon as you reconnect, the results are there, and nothing has been lost.”

Young on the benefit of generative AI:

Young: “In the short term, AI as it’s being marketed is going to be disappointing to a lot of people. It’s going to burn some people in the way that IoT burned some people. But there’s going to be meaningful things that come out of it.”

“…When I was playing with ChatGPT 3.5 and I would ask it cooking questions, the answers were mostly garbage, as judged from my chef perspective. When GPT 4 came out, and I started asking some of the same questions, the answers were actually pretty good. I might quibble with them, but they wouldn’t completely fail you and they weren’t garbage. And if you modified the prompt to rely on information from Serious Eats, ChefSteps, or other reputable sources, all of a sudden, I might have given you a different answer, but it’s not necessarily better. And in many cases, what people want is a good enough answer. Building those kinds of things into the cooking experience where, when you run into a problem, or you’re confused about what this means, something like the Crouton app, or the Combustion app, or a website can quickly give you a real-time good enough answer, that actually solves your problem and keeps you moving forward and getting dinner done. Those I think will be really, really big payoffs, and that stuff’s coming.”

Young on whether big food and appliance brands should invest on building their own AI internal competency:

Young: “It’s hard to give advice when that’s not my business. But I have a few observations from having worked with these companies. It’s very hard to sustain a multi-year effort on something like an AI software feature. For these companies, that culture doesn’t exist, the way of thinking about the long term payoff of software tends to not be a strength of these companies. And so while they have the resources to go do this, the willingness to make those investments and sustain them, for years and years and years, and learn and iterate, that hasn’t proven to be their greatest strength.”

“I think that is kind of why there was an opportunity for Combustion, and for a company like Fisher Paykel (ed note: Fisher Paykel has integrated the Combustion thermometer to work with some of their appliances) to recoup the millions and millions of dollars, we’ve invested in the AI in our algorithms team. (Fisher Paykel) could maybe build the hardware, but doing the software, investing in the hardcore machine learning research, I think it would be very hard for them to sustain that effort for three or four years when they’re only going to maybe sell 12-25,000 units a year. We’re in a much better position because we can spread it across the entire consumer base.”

“And so I think you’re going to see more partnerships emerging between the big appliance companies that can provide the infrastructure, the appliance that’s got ventilation over it, that’s plugged into a 240 volt, 40 amp or 50 amp circuit. They’re going to be very good at that. If they basically open up those appliances as a platform that third-party accessories like the predictive thermometer can take advantage of, I think over the long term, they actually take less risk, but they actually get a market benefit.”

“Because as more small companies like Combustion can get wins by integrating with these appliances inexpensively and easily, making our products more useful, I think you’ll start to get a lot of things like the rice cooker no longer has to be a dedicated appliance that you put in a cabinet. Instead, it can be a special pot that goes on the stove. But now it can communicate with the stove to do what a rice cooker does, which is turn the power on and off at the right time. And now a lot of these small appliances can migrate back to the cooktop, they can migrate back into the oven.”

If you want to hear the full conversation with Chris Young, you can click play below or find the episode on Apple Podcasts or wherever you get your podcasts.