Tech+Art Podcast: Shane Luke


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Welcome to the new Tech+Art Podcast!

Join us on this adventure as we meet & speak with: artists, makers, researchers, designers and creators from all background and fields.

Our objective is to understand their creative perspective, dive into their workflow & creative process, be inspired by new ideas and their work – and stay one step ahead of cutting-edge industry developments.

"People make the error of believing that you can figure out in the boardroom what’s going to be really successful or useful - and why. "


In this episode, we’re chatting with Shane Luke, an entrepreneur who’s built and scaled several products, teams, and startups.

Shane was most recently at Nike, where he was the Senior Director, Machine Learning and AI, within Nike’s Digital Innovation Group – working on a variety of interesting projects.

He shared a little bit about his process, how innovation takes shape within products, some of the most common challenges in exploring new ideas or uses of technology in creative contexts and more!

Question 1: Can you share a bit about how you’ve created your path - one that focuses on innovation and developing new products - over the course of your career?

So that path kinda came about that way, where I was just naive and I didn’t know what was going on. So I went and got a job at a big company, and then I was naive so I got another job at a big company. And then I was finally not as naive, so I went and joined a startup that looked really promising.

One of the things that I learned was that the coolness of the product is really not important to me. And I don’t think it should be generally important to people. Because, what’s cool about developing technology is that it’s fun and hard to do, that people want to use it, and that you get traction in one way or another.

Question 2: You were the Sr. Director of AI & ML at Nike, can you talk to us a little bit about your role there and what you were working on?

The dimension that was being pushed on at Nike was what we call fine grain […] So one possible output class could be shoe. You send me a picture of something and the computer program classifies it as a shoe.

Question 3: What’s the process for thinking of some idea, thinking of some commercial uses or internal uses before commercialization. What was that process like at a high-level?

I think sometimes people make the error of believing that you can figure out in the boardroom what’s going to be really successful or useful – and why. […] People who don’t understand technology, fall into this sort of approach of saying ‘let’s think of the customer first’, and I don’t think that that’s wrong, but I think you think about it all. […]

If you go think of the customer first and nothing else, what are you doing? You think of some problem: ‘well people have this problem, ok let’s work back to a solution’. Well, things that we know are going to be successful if you work back to a solution have solutions – right? They’re around. If it’s that clear, it’s very easy to solve for. So it’s very rare, I think, for you to be able to find something that ‘oh nobody realized that’s a problem’. The way you find the things that nobody realizes is a problem is that you launch products, people use them and then you see the problem. It’s a rear-looking thing.

It’s really difficult in advance to figure out that, you just have a guess. So you guess, and if you’re right, then it’s easy to see that you’re right. And I think that’s what you want to do.

I think what’s typically done if people try to think their way to the solution, end-to-end. And they want to have consensus that that’s the right thing to do, before they start working on something.

It’s more of a guess and check process. And so I want to take a lot of guesses, cause I know we need a lot of guesses to be successful […] and the way to do that is don’t talk about it much. Just start. If you have the right team they can just start projects: ‘start this’, ‘try this’, ‘hey this sounds kind of interesting’. You do have a thesis in your own head, of what you believe there is product/market fit – you’re just probably wrong.

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Your dose of creativity

Question 4: So given your experience in exploring interesting ideas and problems - what role does creativity play in building technology and products?

Creativity or inspiration or whatever you want to call it, or some combination of those things – is kind of the only thing that makes really good new products. You can extend existing products without a lot of creativity […]

The only way to do that is to start somewhere that isn’t defined by things that you’ve already done – or is only partly defined by them. Obviously you get inspiration from things you’ve already done or things you see or whatever. […]

Rather than optimizing around something that you already have, or something that exists, or that’s a pretty small iteration on something that out there, you kind of try something really new. We don’t have algorithms to do that for us. And by an algorithm I don’t mean inside of computers, I mean there’s no process humans implement that make that happen. And so when you look at larger companies that are doing innovation […] a lot of times, they are looking for some sort of process oriented approach to building new things that matter to their business. And I would argue that: sure there’s a process, but it’s a process we don’t understand. It’s a process that happens inside the minds of people. And that is creativity and inspiration.

The only way to think yourself to success if to not do anything too radical.

You’re in a class of problems that are not things that you can find by just thinking about them, by designing your algorithm in advance. You have some idea, you believe it will be a solution, you try it. In this case, you might try it and if it’s very successful, then it’s easy to see it was right. If it’s unsuccessful, it’s still not a hundred percent sure.

Don’t try to design a perfect solution to finding this. Use your creativity – that’s what’s going to do it. I mentioned earlier that you can try random solutions to a problem? Well your creativity is the thing that’s better than random. The better you are at it, the better you are than just randomly trying stuff. Like the role of human creativity, it’s kind of the essential thing.

Question 5: What do you think is missing in order to help extend that creativity? Is it more ML/AI? How would you approach testing that “better than random” approach to building products & customer experiences?

You can’t just have the product. […] you can’t just pull the product out of the box and give it to people and let them try it. So you have a long process in order to develop this, one in which you know there’s a very high degree of probability that you won’t be successful. […] So at the starting point, you’re trying to make the lightest weight test that will give you some more information. And then you’re making another judgement.

If you’re pre-product/market fit completely – you’re really trying to get some signal. You’re usually working with very limited information, and you’re still pretty much using your own judgement – which I would argue involves a lot of creativity, like the way you’re thinking about what people are doing.

People don’t always know what they want. A lot of value propositions that a product would provide, are hard for someone to imagine if they haven’t seen it yet.

The reality is you’re often not 100% certain, but you have to have some conviction around that.

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