Building Successful Startups: Focus On The Problem, Then The Technology

Ayush: Hello, Doug! How are you today?

Doug: Good Ayush! Nice to see you.

Ayush: Same here. So Doug, why not? We start with a quick introduction about yourself. I did mention some of the accolades, but would like to know from you like the different things you have been involved in, maybe in a chronological order. You could start you know like where you started. Where did you grow up? And you know, like what led to the different things? You know that you were involved in, and how did you grow them up.

Doug: Yeah. Sounds good. Yeah. You had in the questions you had. Where did you start your journey? So that seemed like we can start there. So I am the good fortune of being from a family of people, of engineers. Both my grandfathers, my dad as well as folks involved in healthcare. So my wife’s dad, my sister, my mom. So I’ve got kind of a technology in healthcare background which was very lucky to have. Ayush, We’re similar generations. I might be a little ahead of you. But we’ve had the good fortune of having the age of electronics growing up, seeing you know, all the developments that have been basically continuous. 

And so as I was growing up, I had the radio shack electronic kits, the chemistry kits all very accessible. And I thought it would be a fun way to have a career. If I could take these new developments and figure out ways to use them in useful ways. So that’s basically what I’ve what I’ve been able to do. And it’s it’s been a great ride. And, along those lines. I’ve been able to rise successive waves. So I also was interested in startups. I thought, large companies are critically important to the world. We need innovation at large companies. 

But it’s a bit of a cheat to do startups, because you can start small, if you focus in your customers thinking how to use the tech you can go from small to bigger which I’ve been able to do. And I’ve been able to ride successive waves. The Internet, an education startup we sold the Pearson the largest education publisher. Did a couple of platforms, rode the mobile waves as head of engineering for Zipcar and run keeper, and most recently, have been focusing healthcare, I think, which is where we intersect trying to kind of modernize use, use what we learn in the consumer spaces and apply it to healthcare. You know, that’s that’s basically we’re all used to healthcare or to consumer apps. That’s the bar and in healthcare, we wanna raise the bar to have, you know, similar capabilities whether you’re in a hospital, or you’re a patient.

That’s ultimately where I’m focused. And then at MIT. They asked me to come in and build tools for startups. So I’ve done multiple startups. Now, I’m getting a candy store, getting to build tools for entrepreneurs, and using the latest. So the latest wave is the Generative AI wave. And we’re building using the latest AI tools to build what is turning out to be quite useful tools for entrepreneurs built on MIT’s discipline, entrepreneurship, and startup tactics frameworks.

Doug: So that’s me as a monologue on it. But very lucky to have grown up during this time. Now, I feel like our generation needs to help correct and and sponsor the new folks coming in to fix the problems that we’ve left, climate, healthcare. So just came out of a MIT session with Ocean MBAs looking at climate tech so

great place to be. And we’re trying to make these tools available ultimately worldwide, which is consistent with MIT’s mission.

Ayush: Right, so very interesting. And Doug you know, as you know, I’ve seen like the versatility in your experience, and I think the agility to change cares that you have done so well throughout this career that’s really commendable you know, because, you know, making those choices. And you know, like going with

the possibilities is equally important for one to grow just like now you have joined the hotbed of Gen AI. What I can see, you know, at Orbit the MIT’s entrepreneurship platform. So tell us more about that. And then also elaborate more about the Gen AI view – from where you see it.

Like, you know, because lot is happening with AI today, but we would love to learn your insights into it, and what’s coming in the future.

Doug: Yeah, so I have, had the good fortune to have worked across a number of successful ventures. And I think I feel like I’ve been able to assist, but essentially

I’ve been able to find people that had mission-driven startup companies. And what I’ve been able to do is really pattern match, really, really well. So I basically have been able to find places that really felt like they could go forward, and then my main goal was to help keep pushing the opportunity up – up into the right, and

that’s made it easy to move around so that my agility is basically knowing a lot of other smart people and collaborating with them. And certainly that was the case here at MIT. So when the Gen AI wave hit, we were all sort of trying to figure out where to get our feet on the ground, and when I find, I feel like I’m a little unmoored I usually begin to think who can help me get unmoored, and began reaching out. And certainly being at MIT is a – There’s a lot of people. So you look for people you wanna work with, who can help. 

And now I found a huge group of people that we can help and but the the critical part that I would impart to anybody is, think about what problems you’re trying to solve and then how the technology can work to solving those problems as opposed to getting enamored just with the technology, in developing it and then later trying to figure it out. So we just came out of a session with we have a week-long boot camp for Ocean MBAs, and we emphasized  really thinking about who’s your target market? Who’s your beachhead market? Essentially, with any new technology, where can you be great? where can you make a difference? And this is true at large companies, if you’re doing a new initiative. Who’s your customer? How is this gonna improve? What problems are you solving? How is this gonna improve from the status quo? And Ayush, I know as you work with your clients, you try to steer into the same thing, and you look for clients that are thinking that way. That’s really the whole game. And then the technology comes afterwards. 

Ayush: Right. Right. So how are you basically making a difference really? You cannot start with the technology, basically. So you have to start with the problem. And then, is there technology? Or can you make a technology that can solve it?

Doug: Usually always, at least for me. Starting with what’s the problem, and then figuring out the technology, how it can make a difference. And kind of riding, you know, is mobile applications come to the forefront. How could we apply those? So keeping an outlook for people that are doing things that are you know, for me. The first piece is, do I care about the mission of the company? So I would strongly encourage people.

Do I care about it? Will my friends care about it? So if I’m helping recruit others, would I want them? Would I be proud to bring them in? I, I think, are critical, and do I think it can be successful if if you bring all your friends to a place that can’t be successful. Then, that’s hard for everybody. So kind of get an assessment. What I think the prospects are, how the entity can make money. And do I agree with the mission?

Doug: That’s first. And then figuring out, do I like the people involved, you know? Is this somebody I want to work with? Cause you could be with people you want to work with, and going down a place where you’re 5 years too early, you know. There’s the people often say I had that idea 2 years ago. My answer is inevitably, that’s great. It’s it’s all about timing, so is the timing right? So the right people, the right timing and then you can kinda build around those things. And that’s what I’m, I’m looking for that fit. So I was a runner. So Runkeeper was a natural fit.

That was also a kid in the candy store. I’ve been a startup person working here at the trust center and providing tools at MIT, but also beginning to make them available worldwide super appealing. And and now we’re in the stage of are they really giving useful outputs? And the fun thing for us is they are beginning to give very useful outputs. I can speak more about what we’re doing and what that is. But that’s basically the recipe I followed. And Ayush, I think you’ve done similarly, I think you’ve steer more to healthcare because you see the the need and the expertise, so you can’t be great at everything. When you do work, you want you and your company to be great and similar.

Ayush: In order to go deep, you have to focus, because otherwise you are just too spread, too thin. Basically.

Doug: Yeah. And you can’t be great. You just, if you just coding, you’re just okay across things. You wanna like, you all have focused on healthcare. You can get great in it. That’s what I’ve tried to do, and that’s what I always recommend for startups is, where are you gonna be world-class? And do you see a path? You might not start there. But you should have a path to it.

Ayush: Right, no makes so much sense definitely, Doug, but I’d like to touch upon the Ocean MBA you know, would like to learn more what’s happening there, and especially around climate. So because we know we’re sitting on a time bomb. And for most of us really don’t have any control on it like what we can really do. So tell us more you know, because since you’ve been deep into that for the recent times.

Doug: So I’m at the Martin Trust Center for MIT entrepreneurship, the entrepreneurship center. And our thinking is we’re gonna need entrepreneurs, entrepreneurs like you, Ayush, and entrepreneurs like the Ocean MBAs to solve our biggest problems. We’re gonna need that level of innovation. So we’re really encouraging mission-driven startups wherever possible.

Climate is one of the big initiatives MIT just wants for Climate Center, for exactly the reasons that it’s an existential threat. So these are folks with a whole range of different technologies, you know biotech, microalgaes, for more efficient aquaculture. New battery technologies, carbon capture a whole range of different solutions, we try to kind of, we segment those solutions and then ask them to further segment it.

So we’re helping our role we feel is to give them the best tools. So I just presented our AI tools to them.

And we’re beginning to refine our tools to specialize for things like climate and healthcare. So we’re trying to give them the best education we have, and then begin to build communities. So this Ocean MBA is a boot camp for other people in climate startups to meet each other, the investors and get MIT style training. And then as we work, this is a worldwide problem. So MIT’s mission is to serve MIT in the world.

We’re looking to where we can take our learnings and make them available worldwide, which is why I’m happy to talk to you. You’ve got a worldwide perspective.

Ayush: Right. Right. Not true that. And it like you said, it’s not a problem of a particular area or a country. It’s like a global biggest problem. In fact, just to share we as a company you know, just became carbon-neutral certified this year. So we ourselves and you know, that’s one of the values I had taken when I went to Berkeley. And it was all about like while work is important, you have to still do it in a way that is sustainable and good to the environment, good to the people. So you know, all these values, while may look small. They make a lot of difference to

how you really live your life, you build your company or build things, and so on. So I’m like, always, in fact, a big proponent of the whole cause, because I can see that it’s such a big problem that has to be solved. And we can at least do small bits whatever and whenever possible.

Doug: That’s right. And we encourage the students to be thinking that way. You know, how can they make a difference in the world. And yeah, I think each of us has to do our part and then we’re also looking for these really smart innovators to come up with ideas that we may not have thought of. And so that’s the kinds of things we’re doing with the climate MBAs, for instance.

Ayush: Right. So, Doug, tell me one thing which is always challenging for early stage, or a student startup is, you know, over the you know, over the years. How have you seen, 

So I was asking that. What do you see as the recipe for making these startups really make a mark? Because you know. Of course, everybody starts with a promise of making a world-class product or making a dent to the world. But then, what could be those repeatable lessons that you would like to share for student startups, not only at MIT, but also throughout the world, because, like you said, we all need

new entrepreneurs and new thinking, and students and young people are our hope. So, you know how we can all do better at it, or how we can create that ecosystem, or what lessons we need to inculcate to the young guys. 

Doug: Yeah, great, great question. And we’re we think about that a lot. Cause we’re the center here at MIT, and we feel responsibility here and beyond. So we’re very lucky you are at Berkeley, that’s another hotbed where you’ve got all the elements.

But how do we make those available worldwide? So I’ve worked with people in Pune and Shanghai. Those are other hotbeds. So the US doesn’t have a monopoly on these things. But we do want to share best practices or what we learned. 

You know the elements. certainly. Leadership Ayush, you provide that for your organization, I think, having strong, committed ideally more about the mission, less about the ego style. Leadership is critical. That’s what that’s what investors are looking for.

Folks can run the risk of looking at investment first, often, with today’s technology, a lot of problems can be solved much less expensively, not always true in climate, which is what we’re trying to help sponsor, but certainly in software and Ayush you certainly seen that. And so we really work on focusing on the customer solution first, and then the building of it later. From a technology standpoint.

I’m pretty confident. I think you are Ayush as well, that if we’re asked to build something we can build almost anything. It’s it’s really just amount of time and resources and more resources isn’t always better. So it’s a tight focus. And really understanding the problem to be solved and the priorities of the problem to be solved. So if you, if you have a big problem. You can’t eat an elephant. One bite. You have to either a step at a time. 

So those basic understandings which certainly in software, I think we’re understanding. Really help. I think as you’re solving problems, understanding investors who are also interested in the idea is critical. That’s that that does become the fuel. So I think having early conversations and relationships, if you’re building a company on the innovation side is important, but also looking how far you can get on your own investments you’ll have more control. So at Runkeeper Jason was able to build a lot out small and so he ultimately had a lot of final say until we sold it to ASICS. 

So, Ayush, you must be thinking that way, too, how much you can self-fund. So, but it all comes back to looking at the problem and then looking at the market opportunity and providing a map an economic map along with a technology map, is the key.

Ayush: Right, right. True that. And we often say that in technology that you know, building is easy, but finding the market fit or the use case benefit. You know, is important and becomes really the key for finding success. Because, you know, technology today is definitely far more easy if I can just say as compared to you know how difficult it was. Say, building something say 10 years or 20 years back, but then you still have to you know make sure that what you’re building is really

fit for the audience or for the problem that you are solving, and we still get surprised all the time. 

Doug: And that’s the risk is, it’s fairly easy to build an app

and so you kinda get started before you focus on it, and Runkeeper would be a good example. Obviously, this early focus was running as we began to get ourselves bigger, we began adding other sports and to the point where, you know, biking and a lot of runners do kind of cross-training. But we began to find ourselves developing a more of a general health app as opposed to.

So we’re good general health app as opposed to a great running app, and so we had to refocus, and we found the employees of Runkeeper tended to be former runners. You know, their idea of running is 6 to 8 min miles. I started there. But as I’ve gotten older, I began to evolve into our target market, which we found was beginning to intermediate runners and so when we finally refocused ourselves on running that was the market. 

If you’re gonna carry a phone, you’re more of a beginning to intermediate runners, and we became a gateway to running, which is a fabulous place to be, and that’s what ASICS was most interested in is that broader segment. So even Runkeeper we had to redouble our efforts and refocus on running. And that’s a message I think Ayush, you would want for your clients as well. 

It’s it can be sometimes too easy to build and the action on building becomes a substitute for real market knowledge. So when we focus back on running, we had to go back out we actually accosted people on the street and and asked them and you know, commendable to our user experience department. Sohere we’re always talking about focusing on the customer. You don’t get smarter talking to your own team. You get smarter talking to your customers.

Ayush: So my last question, Doug for today would be, you know, again. I’ll go back, since you are in such a perfect you know, place in terms of where innovation intelligence and technology are coming together. So what are your big bets or big ideas that you’re counting on you know, or the great themes that are coming along that you see in the next 3 to 5 years to make the world better?

Doug: Yeah. So in the near term we’ve been really hardened. Everyone’s looking at the large language models and trying them out, that becomes just tools available for all of us to build on. So just like the mobile technologies. We could build on Android and iOS, we could count on those, and the hardware associated the cloud infrastructure we could count on. We can now count on the essentially the large language models will be in an arms race to build the best possible. And so for us, that are not at the large tech companies, we can apply the technology. 

So it really comes down to thinking through the same things we’ve been talking about where you apply it. So we found great success. We have a 24-step entrepreneurship framework. It’s a lot of steps. But it’s the right steps. It’s thinking about the customers, thinking about finance, I think Ayush, you’ve looked at it. We’ve pulled the best practices across a range of books and research. So what we’ve been surprised at is how well the models work when we focus it step by step by step, when we were blending it. They got confused. But our frameworks really help so thinking about problems like that, we have a alumni working in the Australian railway system.

They could do the same things. They have process documents they could automate in my mind, particularly on that AI side is thinking how to assist a human rather than replace a human. So a lot of us have been surprised at the push for self-driving cars. But the technology to assist drivers is fantastic.

Taking a driver out of it as a software engineer, I get concerned, I think, the only software without bugs is not used but you can have bugs as long as a human is there to kind of catch it, and you can push the boundaries. So when we provide tools for entrepreneurs, they’re AI assistance for entrepreneurs. There’s no substitute for the entrepreneur doing its own research. 

But boy, you can provide assistance. So I would encourage users to think about how where areas that you could provide assistance rather than replacing people. So all the process documents you have at large corporations. Healthcare which is the area that both of us are focused on is right for that. The world’s corpus of healthcare information has been put online. If you’re a nurse or a doctor, you’re doing Google searches today. That data is very accurate all the coding systems, SNOWMED codes for diagnoses, LOINC codes for lab results. Those are all published and continually published.

AI can do a great job of pulling that together as long as we’re thinking that there’s a human, and examining the results and finding ways to continue to make the results better. Which will happen somewhat automatically as we train the models. But also, as you’re developing projects in healthcare, you want to keep thinking about, how do you help a human help a patient. And I think in those areas we can be fairly aggressive and beginning to do. 

I’ve been involved in the FHIR work for standardizing healthcare beginning to think how we can do automation, but all with the idea of making a better experience for the patient. If we can stay patient focused and keeping humans in there. Not just trying to automate it. I think we make mistakes. I’ve done a bunch of automation projects literally in factories. And I had a few where I had to back off the automation I inherited the first line where we tried to take too many humans out and was asked to prove it. And I put more humans back in.

So the goal should be to assist our our our fellow humans. And if you do that, I think the new technologies can be really used and aggressively, because there’s someone looking at the outputs and keeping focus on how you’re making the world better with what you’re doing.

That’s my take.

Ayush: Okay. No, Well said, I think again connects back to the foundation piece of how, you know, there’s still these foundational problems that need to be solved, and from that only, like some of the great companies or startups or ideas would come up.

Doug: Yeah in the you asked kind of where do I think? You know we’ve got the

big, well-known anthropics or perplexities? I, personally am hoping companies like Liquid AI that are people are beginning to look at how to make these large language models more efficient. I think it’s going to be important. So it’s not just applying the technology. We need to make sure. That we’re applying it in ways that are good for the world, including, I think that’s one of the problems I have personally, with Bitcoin is the energy consumption, much less the other issues. But large language models can help us. We need to find ways to make them more efficient. People like Liquid AI.

Ayush: Yeah, there was this Microsoft launch of a mini LLM kind of thing which can be run on a less capacity, and you know, can be for a specific task or specific kind of.

Doug: That’s right. And Ethan Molock is talking about agents so smaller LLMs for specific tasks, I think, are really gonna end distributed. So we, our phones or laptops, all are can run the large language models, and we just need to think about it both from efficiency at getting the job done, but also at things like energy efficiency. You know. Shame on us if we invent this new technology that doubles our energy consumption. We haven’t really moved the bar forward. So I’m hoping that we see innovations and those kinds of areas.

Ayush: That’s super awesome stuff, Doug, thank you so much really enjoyed this conversation. And some points well taken. So, thank you so much, and it was a pleasure hosting you.

Doug: Thank you. Thank you, Ayush, and good luck to you and your and your team. So

Doug: bye.

Welcome to HealthTech with Purpose! In this episode, Ayush Jain talks with Doug Williams, a Product Lead at Orbit Software and an AI expert at MIT. Doug shares tips for turning ideas into reality, focusing on solving real problems, and prioritizing customers. He also discusses the future of technology, including AI’s role in empowering humans. If you’re interested in innovation or starting a business, don’t miss this episode!