theCUBE + NYSE Wired: Physical AI & Robotics Leaders | Jon Brennan-Bedal, Opentrons

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Jonathan Brennan-Badal

CEO

Opentrons Labworks Inc.

play_circle_outline Revolutionizing AI Communication and Automation through Integration with Robotics and Novel Assays for Cost-Effective Experimentation

play_circle_outline Example of high-throughput screening cost reduction and time efficiency.

play_circle_outline Success of Opentrons reflected in revenue growth and number of robots deployed.

play_circle_outline Role of open source in enabling collaboration and innovation.

play_circle_outline Benefits of utilizing robotics and AI in scientific research

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Jon Brennan-Bedal, Opentrons

Jonathan Brennan-Badal

CEO Opentrons Labworks Inc.

Understanding the Role of Robotics and AI in Life Sciences: theCUBE and NYSE Wired Robotics and AI Media Week Coverage

In this insightful session, Jon Brennan-Badal, the Chief Executive Officer of Opentrons, shares expertise at theCUBE and NYSE Wired Robotics and AI Media Week. This event, hosted at the New York Stock Exchange, explores the intersection of robotics and artificial intelligence with a special focus on life sciences, showcasing how these technologies propel innovation in pharmaceuticals, healthcare, and biochemistry.

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What has Opentrons built in order to reduce the cost of running experiments and generate data more efficiently in the era where everyone has super computers? add

What are some advantages of using high-throughput screening in drug discovery? add

What is the company's current status in terms of fundraising, robot deployment, revenue growth, and the use of AI technologies? add

What is an example of how robots can be used in chemical synthesis processes? add

What new capabilities is the company pushing the limit to enable, specifically in the field of genome scale cell engineering? add

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Jon Brennan-Bedal, Opentrons

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>> Welcome back everyone, to theCUBE Live here in New York City at our New York Stock Exchange Studio with the NYSE Wired community, our new open source trust network that's forming between our Palo Alto Studios here in Wall Street connecting tech and business and money. Robotics is a big event going on this week here in New York City. Of course, we got all the best people coming in and we're seeing more and more physical AI, more digital twins, more AI intelligence, helping the humans in the loop. And of course, one area that's impacted the most by that's life sciences and the development around pharma, healthcare, drugs, biochemistry, all the heavy lifting compute, where software is going to do a lot of great good for the world and of course, humanity. We got a great guest here, Jon Brennan-Badal, CEO of Opentrons is here. Jon, great to have you on. Thanks for coming on.

Jonathan Brennan-Badal

>> Thank you. Pleasure to be here.

>> We were riffing before we came on camera about some of the work going on at scale around some of the advances with whether it's GPUs and all this super computing capability, that's essentially been democratized. Right? So you combine that with the wave and surge of continuing with open source, you got Opentrons doing some great work around open source, but also you're doing some of the work that's going to allow people to do more things in life sciences, where now everyone's got super computers.

Jonathan Brennan-Badal

>> Absolutely, and so Opentrons has built this, I think, powerful platform using robotics, novel assay, and AI, to just collapse the fundamental cost of running many experiments. And so we can now generate data 100 times cheaper than traditional methods. What that means is, for the same amount of money and effort, you can generate 100 times more data, or if you're willing to spend a little more, you can start generating a thousand, 10,000 times larger data sets. And now that creates a massive opportunity where there's value in creating that much data because you can put that into these AI data centers to really train and develop fundamentally better AI models.

>> I mean, it's just an amazing surge of innovation because the speed of advancements, just the breakthroughs specifically, happen faster. We're seeing quarters, what used to take years, quarters, quarters to months and days, all fundamentally happening like right now. So I have to ask you, what are you guys doing specifically? Lay out what your business is and where you're seeing the needle moving specifically.

Jonathan Brennan-Badal

>> Absolutely, and so there's three key components of our platform. A robotics element, that is an affordable robot that can automate a wide range of different activities in your lab, and it's highly scalable so you can run things in a parallel fashion to generate massive data sets. Second, on top of that, we've developed proprietary assays so that those common activities, you can actually run in a fundamentally more efficient manner. And then our AI platform built around top is really two-fold. One is our text to action, so you can just give natural language prompts and the robot can convert that into code and readily run those activities. And then second, leveraging some of those massive data sets that we've generated, we can enable you to design fundamentally more efficient experiments. And so combining all of that together, it's really a turnkey solution to get your lab work done in the most efficient way possible.

>> Yeah, I love this movement and one of the things I've noticed is stories drive movement. So, tell me a story about where this is working. Layout the use case, give an example where that all comes together.

Jonathan Brennan-Badal

>> Yeah, absolutely. So I'll give you an example of high-throughput screening, which is important activity in the context of drug discovery. And so, our robots can automate these types of activities at literally 1/30th the cost of the nearest competitor. Then layered on top of that, we've built our own proprietary assay with our own genetically engineered cells that can run a fundamentally more efficient process. You combine those together and we're able to take a campaign and reduce the all-in cost of running a discovery campaign by over 20x, and run that same campaign in about 1/10th the time.

>> All right, scope the magnitude of the old way versus the new way. Just, what does it look like? Scope that out, because old way was what? What would the process look like in the old kind of way?

Jonathan Brennan-Badal

>> You have-

>> And what's the new way look like?

Jonathan Brennan-Badal

>> Totally, and so the old way oftentimes involved manual processes, manual pipetting. And even in the old, second the old processes use old techniques where you'd have to have multiple different pipelines that come together to get to an end result. We've consolidated that into a single process because we actually have these special cells we design that actually run key parts, portions of that process in cell, versus needing to have it be a .

>> So you collapsed the workflow, big time.

Jonathan Brennan-Badal

>> Exactly, collapse it and automate it.

>> All right. I can see people banging on your door hard for this. Who's using it? You don't have to name names, but you can name names, it'd be great, but show me-

Jonathan Brennan-Badal

>> Totally, yeah.

>> I mean, what are they doing?

Jonathan Brennan-Badal

>> Yeah, and so if you look at our platform broadly, honestly, everyone's using it. And so over 90% of the top 100 biotech and pharma companies worldwide are our customers. 95% of the top 100 universities are our customers. And so, every organization from an academic to a major pharma finds value.

>> Sales are good, sales are good on your end on Opentrons.

Jonathan Brennan-Badal

>> Absolutely.

>> So, what's the pitch? It must be an easy pitch. Take me through the pitch, because their pain points are multi-fold, workflow time, bad outcomes.

Jonathan Brennan-Badal

>> Absolutely.

>> What's the pitch?

Jonathan Brennan-Badal

>> The pitch, it's super simple. Hey, I've got a manual process that we're running, it's painful, we want to do a lot more. Fine, I got complete turnkey solution. You can buy our robot, it comes with our assay and our software, and you can just drop that in and run it. And that'll save you money, save you time, and if you want to do 10 times as much, it's simple as just flipping a switch.

>> Yeah, Jon, not to date myself, but back in the computer industry days, the term God Box was used to describe the big server, the Sun Microsystems, monster machines. High performance computing has been like, it's like inch by inch, it's better, but they're still slow. Now you've got super computing which are not just servers, they're like clustered systems, powering all these. So now you have these devices and proprietary stuff like what you're doing to bring it all together. That is such a mind-blowing feature for people, sometimes they're like, they got to be really understanding the business side. In your case, the business benefit is so obvious. Time to market, cost, quality, you check all the boxes. Okay, so what's the coolest thing you're working on now because now you've got good beachhead, you have all these customers, what's coming out of it? Because in all this AI conversations that I've had with leaders is, they knocked down a use case, they're in market, good success. And then what happens normally is, whoa, holy moly, we got other cool stuff coming up as a result of the innovation. Can you share some cool things emerging out of this?

Jonathan Brennan-Badal

>> Absolutely, and so one major area for us is around cell culture automation. And so not just automating genomics assays and things like that, but really standardizing and bringing down the fundamental cost to grow cells. It seems kind of like a silly thing, but actually really important. So when you look at say, cell therapies that cost hundreds and hundreds of thousands of dollars. Well, big reason for that is, is because you have a small lab that will spend months basically growing a single dose. And so it's not surprising that it literally costs $250,000 to grow a dose. If you can through our technology, take two zeros off of that, you can make some really powerful therapeutics, far more broadly accessible.

>> It's interesting, the old data modeling was pretty much because of the manual process, I'm sure, and all of the collection wrangling involved, most of the therapeutics was kind of more general purpose. Corner cases weren't discovered because they couldn't provision or stand up and an environment, and now you can.

Jonathan Brennan-Badal

>> Absolutely, and so our systems are super easy to deploy. You can literally take them out of the box and have them up and running in an hour or two. I'll give you kind of an example. So, it was in the middle of, early in the COVID pandemic, Quest and LabCorp were kind of running a little bit far behind. City of New York asked us, "Hey, can you stand up a lab and take on a bunch of testing?" We said, "No problem." So we set up 40 of our robots in our lab at our offices, and we did the majority of all COVID tests in New York City, testing over 15 million people, and that's just using 40 robots and about 1,500 square feet of space.

>> That's awesome.

Jonathan Brennan-Badal

>> Right? And we got that lab stood up in literally a couple of days. Right? And so it's-

>> Yeah. I mean, that's impact....

Jonathan Brennan-Badal

>> really powerful of how you can, yeah.

>> That's real impact. Now let's get into the robotic side of it, because I love the whole AI world. Get down in the weeds with NVIDIA's pumping robots all the time. Robotics is here, everyone sees it coming. What are some of the robotics integrations? Take me through how the robots work on your side.

Jonathan Brennan-Badal

>> Absolutely, yeah. It's really kind simple. You have a box about yay size, about two feet by two feet by two feet. And you put your samples in the robot, you put in some of our other hardware modules that are things that do things to those set of samples, and you let it run and you come back after a period of time and that lab work is done.

>> So it's all-

Jonathan Brennan-Badal

>> It's really that simple, and it's a general purpose device that can run a very wide range of-

>> What's the back end look like for that, software wise? Is there a lot of intellectual property going into that? How do you guys feed the robots?

Jonathan Brennan-Badal

>> Yeah, absolutely. And so, there's a lot of novelty both at the hardware and software level, and particularly the combination, because one of the exciting aspects is we've collapsed the cost of these types of systems. So typically to have an automated lab, you're spending hundreds of thousands of dollars to many millions of dollars, and the starting point of our robots are under $20,000. Right? And so it makes it fundamentally more accessible and allows you to generate fundamentally more kind of data. That's made possible through the combination of very smart hardware and software design. To give just one kind of specific example. Hey, these other robots are far larger than ours and they're made that because so they can be, they're over engineered to be perfectly square so you can guarantee positional accuracy. But instead by having smart sensors, smart software that can constantly kind of be checking recalibrating, etc, etc, you can get comparable or better performance with hardware that's literally 20x cheaper. And you can offload that onto the software side to, yeah.

>> So you optimize for the integration of hardware software versus the other way, which is optimized for the device, make sure it's fully software enabled, but then if something changes-

Jonathan Brennan-Badal

>> Exactly....

>> there's no real headroom involved more beyond what their specific purpose was.

Jonathan Brennan-Badal

>> Exactly, and so there's a lot of deep thinking on the right software approaches.

>> On the city events going on, a lot of investors are out there. What's the business for you guys now? Give us an update on where you guys are at. Size, funding, business is good?

Jonathan Brennan-Badal

>> Yeah, absolutely, business has been great. We have raised about $200 million over all time. We've deployed over 10,000 robots worldwide, making us the largest lab robotics player in the spaces that we compete. We are growing rapidly. We basically doubled revenue year over year. And so and now as there's been so much progress on the AI front, which we've benefited from, one in terms of using natural language to program our robots, or two, being able to use all of these data sets to get greater value out of it.

>> You only get bigger because you have beachhead, you're in the workflows.

Jonathan Brennan-Badal

>> Exactly.

>> You're the factory-

Jonathan Brennan-Badal

>> Exactly....

>> for lab testing and development.

Jonathan Brennan-Badal

>> Exactly.

>> So once you have that, then your switching costs go high as you get more intelligent because the competition would have to one, beat the footprint out. So the switching costs are probably high.

Jonathan Brennan-Badal

>> And we built this massive ecosystem around us. Because we're open source, all of particularly the academic customers that adopt our platform are building literally hundreds and thousands of new applications that are all validated and unique to our platform.

>> Yeah, I think this is a huge point. I want to get into that, because I think what you were laying out from a business architecture standpoint, venture architecture, is that it's classic competitive strategy. You got the beachhead now, now I'm distracting away a little bit here, but you got the beachhead. The product market fit, got agility and versatility in the product, check, it checks the boxes. Simple, outcome driven, high quality product, results, right? That's a winning hand right there. And then now you've got the ecosystem of open source because you're in an area, there's a lot of domain experts and it's a community. Talk about the role of open source in your world, and why that feeds into the benefits of having all that beachhead.

Jonathan Brennan-Badal

>> Yeah, so I'll give you an example around actually chemical synthesis, which is an area of interest to you. And so, we're more of a life science-centric company, we're not experts in chemical synthesis. That's fine. Our robots are, I think so disruptive that they're actually finding uses within the chemistry field and used to automate chemical synthesis processes. And so, a lab out of Carnegie Mellon that has been using our robots, built a LLM-based agent that develops new synthesis patterns. And it's integrated closed loop with our platform, where it generates a hypothesis on how to do a chemical synthesis, and then it runs in our robot, it iterates to find an optimal pathway. That is a great example, and that was all developed and contributed open source.

>> And as a CEO, you're like, okay, for me to do that, I'd have to get more funding, staff up some people. Not quite. You've essentially crowdsourced intelligence from other people. You've enabled that with the device because you've got the software, hardware piece. So all they do is plug their LLM to the platform. How do they integrate in? Because this is a totally exponential growth.

Jonathan Brennan-Badal

>> Oh, yeah, absolutely right. And with our OpenTron's AI, it starts changing the game, right? So normally you'd have to do some deep API to API kind of integration, which takes time and effort. Now it's just our OpenTron's AI agent talking to their AI agent, and it can talk in basically natural language because it converts it on our end, converts it .

>> Have my agent talk to your agent. You know that old joke? Hey, let's have our agents talk to each other. Right? That's actually happening.

Jonathan Brennan-Badal

>> Exactly, that is happening.

>> Today.

Jonathan Brennan-Badal

>> Happening now and happening, there's dozens of startups and even more academic labs that are just building AI agents on top of our platform.

>> All right, I want you to just take a minute because I think this is so important because there's a lot of hype in the agentic infrastructure market and it's very complicated, it's very nuanced. Doing a large scale enterprise, there's all kinds of data issues. But there are all people think, oh, agents is a bunch of BS. Talk about why it's not BS, that there's real agents happening today? Share that perspective and then, what happens next? Because I think people are trying to connect two big dots that are too far away in the preferred future but if you look at the dots to connect, agents are here today and they're moving fast along. Take us through your vision of how agentic is working today, agents, and they are.

Jonathan Brennan-Badal

>> Absolutely, and so I'll give you real live examples.

>> Yeah, give us... yeah.

Jonathan Brennan-Badal

>> And this was published in Nature Magazine. Right? And so a lab that might have some very deep technical specialized knowledge has built up some specialized machine learning algorithms, etc, etc, to solve some hard kind of problem. They've wrapped that up in an LLM that you can now converse with in natural language and you can ask it to output. In this chemical synthesis example, you ask that agent of like, "Cool, tell me how to synthesize something," and it'll figure it out using its specialized algorithms that they've developed combined with general AI, and it will output how to do that in natural language. And now, since our system can understand natural language, it takes that natural language and converts it into an actual process that the robot runs.

>> Yeah, sou speak robot.

Jonathan Brennan-Badal

>> Exactly, right.

>> And they speak synthesis.

Jonathan Brennan-Badal

>> Exactly. And now you don't even have to, and now it's an infinitely system.

>> You are a connector for a collective brain. Research could be from an academic, it could be practitioners, whatever. They now have an on-ramp and to the robot interface to apply their knowledge to your hardware.

Jonathan Brennan-Badal

>> Exactly. And now because of all the progress in AI, the progress of the robotics, we've just collapsed the cost of doing everything and the effort it takes. So that a small academic lab, a startup can literally build an agent and easily integrate it with us. Not a big process.

>> Yeah. Jon, I'm fascinated by, first of all, congratulations. You've got a great architecture there on the business side, as well as your growth with the community. Open source obviously is now kind of maturing another level. I mean some would argue three, four generations, maybe it's five, who knows? Pick a number. It's not three, it's beyond that. So a lot of people, there's no books written about how to compete in an era of AI. From a business, I don't mean competitive strategy, I'm talking about how to build a successful business model with open source. Now you're in real world, it's not open source software, you're using open source to power something that is not related to open source software, but you're using open source software to make a business work.

Jonathan Brennan-Badal

>> Totally.

>> So, I think this is kind of like a cutting edge example of a business model innovation, combining physical asset that's on a workflow with AI and software technology you're using, and then connecting to an ecosystem of AI providers into here. I mean, this is new. I mean, this is all kind of breaking out. This looks like the future template.

Jonathan Brennan-Badal

>> I really think so, right? Because this kind of, the talk I gave at Davos earlier this year is, look, you shouldn't think of open source as something scary that will have you lose all your intellectual property. You should be thinking about, what allows me to develop and innovate as fast as possible? That is all that matters. Customers don't care if it's open source or not open source. What they care is, can you provide me a solution that works and is it better than everything else? And if open source is the means to do that, do that. And I would argue strongly that in our case in the field that we do is, is that it's been-

>> There's no arguments, there's total agreement because it takes just... Turn on cognition in your brain, you say no-brainer, because what you've done is you've simplified access to device that the other party wouldn't have to build. You've accessed intellectual capital from an individual or group of individuals into a domain expert into your system. That is the fast path, because what's the alternative? Then buy hardware and build hardware and do all that work? You build .

Jonathan Brennan-Badal

>> Exactly, exactly. So it's allowed us to move way faster and gain far more scale than anywhere else. Since now we're in this instance where we have a much larger ecosystem than any other company in the space. Our products are actually more affordable than Chinese made solutions that rhyme a bit with what we're doing, because we have the scale, we have the easiest user experience and all these things to start compounding on each other.

>> The other thing about your business that I like just listening and learning, is that the outcome that you're doing is very high end. Many dimensions that you have to check the boxes on. I mentioned quality.

Jonathan Brennan-Badal

>> Absolutely.

>> I mean, come on. Quality. It can't be bad. You can't do-

Jonathan Brennan-Badal

>> our robots are used, an FDA approved processes. We had to go, right?

>> Yeah.

Jonathan Brennan-Badal

>> .

>> So one, make the product has to be... it can't be like software, where oops, framework update, a couple people died. No, it's got to be high quality out of the gate, that's obviously the top line. But the efficiency and speed is critical because now you have what's going to come next. I mean, COVID was a great example. Great use case deploying very rapidly. I mean, that's agility.

Jonathan Brennan-Badal

>> Absolutely.

>> And quality. So I think you're on something really big here. I will have to ask you, because since you're in such a cool position with what you do and innovative, what's the coolest thing you're working on right now, in your mind?

Jonathan Brennan-Badal

>> Totally, so-

>> Well, company and something that you think is super cool.

Jonathan Brennan-Badal

>> Yeah, totally. And so, we're pushing the limit to enable new robotic capabilities, etc, etc. And so when you start kind adding up a lot of these things, we're actually one of the first kind of pioneers of genome scale cell engineering. And so when you think of cell engineering, it's mostly about like, oh, manipulating individual genes. What about if you could just write an entire genome, design and write an entire genome from scratch? And as you've collapsed the cost of the robotics to do these types of activities as you have AI that helps you in these types of designing, that type of activity is now possible. And actually, our company and our team has actually built the first totally synthetic eukaryotic cell.

>> Yeah, wow.

Jonathan Brennan-Badal

>> And have fired that. And so-

>> That must be cool, in terms of attracting talent.

Jonathan Brennan-Badal

>> Oh, absolutely.

>> Because you must have everyone wanting to work there.

Jonathan Brennan-Badal

>> Oh, absolutely. And so there's, we've been able to get some really, really, really amazing scientists that have started some really amazing companies to be some powerful advisors, because we've started to leverage our strength in robotics and start enabling new capabilities in other areas. And so we're able to, that's how we have novel assays is that we actually can think about wholesale designing entire genomes to enable new capabilities.

>> Yeah, and the other thing that kind of gets me under my claw when people talk about jobs being lost, because also the human intelligence, you're actually showing here with that example of the LLM that's a connected model, it's open source. But you just reduce the workflow. Some will say, "Oh, jobs are lost." Well, they're slow anyway, so, but they move-

Jonathan Brennan-Badal

>> Yeah, it's the end scientists that are choosing to adopt our tools because we made it so affordable, the end users can actually buy them within their discretionary budgets.

>> Well, they're getting more creative.

Jonathan Brennan-Badal

>> Exactly.

>> So their human intelligence is probably will allow them to get paid more probably, or-

Jonathan Brennan-Badal

>> Exactly, because they'd rather spend their time thinking up of new scientific ideas that could be tested and let the robot do all of that work.

>> Yeah, mundane, heavy lifting.

Jonathan Brennan-Badal

>> Exactly.

>> Some say grunt work, toil, undifferentiated heavy lifting. I mean, it's boring. Manual process ever prone too.

Jonathan Brennan-Badal

>> Oh, absolutely. Just a simple thing of sequencing DNA. You forget that there's 10 hours of sample prep where there's literally thousands of steps that you're doing. There's better things to do.

>> Yeah, I love the area around... I think life science is going to be a massive tsunami of innovation, mainly because we have super computing capabilities and the nexus of software and hardware. Hardware being either chips or devices, are coming. You mentioned you were at Davos. What was your talks there? What was the vibe at Davos relative to this area? What were some of the things that were talked about there? Could you share some stories from Davos?

Jonathan Brennan-Badal

>> Yeah, absolutely. And so actually the talk that I gave at Davos was on open source and open source science, and if it's kind of under threat. And so-

>> That was the title of the talk, under threat, or?

Jonathan Brennan-Badal

>> Yeah. Is Open Source-

>> Oh, so it was a question.

Jonathan Brennan-Badal

>> Yeah.

>> Okay. That was the prompt. Is open source-

Jonathan Brennan-Badal

>> That was the prompt I was given and told to kind of-

>> Of course it's not. Yeah, but what was your answer? What was the main thesis there?

Jonathan Brennan-Badal

>> Yeah, I mean, the main kind of point I was making to the audience, this is the two big pharma company, CEOs, etc, etc. It's like, guys, you need to be embracing these technologies because they will enable you to be more innovative. And that fundamentally is what matters at the end of the day. No one cares if you have exclusive IP rights about something that is only second best. You need to be thinking about how you incorporate open source technologies into your stack and to your approach, and that kind of mindset to enable you to move faster because if you don't, someone else is going to move faster, and we're seeing that. Yeah.

>> I think I keep bringing it up because maybe I've just been crying to the moon on this one, but howling to the moon, as they say. But if you look at proprietary software, open one, and what's happening with AI now open is now infiltrated the open source concept, not just software, that's definitely true, that's already been proven. But open ecosystems are infiltrating every single vertical, every single industry. And it's not so much, hey, the classic open source playbook, it's just the concept of open source. Data, talent, these are the things that are emerging. You agree?

Jonathan Brennan-Badal

>> Absolutely.

>> All right, cool. We got one minute left. Put a plug in for the company. What are you guys looking to do? Obviously, you're kicking butt and taking names, which is great, congratulations.

Jonathan Brennan-Badal

>> Yeah, and so if you're a life scientist and you're not using, Opentrons, should check us out.

>> All right.

Jonathan Brennan-Badal

>> Because we can enable you to do things better, faster, cheaper.

>> Jon, thanks for coming on theCUBE, appreciate it. From our new studio here on Wall Street, life sciences, healthcare, all these areas are going to be, it's a rising tide because of AI, but also more importantly, the engineering of the systems is what it's all about. If you look at the successes, it's the architecture of whether it's a venture or a product. They're being re-looked at and the benefits of open connected ecosystems, as many examples here, it's changing the world. And of course, theCUBE is open. We're open source, we're open system, sharing the data with you in real time. I'm John Furrier, your host of theCUBE. Thanks for watching.