Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,
Speaker 1: and welcome to tech Stuff. I'm your host Jonathan Strickland.
Speaker 1: I'm an executive producer with iHeart Podcasts and how the
Speaker 1: tech are you? You know. A couple of years ago,
Speaker 1: it seemed like AI had just exploded into the mainstream
Speaker 1: out of nowhere. Now, in reality, thousands of people have
Speaker 1: been working for decades to bring various AI implementations to
Speaker 1: a point where they could be deployed in the real world,
Speaker 1: not just you know, R and D projects. I think
Speaker 1: a lot of us are hyper aware of generative AI
Speaker 1: in particular. That's the kind of application that's easy for
Speaker 1: the average person to have experienced and perceived, you know,
Speaker 1: first hand. But there's a lot more going on in
Speaker 1: AI than just chat by and image generators, that kind
Speaker 1: of thing. There's widespread agreement that AI is definitely going
Speaker 1: to have an enormous impact on pretty much everything. There's
Speaker 1: less agreement about how this transformation is going to manifest
Speaker 1: or in what time frame it's going to happen. We've
Speaker 1: got a lot of companies that have made some fairly
Speaker 1: aggressive moves into the space, either developing AI or trying
Speaker 1: to implement AI solutions, and it hasn't always worked out.
Speaker 1: In the meanwhile, you've got this incredible environment in which entrepreneurs,
Speaker 1: computer scientists, venture capital investors, and more are all trying
Speaker 1: to leverage the moment. Like that whole idea of opportunity knocking.
Speaker 1: You've got to seize on that opportunity because who knows
Speaker 1: when it's going to come around again, Even if they're
Speaker 1: not actually prepared to execute upon that opportunity. Sometimes leveraging
Speaker 1: opportunity just doesn't go the way you want it to. Now,
Speaker 1: I've already published tech Stuff episodes in the past about
Speaker 1: failed tech startups. Some of those startups reached really high
Speaker 1: levels of valuation, like topping a billion dollars, which catapults
Speaker 1: them into the so called unicorn status. A unicorn is
Speaker 1: a startup doesn't have to be tech. It's a startup
Speaker 1: business that hits evaluation of a billion dollars at some
Speaker 1: point or another. But even with that kind of high valuation,
Speaker 1: and even with the initial excitement and support of investors
Speaker 1: who have really deep pockets, startups sometimes collapse for one
Speaker 1: reason or another. So today I thought we should talk
Speaker 1: about a couple of digital health AI startups that have
Speaker 1: followed this particular path. While AI enthusiasm has built to
Speaker 1: what I would call a frenzy, in more recent months
Speaker 1: that enthusiasm has waned somewhat. Investors have kind of backed
Speaker 1: off a little bit from AI. There have been a
Speaker 1: lot of articles that have been published that say any
Speaker 1: real gains from AI are probably years down the road,
Speaker 1: at least for most implementations. This is not necessarily universal
Speaker 1: for all AI, which is an important thing to remember.
Speaker 1: Not all AI is the same, but for many implementations
Speaker 1: it's just too early to think of AI making an
Speaker 1: enormous impact on business objectives. And so again, investments have
Speaker 1: started to taper off quite a bit. People are being
Speaker 1: more particular with their investments for lots of reasons, but
Speaker 1: a big one of those is the perception that AI
Speaker 1: is not really ready for prime time to totally transform
Speaker 1: everything right now. So a lot of AI companies or
Speaker 1: companies that have, you know, kind of leveraged AI to
Speaker 1: be their sales pitch have kind of found themselves in
Speaker 1: dire straits. The condition of dire straits, not the band.
Speaker 1: Dire straits, not the sultans of swing. Now, before I
Speaker 1: get to specifics, let's just establish some general factors, all right.
Speaker 1: So artificial intelligence is an expensive discipline. It is a
Speaker 1: resource hungry computer application. So you have to either own
Speaker 1: or have access to really powerful data centers. For any
Speaker 1: AI application that's going beyond just a proof of concept.
Speaker 1: If you plan on launching something that is meant to
Speaker 1: be a product or service that is in part or
Speaker 1: wholly dependent upon AI, you have to have access to
Speaker 1: that compute power. Now, on top of that, AI applications
Speaker 1: typically require specific types of powerful parallel processing capabilities, which
Speaker 1: means that you need a particular kind of computer chip.
Speaker 1: You can't just get any computer chip, even a really
Speaker 1: fast one. You need one that's really good at handling
Speaker 1: parallel processing. So, like in the early days of AI,
Speaker 1: GPUs were a really big part of AI processing because
Speaker 1: GPUs graphics processing units typically are designed to be parallel processors.
Speaker 1: They have multiple cores, and they can do multi threading
Speaker 1: and work on a lot of different problems all simultaneously.
Speaker 1: AI needs that capability, or at least a lot of
Speaker 1: machine learning processes require that kind of computational power. So
Speaker 1: the chips best suited to do that are not always
Speaker 1: in plentiful supply. So that means we've got this big
Speaker 1: pool of AI companies. Some of them are part of
Speaker 1: much larger organizations like Microsoft or Google, but all of
Speaker 1: them are competing for a limited supply of processors that
Speaker 1: are suitable for handling AI computational loads. Not everyone is
Speaker 1: going to come out of winner in that kind of competition.
Speaker 1: You know, big companies obviously have a huge advantage over
Speaker 1: smaller startups that are dependent upon rounds of fundraising from investors.
Speaker 1: They're going to venture capitalists to get a influx of
Speaker 1: cash in order to be able to do business. Meanwhile,
Speaker 1: you have these monoliths like Microsoft and Google that have
Speaker 1: decades of wealth that have been generated and they lean
Speaker 1: on that in order to get an advantage in the market.
Speaker 1: So that's one thing. Another is that scaling up is
Speaker 1: particularly challenging for AI companies. This is hard for any startup. Right,
Speaker 1: a startup can come up with a brilliant idea and
Speaker 1: have an idea that truly has a good place in
Speaker 1: the market. Once you get to a level of scale
Speaker 1: where you can make this a revenue generating business, but
Speaker 1: getting there is hard. Right, if it's manufacturing, then you
Speaker 1: have to figure out, well, how are we going to
Speaker 1: afford to manufac facture in the bulk we need in
Speaker 1: order to make this a viable business for services, How
Speaker 1: do we make sure that our business can provide the
Speaker 1: services to the customer base we're going to need in
Speaker 1: order to have a viable business. These are non trivial challenges.
Speaker 1: You have to figure out how do you actually meet
Speaker 1: the demand that you're hoping to create. Now, first, there
Speaker 1: has to be a demand there in the first place.
Speaker 1: And if there's no existing demand, you have to create
Speaker 1: that demand. Then you have to be able to deliver
Speaker 1: value for that demand. These are really hard problems, so
Speaker 1: a lot of startups do fail, like whether they're in
Speaker 1: the tech sector or not, and for AI companies it
Speaker 1: is particularly difficult. Scientists might develop a truly intriguing use
Speaker 1: for artificial intelligence. The work's great on a small scale,
Speaker 1: like yeah, they can prove it works for a small
Speaker 1: test group or maybe a region or a very specific industry.
Speaker 1: But then to grow that so that you can meet
Speaker 1: the needs of customers around the world that could require
Speaker 1: way more resources than you can actually afford even as
Speaker 1: a unicorn. So AI startups can end up burning through
Speaker 1: cash really quickly, not through terrible mismanagement, though of course
Speaker 1: that can happen too, but just because HEYI is expensive.
Speaker 1: So I wanted to clear that up at the start
Speaker 1: because I don't want to get the impression that the
Speaker 1: folks that are behind these businesses necessarily did something wrong.
Speaker 1: Although in one case we'll see that there is at
Speaker 1: least one news outlet that very much feels like the
Speaker 1: founder of a company did many things wrong. I don't
Speaker 1: want to say that they were necessarily bad at managing money.
Speaker 1: That could be a factor as well, but I'm trying
Speaker 1: to separate this from the dot com bubble of the
Speaker 1: late nineties. So with the dot com bubble, you had
Speaker 1: all these startups that got enormous investments in cash, and
Speaker 1: you know, some of them went public on the stock
Speaker 1: market and their stocks inflated to ridiculous levels. And you
Speaker 1: had these companies that didn't have fully baked business plans
Speaker 1: in place. They were absolutely swimming in cash, and a
Speaker 1: lot of times people were making extravagant purchases like crazy
Speaker 1: office things like like a full bar or whatever, without
Speaker 1: actually being able to put those assets to work to
Speaker 1: create a business that could stand on its own. And
Speaker 1: ultimately the bubble burst and the entire industry collapsed. You know,
Speaker 1: some companies survived, but a lot of them didn't. Well,
Speaker 1: I want to draw a line between those and what
Speaker 1: might be an AI tech bubble. I think it's fair
Speaker 1: to call it an AI tech bubble because one almost
Speaker 1: universal issue for all the AI startups is this challenge
Speaker 1: that artificial intelligence is inherently expensive. They could also fall
Speaker 1: victim to the same problems that we solve with dot
Speaker 1: com businesses. That's still a possibility. I'm not saying that
Speaker 1: AI companies are somehow immune to human frailties of going
Speaker 1: overboard and like everybody gets a new car or whatever
Speaker 1: it might be, but that the very nature of AI
Speaker 1: itself becomes a massive risk as far as seeing a
Speaker 1: return on investment. So with all that set, we're ready
Speaker 1: to start diving into discussions of a pair of different
Speaker 1: digital health companies that were largely centered around the idea
Speaker 1: of artificial intelligence revolutionizing the way we do certain things
Speaker 1: in healthcare. Whether or not artificial intelligence was actually playing
Speaker 1: a part in that, that's more of an open question.
Speaker 1: We're going to get into that in just a moment.
Speaker 1: Before we do that, let's take a quick break to
Speaker 1: thank our sponsors. Okay, we're back. Let's talk about our
Speaker 1: first digital health company and what happened. And in these cases,
Speaker 1: I think it's safe to say that the AI component
Speaker 1: was really just one contributing factor to how these two
Speaker 1: health company startups failed over time. I think it was
Speaker 1: a major contributing factor, but just one of multiple factors.
Speaker 1: But another is that there was an understandable but arguably
Speaker 1: foolish rush of cash influence in the health space following
Speaker 1: the twenty twenty COVID outbreak. Now that rush of cash
Speaker 1: was understandable because obviously the pandemic had an enormous impact
Speaker 1: on people all around the world. There were tons of regions,
Speaker 1: entire countries that were operating under lockdown conditions, sometimes for
Speaker 1: so several months at a stretch. Now, obviously that would
Speaker 1: change how we do pretty much everything from how we
Speaker 1: work to how students were attending lessons, to how you
Speaker 1: actually got to see a physician if you needed one,
Speaker 1: and so out of necessity, health companies new and old
Speaker 1: attempted to adapt to this new reality. Now, it could
Speaker 1: be really hard for large established companies to adapt to
Speaker 1: rapidly changing conditions. Being nimble isn't exactly a common trait
Speaker 1: for legacy organizations. That opened up opportunities for younger startups
Speaker 1: to innovate in the space and to attempt to serve
Speaker 1: customers in ways that larger organizations simply couldn't replicate. And
Speaker 1: one of those companies was called Babylon. Now, Babylon was
Speaker 1: founded years before the pandemic. It was founded way back
Speaker 1: in twenty thirteen. Technically it's one year year younger than
Speaker 1: the other digital health company we'll talk about in a
Speaker 1: little bit. Babylon launched in the United Kingdom. Ultimately it
Speaker 1: would extend services to other parts of the world, primarily
Speaker 1: in Asia, but also the United States and a couple
Speaker 1: of places in Africa. It was a subscription based healthcare
Speaker 1: services company, and initially it was one that had a
Speaker 1: fairly simple approach. Customers or patients in other words, would
Speaker 1: communicate with healthcare professionals via text messages and video conferences.
Speaker 1: So it was a telehealth solutions company, which again not
Speaker 1: that groundbreaking, right, there were other telehealth solutions out there,
Speaker 1: and at this stage there was no real AI component.
Speaker 1: This was all let's put patients in touch with doctors
Speaker 1: and do it in a way where the patient doesn't
Speaker 1: necessarily have to take time out of his or her
Speaker 1: or their day to go and meet with physician. They
Speaker 1: could do it through this app. So you might say, well,
Speaker 1: where is the AI component if this episode is about
Speaker 1: AI health startups. Well, that took the form of a
Speaker 1: chat bot that was developed later on in Babylon. It
Speaker 1: was an idea that I think was present from the beginning,
Speaker 1: Like this was a goal early on was to develop
Speaker 1: a chat bot that would be able to interact with patients,
Speaker 1: and the chatbot would be able to answer questions, and
Speaker 1: ultimately Babylon claimed it would be able to do things
Speaker 1: like diagnose patients. So not just like answer questions about
Speaker 1: physician availability or simple questions that might lead to a
Speaker 1: way to alleviate symptoms that are acutely bothering a patient,
Speaker 1: but actually diagnosing the underlying cause of those symptoms. That
Speaker 1: is a huge claim, I mean as a remarkable claim,
Speaker 1: and it requires remarkable evidence to support it. When you're
Speaker 1: talking about healthcare, there's a high bar you need to meet,
Speaker 1: a high bar of confidence. If you do not meet that,
Speaker 1: then that means you probably should not not Probably you
Speaker 1: should not offer these services to patients who were talking
Speaker 1: literally matters of life and death. So understandably, a lot
Speaker 1: of critics were raising concerns about how reliable this chatbot
Speaker 1: actually was and whether or not it was ethical to
Speaker 1: even suggest that a chatbot could accurately diagnose someone's ailments
Speaker 1: through interacting with that patient. And again, all of this
Speaker 1: was happening before the pandemic and well before Open Ai
Speaker 1: really opened the floodgates with chat GPT in twenty twenty two,
Speaker 1: So a very aggressive approach toward positioning AI as a
Speaker 1: solution to a complicated problem. Now, in the past, I've
Speaker 1: talked about how AI generated answers can sometimes be incomplete
Speaker 1: or unreliable. That's you using today's AI chatbots, which are
Speaker 1: miles ahead of the stuff that was being developed in
Speaker 1: the twenty tens. So imagine putting your life in the
Speaker 1: virtual hands of a fallible chatbot back in twenty eighteen. Wolf. However,
Speaker 1: according to a great piece that appeared in Wired, it
Speaker 1: was written by Grace Brown. It's titled The Fall of
Speaker 1: Babylon is a Warning for AI Unicorns. Well, according to
Speaker 1: that piece, the AI bit of Babylon might have been
Speaker 1: a stretch in the first place, it might have been
Speaker 1: disingenuous to reference this as artificial intelligence. So Brown cites
Speaker 1: a consulting doctor by the name of Hugh Harvey, who
Speaker 1: at one time worked for Babylon. Now. According to Harvey,
Speaker 1: the AI decision tree that it would follow when interacting
Speaker 1: with patients was essentially an Excel spreadsheet that correlated to
Speaker 1: different parts of the human body. So a patient using
Speaker 1: Babylon's app would indicate, you know, where their symptoms were
Speaker 1: affecting them, like oh, my leg is itching or something
Speaker 1: like that. The app would essentially hone in on possible
Speaker 1: diagnoses by eliminating all the stuff that wasn't a potential
Speaker 1: candidate for an explanation, which is not a very sophisticated
Speaker 1: method of determining what the underlying cause is. And as
Speaker 1: Harvey told Brown, quote, I was like, well, this isn't
Speaker 1: really artificial intelligence, end quote. But whether we should classify
Speaker 1: the inner workings of Babylon as AI or not AI
Speaker 1: was definitely part of the company's messaging to investors. So
Speaker 1: you could say, well, this isn't really artificial intelligence. This
Speaker 1: is a very simplistic decision tree. There's no artificial intelligence
Speaker 1: going on here. There's no decision making, but that's not
Speaker 1: how the company was marketing their capabilities to potential customers.
Speaker 1: Babylon was saying, we use artificial intelligence to help treat
Speaker 1: patients to die noose and treat them. So, whether you
Speaker 1: want to argue that AI was happening or not, the
Speaker 1: company certainly was claiming that to be the case. And
Speaker 1: Babylon initially did pretty well when it came to raising investments.
Speaker 1: So by twenty nineteen, before the pandemic, Babylon had raised
Speaker 1: more than half a billion dollars in funding over the years.
Speaker 1: So remember it was founded in twenty thirteen. By twenty nineteen,
Speaker 1: more than half a billion dollars in various investment rounds.
Speaker 1: In twenty twenty one, the company played the risky maneuver
Speaker 1: of going public through the use of a special purpose
Speaker 1: Acquisition company or SPAC SPAC aka a blank check company.
Speaker 1: Now I have talked about spacks before, but let's have
Speaker 1: a quick refresher. Typically, when a private company is preparing
Speaker 1: to transition into a publicly traded company where the average
Speaker 1: citizen can buy stock in the company, it first has
Speaker 1: to go through an extensive set of steps in order
Speaker 1: to get to the IPO or initial public offering. This
Speaker 1: involves a ton of scrutiny from regulators. Here in the
Speaker 1: United States, it's the Securities and Exchange Commission, or SEC.
Speaker 1: As Kate Ashford wrote in Forbes Advisor quote, going public
Speaker 1: is a challenging, time consuming process that's difficult for most
Speaker 1: companies to navigate alone. A private company planning an IPO
Speaker 1: needs not only to prepare itself for an exponential increase
Speaker 1: in public scrutiny, but it also has to file a
Speaker 1: ton of paperwork and financial disclosures to meet the requirements
Speaker 1: of the Securities and Exchange Commission SEC, which oversees public
Speaker 1: companies end quote. So if a startup is looking to
Speaker 1: get access to a ton of cash through going public
Speaker 1: and it's a bit strapped for time, an alternative to
Speaker 1: the IPO is the SPAC. So with a SPACK you
Speaker 1: have a holding company. So this company doesn't really make
Speaker 1: or do anything. It's kind of like an empty envelope.
Speaker 1: So the one thing it can do is it can
Speaker 1: go through all the regulatory processes required to go public
Speaker 1: and become a publicly traded company. So now you've got
Speaker 1: a publicly traded company that doesn't actually do anything else.
Speaker 1: So using this empty shell of a company, you then
Speaker 1: can acquire a private company that's just itching to go public,
Speaker 1: but it doesn't have the time or the ability to
Speaker 1: do this through the IPO method, or if they did
Speaker 1: do an IPO, the value of stock that would be
Speaker 1: determined through that process would be much lower than what
Speaker 1: they actually want it to be. So your SPAC, your
Speaker 1: SPAC acquires this private startup. Now through the transitive property
Speaker 1: of ownership, that startup is a publicly traded company, or
Speaker 1: at least it's part of a publicly traded company, And
Speaker 1: it's like the startup got a chance to skip all
Speaker 1: that boring paperwork and get straight to the part where
Speaker 1: people throw money at it. However, if it turns out
Speaker 1: the startup doesn't have the ability to succeed in the
Speaker 1: public marketplace, while all of this can then come crashing down.
Speaker 1: Shareholders can lose confidence in the company, They can sell
Speaker 1: off their shares, share prices can fall, that big old
Speaker 1: pile of money can start to shrink, and it's almost
Speaker 1: like skipping all those steps that are intended to make
Speaker 1: sure that companies can make the transition from private to
Speaker 1: public in a sustainable way might be a bad idea.
Speaker 1: That's what happened with Babylon, at least to some extent.
Speaker 1: A SPAC called al Kourie Global Acquisition Corporation acquired Babylon
Speaker 1: in October twenty twenty one. The SPAC, in turn had
Speaker 1: the backing of palanteer Is, Peter Thiel's big data analytics company.
Speaker 1: It was one of many investors that were part of this.
Speaker 1: Babylon's valuation was estimated at four point two billion with
Speaker 1: a B dollars, and it is wild to think that
Speaker 1: just two years later Babylon would get sold off for
Speaker 1: parts as the value of the company had totally collapsed.
Speaker 1: And by collapsed, I mean that eighteen months after being
Speaker 1: listed in the New York Stock Exchange, the stock price
Speaker 1: was ninety nine percent lower than where it had started off.
Speaker 1: How did that happen? I'll explain more, but first let's
Speaker 1: take another quick break. Okay, we have Babylon, a startup
Speaker 1: from twenty thirteen that reaches incredible heights through this reverse
Speaker 1: merger process with a SPACK and is worth or value
Speaker 1: that I should say, four point two billion dollars. Well,
Speaker 1: according to Brown's Piece and Wired, Babylon was actually already
Speaker 1: in trouble. By the time it joined the stock exchange,
Speaker 1: the company was running through cash very quickly in an
Speaker 1: attempt to scale the business, to grow it beyond what
Speaker 1: it already was. Now. As I mentioned at the top,
Speaker 1: AI businesses in particular are really costly to scale, So
Speaker 1: unless you've got really deep pockets, like the pockets of
Speaker 1: one of the big five tech companies Microsoft, Google, Meta, Apple,
Speaker 1: or Amazon, well, you'll likely find challenges in making your
Speaker 1: money last long enough for you to scale properly and
Speaker 1: be self sufficient. Babylon was spending way more money than
Speaker 1: it was bringing in, so it was losing money year
Speaker 1: over year, and as a publicly traded company, Babylon had
Speaker 1: to share this information with the sec. You know, if
Speaker 1: you're a privately held company, you don't have to talk
Speaker 1: about how much money you lost. The public remains uninformed,
Speaker 1: But with publicly traded ones, that information gets filed and
Speaker 1: it becomes available to the public, so you can see
Speaker 1: how much money the company is losing year over year. Well, clearly,
Speaker 1: shareholders lost confidence. The stock price crashed, and just a
Speaker 1: couple of years after having going public with that SPAC transaction,
Speaker 1: Babylon went into administration. In the UK and bankruptcy in
Speaker 1: the US. So administration in the UK is kind of
Speaker 1: similar to bankruptcy here in the United States. They're not identical,
Speaker 1: but they are similar processes. It's meant to try and
Speaker 1: return as much value to investors as possible while a
Speaker 1: business effectively shuts down. So Brown's piece gives more details
Speaker 1: about what was actually going on within Babylon, but in general,
Speaker 1: it was a case of a company spending money it
Speaker 1: had not yet raised in the hopes of hitting that
Speaker 1: sweet spot and delivering upon the company's value proposition. The
Speaker 1: stories of Babylon sound kind of similar to what I
Speaker 1: heard about Aharranos Now Farrannose was that infamous high tech
Speaker 1: health company that absolutely imploded after an expose revealed that
Speaker 1: the company's flagship product, a device that was meant to
Speaker 1: analyze a tiny micro drop of blood and potentially run
Speaker 1: hundreds of different medical tests on It turned out that
Speaker 1: product just did not work as advertised, and in fact,
Speaker 1: it might not ever work at all, at least not
Speaker 1: to the extent that was being promised by the company.
Speaker 1: No matter how much effort was put into it, it
Speaker 1: was going to run up against some fundamental limitations that
Speaker 1: meant it just could not work the way it was envisioned,
Speaker 1: and that the whole company was essentially a house of
Speaker 1: cards built upon this belief that ultimately tech can do
Speaker 1: anything if you just work at it hard enough, and
Speaker 1: it turned out that just wasn't true. Well, it sounds
Speaker 1: like Babylon suffered a kind of similar fate. Now check
Speaker 1: out Grace Brown's article on Wired to read a more
Speaker 1: detailed story about that. But we need to move on
Speaker 1: at this point. So Babylon ultimately goes out of business,
Speaker 1: sells its various business divisions and assets off to other
Speaker 1: companies to return as much value to investors as possible,
Speaker 1: and goes by by Now we're going to talk about
Speaker 1: a different digital health company that had AI aspirations, this
Speaker 1: one called Olive, sometimes called Olive AI. So for this bit,
Speaker 1: I'm referencing a few different articles that I found particularly
Speaker 1: helpful while reading up on the company. One of those
Speaker 1: is an article by Emily Olsen in healthcare drive dot com.
Speaker 1: It was written back in November twenty twenty three. It
Speaker 1: is titled health AI startup Olive to shut down. So
Speaker 1: spoiler alert there, except you know, that's what this episode's
Speaker 1: all about. So maybe not so much a spoiler. I
Speaker 1: also used another article by Giles Bruce. This one was
Speaker 1: for Becker Hospital Review. It was titled the Rise and
Speaker 1: Fall of Olive AI, a timeline that gave some you know,
Speaker 1: simple little moments in time of what was going on
Speaker 1: within Olive. And there were others as well. There was
Speaker 1: an article by Free Press staff of Free Press Columbus
Speaker 1: is in Columbus, Ohio that was very useful and also
Speaker 1: not at all unbiased. Let's I'll talk about so like Babylon,
Speaker 1: Olive actually got its start well before the current AI craze,
Speaker 1: not to mention before the pandemic. It launched back in
Speaker 1: twenty twelve in Columbus, Ohio a guy named Sean Lane,
Speaker 1: whom the Columbus Free Press said, developed quote shadowy and
Speaker 1: shady AI software which promised to cut administrative costs for
Speaker 1: healthcare providers in quote, led Olive AI for a little
Speaker 1: more than a decade before the company totally collapsed. The
Speaker 1: Free Press has a lot of things to say about
Speaker 1: Sean Lane, and they are pretty darn critical. They pull
Speaker 1: no punches in their take. For example, that piece points
Speaker 1: out that Shawn Lane incorporated a new company on the
Speaker 1: very same day that olive Ai announced it was going
Speaker 1: on a business So they said, well, that doesn't sit
Speaker 1: well with us. Like your company that you led for
Speaker 1: more than a decade spectacularly fails, and on that same
Speaker 1: day that it shuts down, you announce or not announced,
Speaker 1: but you incorporate a new company. That seems kind of questionable.
Speaker 1: That's what the Free Press was saying. So if you
Speaker 1: want to read some serious shade directed at Lane and
Speaker 1: olive Ai, check out the article in Free Press Columbus.
Speaker 1: It's titled out of control venture capitalysts throw more millions
Speaker 1: at disgraced Columbus CEO. But again, note that there might
Speaker 1: be a teenc bit of bias in that reporting. I'm
Speaker 1: not saying it's misplaced bias, but it's there, all right.
Speaker 1: So oli Ai, let's talk about what the company's sales
Speaker 1: pitch was. So this was a B to B kind
Speaker 1: of company, meaning it would count other businesses as its customers.
Speaker 1: It's a business to business company. It didn't interface with
Speaker 1: private citizens or anything like that. And the company's main
Speaker 1: product was a software package that was meant to help
Speaker 1: healthcare companies automate certain processes such as keeping tabs on
Speaker 1: patients insurance coverage, making sure that you know their insurance
Speaker 1: is still active that thing, or processing authorization requests through
Speaker 1: an automated system. So essentially, the idea was to streamline
Speaker 1: the numerous and repetitive tasks that are involved in healthcare administration.
Speaker 1: And this was a pitch that a lot of investors
Speaker 1: loved because it suggested that healthcare companies would be able
Speaker 1: to significantly decrease their costs and increase their efficiency while
Speaker 1: passing savings on to customers. Oh no, wait, sorry, no,
Speaker 1: I forget that last part. I actually meant while generating
Speaker 1: massive profits that mean huge shareholder returns, customers the patients,
Speaker 1: they would still see the same costs because, after all,
Speaker 1: here in the United States, it's usually an insurance company
Speaker 1: that's actually paying up. No one cares if an insurance
Speaker 1: company has to pay the same amount for services that
Speaker 1: are actually costing less because the hospital or other healthcare
Speaker 1: service provider has found a more efficient way of doing business.
Speaker 1: They don't care if the insurance company is still paying
Speaker 1: the same amount, even if the services themselves technically cost less.
Speaker 1: Of course, insurance companies might care, and then therefore insurance
Speaker 1: customers are going to care because ultimately those providers are
Speaker 1: going to pass those costs down to the insurance customers
Speaker 1: and they're gonna the customers are going to see higher
Speaker 1: deductibles and higher premiums that kind of thing. But never
Speaker 1: mind all that, that doesn't matter to investors, right. So
Speaker 1: the earliest version of ol of debuted back in twenty seventeen,
Speaker 1: so this is like five years after the company has
Speaker 1: been founded, and the company enjoyed support from investors throughout
Speaker 1: its early years. But like Babylon and countless other digital
Speaker 1: health companies, it was the pandemic that would send the
Speaker 1: company's fortunes to the moon. That's when investors were just
Speaker 1: pouring huge amounts of money into these digital health companies.
Speaker 1: So in twenty twenty, all of Ai raised nearly a
Speaker 1: billion dollars in funding. That's just in one year, and
Speaker 1: this is after the company had been incorporated for nearly
Speaker 1: a decade. So in twenty twenty one, Olive acquired another
Speaker 1: AI focused healthcare company called Empiric Health, which itself was
Speaker 1: a spinoff from yet another healthcare company called inter Mountain
Speaker 1: Health out of Salt Lake City, Utah. Stuff gets really complicated,
Speaker 1: not just from a technical perspective, So empiric Health focused
Speaker 1: on clinical analytics and used artificial intelligence to identify potential
Speaker 1: irregularities in clinical procedures. So essentially, the tool was meant
Speaker 1: to isolate instances of unwanted clinical variation so that healthcare
Speaker 1: companies could address any problems early on before they become
Speaker 1: bigger issues. By the summer of twenty twenty two, Olive
Speaker 1: AI was in a totally different financial position because the
Speaker 1: economy was no longer booming. Olive had potentially over extended itself,
Speaker 1: so the company did what countless others did in the
Speaker 1: summer of twenty twenty two, it held extensive layoffs, so
Speaker 1: around four hundred fifs if the employees at Olive were
Speaker 1: let go. Things however, did not improve, and so like Babylon,
Speaker 1: Olive ultimately would begin selling off components of its own
Speaker 1: business to other companies it was so it was essentially
Speaker 1: getting broken down for parts, and this might be one
Speaker 1: of the reasons that the Free Press of Columbus is
Speaker 1: so critical of CEO Sean Lane, because the layoffs affected
Speaker 1: many people in Ohio, and a lot of people likely
Speaker 1: felt that leaders like Sean Lane were exploiting the products
Speaker 1: of labor so that they and other investors could hit
Speaker 1: the eject button while avoiding the worst of the consequences.
Speaker 1: You know, if you actually play your cards right, you
Speaker 1: could end up better off than you did when you
Speaker 1: started the whole thing. And sure, a whole bunch of
Speaker 1: employees and former customers might not be able to say
Speaker 1: the same, but you got yours. Gush darn it. At
Speaker 1: least that's the feeling I get when reading the Free
Speaker 1: Press article, which I could be projecting here. I'm sure
Speaker 1: the truth of the matter is far less cynical than that.
Speaker 1: By how much I don't know, But like Babylon, critics,
Speaker 1: including former Olive employees, argued that a lot of the
Speaker 1: AI powered components weren't really true AI when you got
Speaker 1: down to it, or they were extremely simplistic automated processes that,
Speaker 1: depending upon your perspective, don't actually meet the threshold to
Speaker 1: be called artificial intelligence. Now, I would say that's a
Speaker 1: slippery slope, because defining artificial intelligence is deceivingly difficult. Heck,
Speaker 1: for that matter, defining human intelligence is actually really tricky.
Speaker 1: So is an automated algorithm artificial intelligence? And if not,
Speaker 1: how complicated does the system need to be in order
Speaker 1: to qualify as AI does there need to be some
Speaker 1: sort of decision making component to it in order to
Speaker 1: be AI. I don't actually have the answers to these questions.
Speaker 1: You know, what's AI to one person might not be
Speaker 1: AI to someone else, Which is kind of like the
Speaker 1: legal definition of pornography in someplace is where it said
Speaker 1: I can't tell you what it is, but I know
Speaker 1: it when I see it. It's kind of that similar
Speaker 1: situation anyway. With Oli, the problem was that once the
Speaker 1: post pandemic boom had settled, the company was facing high
Speaker 1: costs of business and revenue just wasn't keeping up. Hiring
Speaker 1: freezes and layoffs in twenty twenty two were followed by
Speaker 1: some high profile departures from the company. The chief financial
Speaker 1: officer and the chief product officer both left by the
Speaker 1: fall of twenty twenty two. Olive also saw its client
Speaker 1: base Diminish providers began to shop around to some of
Speaker 1: Olive's competition, so the company began to lose customers and
Speaker 1: the ending was not yet set in stone. As late
Speaker 1: as March twenty twenty three, Olive continued to raise hundreds
Speaker 1: of millions of dollars collectively, the company raised more venture
Speaker 1: capital funding than any other health tech startup in history,
Speaker 1: but that was not enough to make the business model
Speaker 1: actually work, so Olive sold off different parts of its
Speaker 1: business to various companies, and also faced a lawsuit from
Speaker 1: Ohio's state Economic Development Department because the company had failed
Speaker 1: to live up to an obligation it had made in
Speaker 1: order to provide a certain number of jobs in return
Speaker 1: for the considerable tax incentives that it had enjoyed. So
Speaker 1: on Halloween twenty twenty three, all of AI shut down. Now,
Speaker 1: these are just two examples of Heck, it's just two
Speaker 1: examples of digital health companies with AI components to it
Speaker 1: that shut down despite the huge boom and AI investment.
Speaker 1: If we extend that to AI startups in general, there
Speaker 1: are tons of examples of AI startups that have had
Speaker 1: to shut down over the last year or so. And again,
Speaker 1: that's not necessarily an indication that the business itself was
Speaker 1: a bad idea, or that the service or product they
Speaker 1: planned to provide just had no place. That might not
Speaker 1: be the case. AI is inherently a difficult discipline to
Speaker 1: get into and make it work from a business perspective,
Speaker 1: It needs to work really well. It needs to be
Speaker 1: dependable and replicable, Like you need to make sure you
Speaker 1: can rely on the results and that if you ask
Speaker 1: the thing twenty times, you're going to get the right
Speaker 1: answer all twenty times. That's hard to do from a
Speaker 1: technical level. But also, as I mentioned multiple times, just
Speaker 1: the expense of running an AI centric business is so
Speaker 1: high that in order to make enough money to cover
Speaker 1: all the costs of operation and then make profit on
Speaker 1: top of that, it's really hard. You either have to
Speaker 1: scale up super fast so that you're able to meet
Speaker 1: an enormous number of customers around the world, or you
Speaker 1: have to price yourself at a level where you're going
Speaker 1: to see a return, but then you run the risk
Speaker 1: of no one buying your product or service because it's
Speaker 1: way too expensive. Yeah, it might be AI powered, but
Speaker 1: why do I want to spend ten times more than
Speaker 1: I would if I go with a human powered company.
Speaker 1: It's going to get me reliable results, it just won't
Speaker 1: be AI driven results. So yeah, we're still in this
Speaker 1: world where AI it's got incredible potential, Like I can't
Speaker 1: even begin to imagine the potential AI has to transform
Speaker 1: how we do everything. If it's applied properly. But the
Speaker 1: challenges of getting there are considerable, and they're not going
Speaker 1: to be solved overnight. And it doesn't matter how flashy
Speaker 1: an AI company is or how excited investors are to
Speaker 1: try and get in on that particular gold rush. It's
Speaker 1: not going to make the AI powered future get here
Speaker 1: any quicker. It might actually slow things down. So, as always,
Speaker 1: I recommend employing critical thinking whenever you encounter anything, honestly,
Speaker 1: but particularly when you encounter information or news about artificial intelligence.
Speaker 1: Use critical thinking because again, I do believe there are
Speaker 1: ways where AI is going to make a positive difference
Speaker 1: in how we go about doing different tasks. But slapping
Speaker 1: AI onto something does not automatically make it better, just
Speaker 1: as I would tell the hosts of the podcast The
Speaker 1: Besties that throwing the adjective super in a video games
Speaker 1: title does not automatically make it better. That's just a
Speaker 1: general joke at The Besties. I listened to an episode
Speaker 1: recently where they were jokeingally suggesting that if you have
Speaker 1: super in the title, it must mean that the game
Speaker 1: is better. So great show. By the way, I have
Speaker 1: no connection to the Besties. I don't even know any
Speaker 1: of the people who are the hosts of that show.
Speaker 1: But if you like video game discussions, you should definitely
Speaker 1: check it out. That's just a free plug from yours truly,
Speaker 1: and again I have no connection to them. They're not
Speaker 1: an iHeart podcast than like that. It's just a show
Speaker 1: I enjoy. That's it for this episode. I'll probably do
Speaker 1: more episodes about AI startups and kind of talk about
Speaker 1: the challenges they face, because I really do think there
Speaker 1: are some startups out there, including in the digital health space,
Speaker 1: that are trying to do really interesting, important work. But
Speaker 1: in many cases, I think the folks who perhaps are
Speaker 1: the leaders behind those companies may not have a full
Speaker 1: understanding or appreciation of how hard it's going to be,
Speaker 1: and that ends up falling on the actual experts in
Speaker 1: the field, the computer scientists, etc. To try and realize
Speaker 1: a vision that is inherently extremely difficult to accomplish, not impossible, necessarily,
Speaker 1: but very challenging. So I'll probably do some more of
Speaker 1: these in the future, not so that I could just say, Haha,
Speaker 1: look at these companies that didn't make it, but to
Speaker 1: get a deeper understanding of why didn't they make it,
Speaker 1: or for the ones that do make it, what's set
Speaker 1: them apart, because I think there's some valuable lessons to
Speaker 1: be learned there. In the meantime, I hope all of
Speaker 1: you out there are doing well, and I will talk
Speaker 1: to you again really soon. Tech Stuff is an iHeartRadio production.
Speaker 1: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,
Speaker 1: or wherever you listen to your favorite shows.