October 2, 2024 • 40 mins
AI is hard. How hard? Your company can hit a valuation of more than one billion dollars one moment and you're out of business the next. We look at two digital health companies that went from the highest highs to the lowest lows.
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Speaker 1 (00:04):
Welcome to tech Stuff, a production from iHeartRadio. Hey there,
and welcome to tech Stuff. I'm your host Jonathan Strickland.
I'm an executive producer with iHeart Podcasts and how the
tech are you? You know. A couple of years ago,
it seemed like AI had just exploded into the mainstream
(00:28):
out of nowhere. Now, in reality, thousands of people have
been working for decades to bring various AI implementations to
a point where they could be deployed in the real world,
not just you know, R and D projects. I think
a lot of us are hyper aware of generative AI
in particular. That's the kind of application that's easy for
(00:50):
the average person to have experienced and perceived, you know,
first hand. But there's a lot more going on in
AI than just chat by and image generators, that kind
of thing. There's widespread agreement that AI is definitely going
to have an enormous impact on pretty much everything. There's
(01:12):
less agreement about how this transformation is going to manifest
or in what time frame it's going to happen. We've
got a lot of companies that have made some fairly
aggressive moves into the space, either developing AI or trying
to implement AI solutions, and it hasn't always worked out.
(01:34):
In the meanwhile, you've got this incredible environment in which entrepreneurs,
computer scientists, venture capital investors, and more are all trying
to leverage the moment. Like that whole idea of opportunity knocking.
You've got to seize on that opportunity because who knows
when it's going to come around again, Even if they're
(01:55):
not actually prepared to execute upon that opportunity. Sometimes leveraging
opportunity just doesn't go the way you want it to. Now,
I've already published tech Stuff episodes in the past about
failed tech startups. Some of those startups reached really high
levels of valuation, like topping a billion dollars, which catapults
(02:18):
them into the so called unicorn status. A unicorn is
a startup doesn't have to be tech. It's a startup
business that hits evaluation of a billion dollars at some
point or another. But even with that kind of high valuation,
and even with the initial excitement and support of investors
(02:38):
who have really deep pockets, startups sometimes collapse for one
reason or another. So today I thought we should talk
about a couple of digital health AI startups that have
followed this particular path. While AI enthusiasm has built to
what I would call a frenzy, in more recent months
(03:00):
that enthusiasm has waned somewhat. Investors have kind of backed
off a little bit from AI. There have been a
lot of articles that have been published that say any
real gains from AI are probably years down the road,
at least for most implementations. This is not necessarily universal
for all AI, which is an important thing to remember.
(03:22):
Not all AI is the same, but for many implementations
it's just too early to think of AI making an
enormous impact on business objectives. And so again, investments have
started to taper off quite a bit. People are being
more particular with their investments for lots of reasons, but
(03:46):
a big one of those is the perception that AI
is not really ready for prime time to totally transform
everything right now. So a lot of AI companies or
companies that have, you know, kind of leveraged AI to
be their sales pitch have kind of found themselves in
dire straits. The condition of dire straits, not the band.
(04:09):
Dire straits, not the sultans of swing. Now, before I
get to specifics, let's just establish some general factors, all right.
So artificial intelligence is an expensive discipline. It is a
resource hungry computer application. So you have to either own
(04:29):
or have access to really powerful data centers. For any
AI application that's going beyond just a proof of concept.
If you plan on launching something that is meant to
be a product or service that is in part or
wholly dependent upon AI, you have to have access to
that compute power. Now, on top of that, AI applications
(04:52):
typically require specific types of powerful parallel processing capabilities, which
means that you need a particular kind of computer chip.
You can't just get any computer chip, even a really
fast one. You need one that's really good at handling
parallel processing. So, like in the early days of AI,
GPUs were a really big part of AI processing because
(05:15):
GPUs graphics processing units typically are designed to be parallel processors.
They have multiple cores, and they can do multi threading
and work on a lot of different problems all simultaneously.
AI needs that capability, or at least a lot of
machine learning processes require that kind of computational power. So
the chips best suited to do that are not always
(05:38):
in plentiful supply. So that means we've got this big
pool of AI companies. Some of them are part of
much larger organizations like Microsoft or Google, but all of
them are competing for a limited supply of processors that
are suitable for handling AI computational loads. Not everyone is
(05:59):
going to come out of winner in that kind of competition.
You know, big companies obviously have a huge advantage over
smaller startups that are dependent upon rounds of fundraising from investors.
They're going to venture capitalists to get a influx of
cash in order to be able to do business. Meanwhile,
you have these monoliths like Microsoft and Google that have
(06:21):
decades of wealth that have been generated and they lean
on that in order to get an advantage in the market.
So that's one thing. Another is that scaling up is
particularly challenging for AI companies. This is hard for any startup. Right,
a startup can come up with a brilliant idea and
(06:42):
have an idea that truly has a good place in
the market. Once you get to a level of scale
where you can make this a revenue generating business, but
getting there is hard. Right, if it's manufacturing, then you
have to figure out, well, how are we going to
afford to manufac facture in the bulk we need in
order to make this a viable business for services, How
(07:05):
do we make sure that our business can provide the
services to the customer base we're going to need in
order to have a viable business. These are non trivial challenges.
You have to figure out how do you actually meet
the demand that you're hoping to create. Now, first, there
has to be a demand there in the first place.
(07:25):
And if there's no existing demand, you have to create
that demand. Then you have to be able to deliver
value for that demand. These are really hard problems, so
a lot of startups do fail, like whether they're in
the tech sector or not, and for AI companies it
is particularly difficult. Scientists might develop a truly intriguing use
(07:46):
for artificial intelligence. The work's great on a small scale,
like yeah, they can prove it works for a small
test group or maybe a region or a very specific industry.
But then to grow that so that you can meet
the needs of customers around the world that could require
way more resources than you can actually afford even as
(08:08):
a unicorn. So AI startups can end up burning through
cash really quickly, not through terrible mismanagement, though of course
that can happen too, but just because HEYI is expensive.
So I wanted to clear that up at the start
because I don't want to get the impression that the
folks that are behind these businesses necessarily did something wrong.
(08:30):
Although in one case we'll see that there is at
least one news outlet that very much feels like the
founder of a company did many things wrong. I don't
want to say that they were necessarily bad at managing money.
That could be a factor as well, but I'm trying
to separate this from the dot com bubble of the
(08:51):
late nineties. So with the dot com bubble, you had
all these startups that got enormous investments in cash, and
you know, some of them went public on the stock
market and their stocks inflated to ridiculous levels. And you
had these companies that didn't have fully baked business plans
in place. They were absolutely swimming in cash, and a
(09:15):
lot of times people were making extravagant purchases like crazy
office things like like a full bar or whatever, without
actually being able to put those assets to work to
create a business that could stand on its own. And
ultimately the bubble burst and the entire industry collapsed. You know,
(09:36):
some companies survived, but a lot of them didn't. Well,
I want to draw a line between those and what
might be an AI tech bubble. I think it's fair
to call it an AI tech bubble because one almost
universal issue for all the AI startups is this challenge
that artificial intelligence is inherently expensive. They could also fall
(10:01):
victim to the same problems that we solve with dot
com businesses. That's still a possibility. I'm not saying that
AI companies are somehow immune to human frailties of going
overboard and like everybody gets a new car or whatever
it might be, but that the very nature of AI
itself becomes a massive risk as far as seeing a
(10:25):
return on investment. So with all that set, we're ready
to start diving into discussions of a pair of different
digital health companies that were largely centered around the idea
of artificial intelligence revolutionizing the way we do certain things
(10:46):
in healthcare. Whether or not artificial intelligence was actually playing
a part in that, that's more of an open question.
We're going to get into that in just a moment.
Before we do that, let's take a quick break to
thank our sponsors. Okay, we're back. Let's talk about our
(11:11):
first digital health company and what happened. And in these cases,
I think it's safe to say that the AI component
was really just one contributing factor to how these two
health company startups failed over time. I think it was
a major contributing factor, but just one of multiple factors.
(11:32):
But another is that there was an understandable but arguably
foolish rush of cash influence in the health space following
the twenty twenty COVID outbreak. Now that rush of cash
was understandable because obviously the pandemic had an enormous impact
on people all around the world. There were tons of regions,
(11:54):
entire countries that were operating under lockdown conditions, sometimes for
so several months at a stretch. Now, obviously that would
change how we do pretty much everything from how we
work to how students were attending lessons, to how you
actually got to see a physician if you needed one,
(12:16):
and so out of necessity, health companies new and old
attempted to adapt to this new reality. Now, it could
be really hard for large established companies to adapt to
rapidly changing conditions. Being nimble isn't exactly a common trait
for legacy organizations. That opened up opportunities for younger startups
(12:38):
to innovate in the space and to attempt to serve
customers in ways that larger organizations simply couldn't replicate. And
one of those companies was called Babylon. Now, Babylon was
founded years before the pandemic. It was founded way back
in twenty thirteen. Technically it's one year year younger than
(13:00):
the other digital health company we'll talk about in a
little bit. Babylon launched in the United Kingdom. Ultimately it
would extend services to other parts of the world, primarily
in Asia, but also the United States and a couple
of places in Africa. It was a subscription based healthcare
services company, and initially it was one that had a
(13:23):
fairly simple approach. Customers or patients in other words, would
communicate with healthcare professionals via text messages and video conferences.
So it was a telehealth solutions company, which again not
that groundbreaking, right, there were other telehealth solutions out there,
(13:43):
and at this stage there was no real AI component.
This was all let's put patients in touch with doctors
and do it in a way where the patient doesn't
necessarily have to take time out of his or her
or their day to go and meet with physician. They
could do it through this app. So you might say, well,
(14:04):
where is the AI component if this episode is about
AI health startups. Well, that took the form of a
chat bot that was developed later on in Babylon. It
was an idea that I think was present from the beginning,
Like this was a goal early on was to develop
a chat bot that would be able to interact with patients,
(14:24):
and the chatbot would be able to answer questions, and
ultimately Babylon claimed it would be able to do things
like diagnose patients. So not just like answer questions about
physician availability or simple questions that might lead to a
way to alleviate symptoms that are acutely bothering a patient,
(14:45):
but actually diagnosing the underlying cause of those symptoms. That
is a huge claim, I mean as a remarkable claim,
and it requires remarkable evidence to support it. When you're
talking about healthcare, there's a high bar you need to meet,
a high bar of confidence. If you do not meet that,
(15:06):
then that means you probably should not not Probably you
should not offer these services to patients who were talking
literally matters of life and death. So understandably, a lot
of critics were raising concerns about how reliable this chatbot
actually was and whether or not it was ethical to
(15:26):
even suggest that a chatbot could accurately diagnose someone's ailments
through interacting with that patient. And again, all of this
was happening before the pandemic and well before Open Ai
really opened the floodgates with chat GPT in twenty twenty two,
So a very aggressive approach toward positioning AI as a
(15:49):
solution to a complicated problem. Now, in the past, I've
talked about how AI generated answers can sometimes be incomplete
or unreliable. That's you using today's AI chatbots, which are
miles ahead of the stuff that was being developed in
the twenty tens. So imagine putting your life in the
virtual hands of a fallible chatbot back in twenty eighteen. Wolf. However,
(16:16):
according to a great piece that appeared in Wired, it
was written by Grace Brown. It's titled The Fall of
Babylon is a Warning for AI Unicorns. Well, according to
that piece, the AI bit of Babylon might have been
a stretch in the first place, it might have been
disingenuous to reference this as artificial intelligence. So Brown cites
(16:38):
a consulting doctor by the name of Hugh Harvey, who
at one time worked for Babylon. Now. According to Harvey,
the AI decision tree that it would follow when interacting
with patients was essentially an Excel spreadsheet that correlated to
different parts of the human body. So a patient using
Babylon's app would indicate, you know, where their symptoms were
(17:01):
affecting them, like oh, my leg is itching or something
like that. The app would essentially hone in on possible
diagnoses by eliminating all the stuff that wasn't a potential
candidate for an explanation, which is not a very sophisticated
method of determining what the underlying cause is. And as
(17:22):
Harvey told Brown, quote, I was like, well, this isn't
really artificial intelligence, end quote. But whether we should classify
the inner workings of Babylon as AI or not AI
was definitely part of the company's messaging to investors. So
you could say, well, this isn't really artificial intelligence. This
(17:42):
is a very simplistic decision tree. There's no artificial intelligence
going on here. There's no decision making, but that's not
how the company was marketing their capabilities to potential customers.
Babylon was saying, we use artificial intelligence to help treat
patients to die noose and treat them. So, whether you
want to argue that AI was happening or not, the
(18:04):
company certainly was claiming that to be the case. And
Babylon initially did pretty well when it came to raising investments.
So by twenty nineteen, before the pandemic, Babylon had raised
more than half a billion dollars in funding over the years.
So remember it was founded in twenty thirteen. By twenty nineteen,
more than half a billion dollars in various investment rounds.
(18:26):
In twenty twenty one, the company played the risky maneuver
of going public through the use of a special purpose
Acquisition company or SPAC SPAC aka a blank check company.
Now I have talked about spacks before, but let's have
a quick refresher. Typically, when a private company is preparing
(18:47):
to transition into a publicly traded company where the average
citizen can buy stock in the company, it first has
to go through an extensive set of steps in order
to get to the IPO or initial public offering. This
involves a ton of scrutiny from regulators. Here in the
United States, it's the Securities and Exchange Commission, or SEC.
(19:11):
As Kate Ashford wrote in Forbes Advisor quote, going public
is a challenging, time consuming process that's difficult for most
companies to navigate alone. A private company planning an IPO
needs not only to prepare itself for an exponential increase
in public scrutiny, but it also has to file a
(19:32):
ton of paperwork and financial disclosures to meet the requirements
of the Securities and Exchange Commission SEC, which oversees public
companies end quote. So if a startup is looking to
get access to a ton of cash through going public
and it's a bit strapped for time, an alternative to
the IPO is the SPAC. So with a SPACK you
(19:56):
have a holding company. So this company doesn't really make
or do anything. It's kind of like an empty envelope.
So the one thing it can do is it can
go through all the regulatory processes required to go public
and become a publicly traded company. So now you've got
a publicly traded company that doesn't actually do anything else.
(20:16):
So using this empty shell of a company, you then
can acquire a private company that's just itching to go public,
but it doesn't have the time or the ability to
do this through the IPO method, or if they did
do an IPO, the value of stock that would be
determined through that process would be much lower than what
(20:37):
they actually want it to be. So your SPAC, your
SPAC acquires this private startup. Now through the transitive property
of ownership, that startup is a publicly traded company, or
at least it's part of a publicly traded company, And
it's like the startup got a chance to skip all
that boring paperwork and get straight to the part where
(20:58):
people throw money at it. However, if it turns out
the startup doesn't have the ability to succeed in the
public marketplace, while all of this can then come crashing down.
Shareholders can lose confidence in the company, They can sell
off their shares, share prices can fall, that big old
pile of money can start to shrink, and it's almost
(21:19):
like skipping all those steps that are intended to make
sure that companies can make the transition from private to
public in a sustainable way might be a bad idea.
That's what happened with Babylon, at least to some extent.
A SPAC called al Kourie Global Acquisition Corporation acquired Babylon
in October twenty twenty one. The SPAC, in turn had
(21:40):
the backing of palanteer Is, Peter Thiel's big data analytics company.
It was one of many investors that were part of this.
Babylon's valuation was estimated at four point two billion with
a B dollars, and it is wild to think that
just two years later Babylon would get sold off for
(22:03):
parts as the value of the company had totally collapsed.
And by collapsed, I mean that eighteen months after being
listed in the New York Stock Exchange, the stock price
was ninety nine percent lower than where it had started off.
How did that happen? I'll explain more, but first let's
take another quick break. Okay, we have Babylon, a startup
(22:35):
from twenty thirteen that reaches incredible heights through this reverse
merger process with a SPACK and is worth or value
that I should say, four point two billion dollars. Well,
according to Brown's Piece and Wired, Babylon was actually already
in trouble. By the time it joined the stock exchange,
the company was running through cash very quickly in an
(22:58):
attempt to scale the business, to grow it beyond what
it already was. Now. As I mentioned at the top,
AI businesses in particular are really costly to scale, So
unless you've got really deep pockets, like the pockets of
one of the big five tech companies Microsoft, Google, Meta, Apple,
or Amazon, well, you'll likely find challenges in making your
(23:20):
money last long enough for you to scale properly and
be self sufficient. Babylon was spending way more money than
it was bringing in, so it was losing money year
over year, and as a publicly traded company, Babylon had
to share this information with the sec. You know, if
you're a privately held company, you don't have to talk
about how much money you lost. The public remains uninformed,
(23:43):
But with publicly traded ones, that information gets filed and
it becomes available to the public, so you can see
how much money the company is losing year over year. Well, clearly,
shareholders lost confidence. The stock price crashed, and just a
couple of years after having going public with that SPAC transaction,
Babylon went into administration. In the UK and bankruptcy in
(24:07):
the US. So administration in the UK is kind of
similar to bankruptcy here in the United States. They're not identical,
but they are similar processes. It's meant to try and
return as much value to investors as possible while a
business effectively shuts down. So Brown's piece gives more details
(24:27):
about what was actually going on within Babylon, but in general,
it was a case of a company spending money it
had not yet raised in the hopes of hitting that
sweet spot and delivering upon the company's value proposition. The
stories of Babylon sound kind of similar to what I
heard about Aharranos Now Farrannose was that infamous high tech
(24:49):
health company that absolutely imploded after an expose revealed that
the company's flagship product, a device that was meant to
analyze a tiny micro drop of blood and potentially run
hundreds of different medical tests on It turned out that
product just did not work as advertised, and in fact,
it might not ever work at all, at least not
(25:10):
to the extent that was being promised by the company.
No matter how much effort was put into it, it
was going to run up against some fundamental limitations that
meant it just could not work the way it was envisioned,
and that the whole company was essentially a house of
cards built upon this belief that ultimately tech can do
anything if you just work at it hard enough, and
(25:32):
it turned out that just wasn't true. Well, it sounds
like Babylon suffered a kind of similar fate. Now check
out Grace Brown's article on Wired to read a more
detailed story about that. But we need to move on
at this point. So Babylon ultimately goes out of business,
sells its various business divisions and assets off to other
(25:53):
companies to return as much value to investors as possible,
and goes by by Now we're going to talk about
a different digital health company that had AI aspirations, this
one called Olive, sometimes called Olive AI. So for this bit,
I'm referencing a few different articles that I found particularly
helpful while reading up on the company. One of those
(26:15):
is an article by Emily Olsen in healthcare drive dot com.
It was written back in November twenty twenty three. It
is titled health AI startup Olive to shut down. So
spoiler alert there, except you know, that's what this episode's
all about. So maybe not so much a spoiler. I
also used another article by Giles Bruce. This one was
(26:36):
for Becker Hospital Review. It was titled the Rise and
Fall of Olive AI, a timeline that gave some you know,
simple little moments in time of what was going on
within Olive. And there were others as well. There was
an article by Free Press staff of Free Press Columbus
is in Columbus, Ohio that was very useful and also
(26:58):
not at all unbiased. Let's I'll talk about so like Babylon,
Olive actually got its start well before the current AI craze,
not to mention before the pandemic. It launched back in
twenty twelve in Columbus, Ohio a guy named Sean Lane,
whom the Columbus Free Press said, developed quote shadowy and
(27:19):
shady AI software which promised to cut administrative costs for
healthcare providers in quote, led Olive AI for a little
more than a decade before the company totally collapsed. The
Free Press has a lot of things to say about
Sean Lane, and they are pretty darn critical. They pull
no punches in their take. For example, that piece points
(27:42):
out that Shawn Lane incorporated a new company on the
very same day that olive Ai announced it was going
on a business So they said, well, that doesn't sit
well with us. Like your company that you led for
more than a decade spectacularly fails, and on that same
day that it shuts down, you announce or not announced,
but you incorporate a new company. That seems kind of questionable.
(28:05):
That's what the Free Press was saying. So if you
want to read some serious shade directed at Lane and
olive Ai, check out the article in Free Press Columbus.
It's titled out of control venture capitalysts throw more millions
at disgraced Columbus CEO. But again, note that there might
be a teenc bit of bias in that reporting. I'm
not saying it's misplaced bias, but it's there, all right.
(28:29):
So oli Ai, let's talk about what the company's sales
pitch was. So this was a B to B kind
of company, meaning it would count other businesses as its customers.
It's a business to business company. It didn't interface with
private citizens or anything like that. And the company's main
product was a software package that was meant to help
(28:49):
healthcare companies automate certain processes such as keeping tabs on
patients insurance coverage, making sure that you know their insurance
is still active that thing, or processing authorization requests through
an automated system. So essentially, the idea was to streamline
the numerous and repetitive tasks that are involved in healthcare administration.
(29:12):
And this was a pitch that a lot of investors
loved because it suggested that healthcare companies would be able
to significantly decrease their costs and increase their efficiency while
passing savings on to customers. Oh no, wait, sorry, no,
I forget that last part. I actually meant while generating
massive profits that mean huge shareholder returns, customers the patients,
(29:35):
they would still see the same costs because, after all,
here in the United States, it's usually an insurance company
that's actually paying up. No one cares if an insurance
company has to pay the same amount for services that
are actually costing less because the hospital or other healthcare
service provider has found a more efficient way of doing business.
They don't care if the insurance company is still paying
(29:57):
the same amount, even if the services themselves technically cost less.
Of course, insurance companies might care, and then therefore insurance
customers are going to care because ultimately those providers are
going to pass those costs down to the insurance customers
and they're gonna the customers are going to see higher
(30:18):
deductibles and higher premiums that kind of thing. But never
mind all that, that doesn't matter to investors, right. So
the earliest version of ol of debuted back in twenty seventeen,
so this is like five years after the company has
been founded, and the company enjoyed support from investors throughout
its early years. But like Babylon and countless other digital
(30:39):
health companies, it was the pandemic that would send the
company's fortunes to the moon. That's when investors were just
pouring huge amounts of money into these digital health companies.
So in twenty twenty, all of Ai raised nearly a
billion dollars in funding. That's just in one year, and
this is after the company had been incorporated for nearly
(31:00):
a decade. So in twenty twenty one, Olive acquired another
AI focused healthcare company called Empiric Health, which itself was
a spinoff from yet another healthcare company called inter Mountain
Health out of Salt Lake City, Utah. Stuff gets really complicated,
not just from a technical perspective, So empiric Health focused
on clinical analytics and used artificial intelligence to identify potential
(31:25):
irregularities in clinical procedures. So essentially, the tool was meant
to isolate instances of unwanted clinical variation so that healthcare
companies could address any problems early on before they become
bigger issues. By the summer of twenty twenty two, Olive
AI was in a totally different financial position because the
(31:47):
economy was no longer booming. Olive had potentially over extended itself,
so the company did what countless others did in the
summer of twenty twenty two, it held extensive layoffs, so
around four hundred fifs if the employees at Olive were
let go. Things however, did not improve, and so like Babylon,
Olive ultimately would begin selling off components of its own
(32:10):
business to other companies it was so it was essentially
getting broken down for parts, and this might be one
of the reasons that the Free Press of Columbus is
so critical of CEO Sean Lane, because the layoffs affected
many people in Ohio, and a lot of people likely
felt that leaders like Sean Lane were exploiting the products
(32:30):
of labor so that they and other investors could hit
the eject button while avoiding the worst of the consequences.
You know, if you actually play your cards right, you
could end up better off than you did when you
started the whole thing. And sure, a whole bunch of
employees and former customers might not be able to say
the same, but you got yours. Gush darn it. At
least that's the feeling I get when reading the Free
(32:50):
Press article, which I could be projecting here. I'm sure
the truth of the matter is far less cynical than that.
By how much I don't know, But like Babylon, critics,
including former Olive employees, argued that a lot of the
AI powered components weren't really true AI when you got
down to it, or they were extremely simplistic automated processes that,
(33:15):
depending upon your perspective, don't actually meet the threshold to
be called artificial intelligence. Now, I would say that's a
slippery slope, because defining artificial intelligence is deceivingly difficult. Heck,
for that matter, defining human intelligence is actually really tricky.
So is an automated algorithm artificial intelligence? And if not,
(33:37):
how complicated does the system need to be in order
to qualify as AI does there need to be some
sort of decision making component to it in order to
be AI. I don't actually have the answers to these questions.
You know, what's AI to one person might not be
AI to someone else, Which is kind of like the
legal definition of pornography in someplace is where it said
(34:01):
I can't tell you what it is, but I know
it when I see it. It's kind of that similar
situation anyway. With Oli, the problem was that once the
post pandemic boom had settled, the company was facing high
costs of business and revenue just wasn't keeping up. Hiring
freezes and layoffs in twenty twenty two were followed by
some high profile departures from the company. The chief financial
(34:23):
officer and the chief product officer both left by the
fall of twenty twenty two. Olive also saw its client
base Diminish providers began to shop around to some of
Olive's competition, so the company began to lose customers and
the ending was not yet set in stone. As late
as March twenty twenty three, Olive continued to raise hundreds
(34:46):
of millions of dollars collectively, the company raised more venture
capital funding than any other health tech startup in history,
but that was not enough to make the business model
actually work, so Olive sold off different parts of its
business to various companies, and also faced a lawsuit from
Ohio's state Economic Development Department because the company had failed
(35:11):
to live up to an obligation it had made in
order to provide a certain number of jobs in return
for the considerable tax incentives that it had enjoyed. So
on Halloween twenty twenty three, all of AI shut down. Now,
these are just two examples of Heck, it's just two
examples of digital health companies with AI components to it
(35:33):
that shut down despite the huge boom and AI investment.
If we extend that to AI startups in general, there
are tons of examples of AI startups that have had
to shut down over the last year or so. And again,
that's not necessarily an indication that the business itself was
(35:56):
a bad idea, or that the service or product they
planned to provide just had no place. That might not
be the case. AI is inherently a difficult discipline to
get into and make it work from a business perspective,
It needs to work really well. It needs to be
dependable and replicable, Like you need to make sure you
(36:18):
can rely on the results and that if you ask
the thing twenty times, you're going to get the right
answer all twenty times. That's hard to do from a
technical level. But also, as I mentioned multiple times, just
the expense of running an AI centric business is so
high that in order to make enough money to cover
(36:40):
all the costs of operation and then make profit on
top of that, it's really hard. You either have to
scale up super fast so that you're able to meet
an enormous number of customers around the world, or you
have to price yourself at a level where you're going
to see a return, but then you run the risk
of no one buying your product or service because it's
(37:00):
way too expensive. Yeah, it might be AI powered, but
why do I want to spend ten times more than
I would if I go with a human powered company.
It's going to get me reliable results, it just won't
be AI driven results. So yeah, we're still in this
world where AI it's got incredible potential, Like I can't
(37:21):
even begin to imagine the potential AI has to transform
how we do everything. If it's applied properly. But the
challenges of getting there are considerable, and they're not going
to be solved overnight. And it doesn't matter how flashy
an AI company is or how excited investors are to
(37:42):
try and get in on that particular gold rush. It's
not going to make the AI powered future get here
any quicker. It might actually slow things down. So, as always,
I recommend employing critical thinking whenever you encounter anything, honestly,
but particularly when you encounter information or news about artificial intelligence.
(38:05):
Use critical thinking because again, I do believe there are
ways where AI is going to make a positive difference
in how we go about doing different tasks. But slapping
AI onto something does not automatically make it better, just
as I would tell the hosts of the podcast The
Besties that throwing the adjective super in a video games
(38:27):
title does not automatically make it better. That's just a
general joke at The Besties. I listened to an episode
recently where they were jokeingally suggesting that if you have
super in the title, it must mean that the game
is better. So great show. By the way, I have
no connection to the Besties. I don't even know any
of the people who are the hosts of that show.
(38:50):
But if you like video game discussions, you should definitely
check it out. That's just a free plug from yours truly,
and again I have no connection to them. They're not
an iHeart podcast than like that. It's just a show
I enjoy. That's it for this episode. I'll probably do
more episodes about AI startups and kind of talk about
the challenges they face, because I really do think there
are some startups out there, including in the digital health space,
(39:13):
that are trying to do really interesting, important work. But
in many cases, I think the folks who perhaps are
the leaders behind those companies may not have a full
understanding or appreciation of how hard it's going to be,
and that ends up falling on the actual experts in
the field, the computer scientists, etc. To try and realize
(39:37):
a vision that is inherently extremely difficult to accomplish, not impossible, necessarily,
but very challenging. So I'll probably do some more of
these in the future, not so that I could just say, Haha,
look at these companies that didn't make it, but to
get a deeper understanding of why didn't they make it,
(39:57):
or for the ones that do make it, what's set
them apart, because I think there's some valuable lessons to
be learned there. In the meantime, I hope all of
you out there are doing well, and I will talk
to you again really soon. Tech Stuff is an iHeartRadio production.
(40:19):
For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,
or wherever you listen to your favorite shows.
Welcome to tech Stuff, a production from iHeartRadio. Hey there,
and welcome to tech Stuff. I'm your host Jonathan Strickland.
I'm an executive producer with iHeart Podcasts and how the
tech are you? You know. A couple of years ago,
it seemed like AI had just exploded into the mainstream
(00:28):
out of nowhere. Now, in reality, thousands of people have
been working for decades to bring various AI implementations to
a point where they could be deployed in the real world,
not just you know, R and D projects. I think
a lot of us are hyper aware of generative AI
in particular. That's the kind of application that's easy for
(00:50):
the average person to have experienced and perceived, you know,
first hand. But there's a lot more going on in
AI than just chat by and image generators, that kind
of thing. There's widespread agreement that AI is definitely going
to have an enormous impact on pretty much everything. There's
(01:12):
less agreement about how this transformation is going to manifest
or in what time frame it's going to happen. We've
got a lot of companies that have made some fairly
aggressive moves into the space, either developing AI or trying
to implement AI solutions, and it hasn't always worked out.
(01:34):
In the meanwhile, you've got this incredible environment in which entrepreneurs,
computer scientists, venture capital investors, and more are all trying
to leverage the moment. Like that whole idea of opportunity knocking.
You've got to seize on that opportunity because who knows
when it's going to come around again, Even if they're
(01:55):
not actually prepared to execute upon that opportunity. Sometimes leveraging
opportunity just doesn't go the way you want it to. Now,
I've already published tech Stuff episodes in the past about
failed tech startups. Some of those startups reached really high
levels of valuation, like topping a billion dollars, which catapults
(02:18):
them into the so called unicorn status. A unicorn is
a startup doesn't have to be tech. It's a startup
business that hits evaluation of a billion dollars at some
point or another. But even with that kind of high valuation,
and even with the initial excitement and support of investors
(02:38):
who have really deep pockets, startups sometimes collapse for one
reason or another. So today I thought we should talk
about a couple of digital health AI startups that have
followed this particular path. While AI enthusiasm has built to
what I would call a frenzy, in more recent months
(03:00):
that enthusiasm has waned somewhat. Investors have kind of backed
off a little bit from AI. There have been a
lot of articles that have been published that say any
real gains from AI are probably years down the road,
at least for most implementations. This is not necessarily universal
for all AI, which is an important thing to remember.
(03:22):
Not all AI is the same, but for many implementations
it's just too early to think of AI making an
enormous impact on business objectives. And so again, investments have
started to taper off quite a bit. People are being
more particular with their investments for lots of reasons, but
(03:46):
a big one of those is the perception that AI
is not really ready for prime time to totally transform
everything right now. So a lot of AI companies or
companies that have, you know, kind of leveraged AI to
be their sales pitch have kind of found themselves in
dire straits. The condition of dire straits, not the band.
(04:09):
Dire straits, not the sultans of swing. Now, before I
get to specifics, let's just establish some general factors, all right.
So artificial intelligence is an expensive discipline. It is a
resource hungry computer application. So you have to either own
(04:29):
or have access to really powerful data centers. For any
AI application that's going beyond just a proof of concept.
If you plan on launching something that is meant to
be a product or service that is in part or
wholly dependent upon AI, you have to have access to
that compute power. Now, on top of that, AI applications
(04:52):
typically require specific types of powerful parallel processing capabilities, which
means that you need a particular kind of computer chip.
You can't just get any computer chip, even a really
fast one. You need one that's really good at handling
parallel processing. So, like in the early days of AI,
GPUs were a really big part of AI processing because
(05:15):
GPUs graphics processing units typically are designed to be parallel processors.
They have multiple cores, and they can do multi threading
and work on a lot of different problems all simultaneously.
AI needs that capability, or at least a lot of
machine learning processes require that kind of computational power. So
the chips best suited to do that are not always
(05:38):
in plentiful supply. So that means we've got this big
pool of AI companies. Some of them are part of
much larger organizations like Microsoft or Google, but all of
them are competing for a limited supply of processors that
are suitable for handling AI computational loads. Not everyone is
(05:59):
going to come out of winner in that kind of competition.
You know, big companies obviously have a huge advantage over
smaller startups that are dependent upon rounds of fundraising from investors.
They're going to venture capitalists to get a influx of
cash in order to be able to do business. Meanwhile,
you have these monoliths like Microsoft and Google that have
(06:21):
decades of wealth that have been generated and they lean
on that in order to get an advantage in the market.
So that's one thing. Another is that scaling up is
particularly challenging for AI companies. This is hard for any startup. Right,
a startup can come up with a brilliant idea and
(06:42):
have an idea that truly has a good place in
the market. Once you get to a level of scale
where you can make this a revenue generating business, but
getting there is hard. Right, if it's manufacturing, then you
have to figure out, well, how are we going to
afford to manufac facture in the bulk we need in
order to make this a viable business for services, How
(07:05):
do we make sure that our business can provide the
services to the customer base we're going to need in
order to have a viable business. These are non trivial challenges.
You have to figure out how do you actually meet
the demand that you're hoping to create. Now, first, there
has to be a demand there in the first place.
(07:25):
And if there's no existing demand, you have to create
that demand. Then you have to be able to deliver
value for that demand. These are really hard problems, so
a lot of startups do fail, like whether they're in
the tech sector or not, and for AI companies it
is particularly difficult. Scientists might develop a truly intriguing use
(07:46):
for artificial intelligence. The work's great on a small scale,
like yeah, they can prove it works for a small
test group or maybe a region or a very specific industry.
But then to grow that so that you can meet
the needs of customers around the world that could require
way more resources than you can actually afford even as
(08:08):
a unicorn. So AI startups can end up burning through
cash really quickly, not through terrible mismanagement, though of course
that can happen too, but just because HEYI is expensive.
So I wanted to clear that up at the start
because I don't want to get the impression that the
folks that are behind these businesses necessarily did something wrong.
(08:30):
Although in one case we'll see that there is at
least one news outlet that very much feels like the
founder of a company did many things wrong. I don't
want to say that they were necessarily bad at managing money.
That could be a factor as well, but I'm trying
to separate this from the dot com bubble of the
(08:51):
late nineties. So with the dot com bubble, you had
all these startups that got enormous investments in cash, and
you know, some of them went public on the stock
market and their stocks inflated to ridiculous levels. And you
had these companies that didn't have fully baked business plans
in place. They were absolutely swimming in cash, and a
(09:15):
lot of times people were making extravagant purchases like crazy
office things like like a full bar or whatever, without
actually being able to put those assets to work to
create a business that could stand on its own. And
ultimately the bubble burst and the entire industry collapsed. You know,
(09:36):
some companies survived, but a lot of them didn't. Well,
I want to draw a line between those and what
might be an AI tech bubble. I think it's fair
to call it an AI tech bubble because one almost
universal issue for all the AI startups is this challenge
that artificial intelligence is inherently expensive. They could also fall
(10:01):
victim to the same problems that we solve with dot
com businesses. That's still a possibility. I'm not saying that
AI companies are somehow immune to human frailties of going
overboard and like everybody gets a new car or whatever
it might be, but that the very nature of AI
itself becomes a massive risk as far as seeing a
(10:25):
return on investment. So with all that set, we're ready
to start diving into discussions of a pair of different
digital health companies that were largely centered around the idea
of artificial intelligence revolutionizing the way we do certain things
(10:46):
in healthcare. Whether or not artificial intelligence was actually playing
a part in that, that's more of an open question.
We're going to get into that in just a moment.
Before we do that, let's take a quick break to
thank our sponsors. Okay, we're back. Let's talk about our
(11:11):
first digital health company and what happened. And in these cases,
I think it's safe to say that the AI component
was really just one contributing factor to how these two
health company startups failed over time. I think it was
a major contributing factor, but just one of multiple factors.
(11:32):
But another is that there was an understandable but arguably
foolish rush of cash influence in the health space following
the twenty twenty COVID outbreak. Now that rush of cash
was understandable because obviously the pandemic had an enormous impact
on people all around the world. There were tons of regions,
(11:54):
entire countries that were operating under lockdown conditions, sometimes for
so several months at a stretch. Now, obviously that would
change how we do pretty much everything from how we
work to how students were attending lessons, to how you
actually got to see a physician if you needed one,
(12:16):
and so out of necessity, health companies new and old
attempted to adapt to this new reality. Now, it could
be really hard for large established companies to adapt to
rapidly changing conditions. Being nimble isn't exactly a common trait
for legacy organizations. That opened up opportunities for younger startups
(12:38):
to innovate in the space and to attempt to serve
customers in ways that larger organizations simply couldn't replicate. And
one of those companies was called Babylon. Now, Babylon was
founded years before the pandemic. It was founded way back
in twenty thirteen. Technically it's one year year younger than
(13:00):
the other digital health company we'll talk about in a
little bit. Babylon launched in the United Kingdom. Ultimately it
would extend services to other parts of the world, primarily
in Asia, but also the United States and a couple
of places in Africa. It was a subscription based healthcare
services company, and initially it was one that had a
(13:23):
fairly simple approach. Customers or patients in other words, would
communicate with healthcare professionals via text messages and video conferences.
So it was a telehealth solutions company, which again not
that groundbreaking, right, there were other telehealth solutions out there,
(13:43):
and at this stage there was no real AI component.
This was all let's put patients in touch with doctors
and do it in a way where the patient doesn't
necessarily have to take time out of his or her
or their day to go and meet with physician. They
could do it through this app. So you might say, well,
(14:04):
where is the AI component if this episode is about
AI health startups. Well, that took the form of a
chat bot that was developed later on in Babylon. It
was an idea that I think was present from the beginning,
Like this was a goal early on was to develop
a chat bot that would be able to interact with patients,
(14:24):
and the chatbot would be able to answer questions, and
ultimately Babylon claimed it would be able to do things
like diagnose patients. So not just like answer questions about
physician availability or simple questions that might lead to a
way to alleviate symptoms that are acutely bothering a patient,
(14:45):
but actually diagnosing the underlying cause of those symptoms. That
is a huge claim, I mean as a remarkable claim,
and it requires remarkable evidence to support it. When you're
talking about healthcare, there's a high bar you need to meet,
a high bar of confidence. If you do not meet that,
(15:06):
then that means you probably should not not Probably you
should not offer these services to patients who were talking
literally matters of life and death. So understandably, a lot
of critics were raising concerns about how reliable this chatbot
actually was and whether or not it was ethical to
(15:26):
even suggest that a chatbot could accurately diagnose someone's ailments
through interacting with that patient. And again, all of this
was happening before the pandemic and well before Open Ai
really opened the floodgates with chat GPT in twenty twenty two,
So a very aggressive approach toward positioning AI as a
(15:49):
solution to a complicated problem. Now, in the past, I've
talked about how AI generated answers can sometimes be incomplete
or unreliable. That's you using today's AI chatbots, which are
miles ahead of the stuff that was being developed in
the twenty tens. So imagine putting your life in the
virtual hands of a fallible chatbot back in twenty eighteen. Wolf. However,
(16:16):
according to a great piece that appeared in Wired, it
was written by Grace Brown. It's titled The Fall of
Babylon is a Warning for AI Unicorns. Well, according to
that piece, the AI bit of Babylon might have been
a stretch in the first place, it might have been
disingenuous to reference this as artificial intelligence. So Brown cites
(16:38):
a consulting doctor by the name of Hugh Harvey, who
at one time worked for Babylon. Now. According to Harvey,
the AI decision tree that it would follow when interacting
with patients was essentially an Excel spreadsheet that correlated to
different parts of the human body. So a patient using
Babylon's app would indicate, you know, where their symptoms were
(17:01):
affecting them, like oh, my leg is itching or something
like that. The app would essentially hone in on possible
diagnoses by eliminating all the stuff that wasn't a potential
candidate for an explanation, which is not a very sophisticated
method of determining what the underlying cause is. And as
(17:22):
Harvey told Brown, quote, I was like, well, this isn't
really artificial intelligence, end quote. But whether we should classify
the inner workings of Babylon as AI or not AI
was definitely part of the company's messaging to investors. So
you could say, well, this isn't really artificial intelligence. This
(17:42):
is a very simplistic decision tree. There's no artificial intelligence
going on here. There's no decision making, but that's not
how the company was marketing their capabilities to potential customers.
Babylon was saying, we use artificial intelligence to help treat
patients to die noose and treat them. So, whether you
want to argue that AI was happening or not, the
(18:04):
company certainly was claiming that to be the case. And
Babylon initially did pretty well when it came to raising investments.
So by twenty nineteen, before the pandemic, Babylon had raised
more than half a billion dollars in funding over the years.
So remember it was founded in twenty thirteen. By twenty nineteen,
more than half a billion dollars in various investment rounds.
(18:26):
In twenty twenty one, the company played the risky maneuver
of going public through the use of a special purpose
Acquisition company or SPAC SPAC aka a blank check company.
Now I have talked about spacks before, but let's have
a quick refresher. Typically, when a private company is preparing
(18:47):
to transition into a publicly traded company where the average
citizen can buy stock in the company, it first has
to go through an extensive set of steps in order
to get to the IPO or initial public offering. This
involves a ton of scrutiny from regulators. Here in the
United States, it's the Securities and Exchange Commission, or SEC.
(19:11):
As Kate Ashford wrote in Forbes Advisor quote, going public
is a challenging, time consuming process that's difficult for most
companies to navigate alone. A private company planning an IPO
needs not only to prepare itself for an exponential increase
in public scrutiny, but it also has to file a
(19:32):
ton of paperwork and financial disclosures to meet the requirements
of the Securities and Exchange Commission SEC, which oversees public
companies end quote. So if a startup is looking to
get access to a ton of cash through going public
and it's a bit strapped for time, an alternative to
the IPO is the SPAC. So with a SPACK you
(19:56):
have a holding company. So this company doesn't really make
or do anything. It's kind of like an empty envelope.
So the one thing it can do is it can
go through all the regulatory processes required to go public
and become a publicly traded company. So now you've got
a publicly traded company that doesn't actually do anything else.
(20:16):
So using this empty shell of a company, you then
can acquire a private company that's just itching to go public,
but it doesn't have the time or the ability to
do this through the IPO method, or if they did
do an IPO, the value of stock that would be
determined through that process would be much lower than what
(20:37):
they actually want it to be. So your SPAC, your
SPAC acquires this private startup. Now through the transitive property
of ownership, that startup is a publicly traded company, or
at least it's part of a publicly traded company, And
it's like the startup got a chance to skip all
that boring paperwork and get straight to the part where
(20:58):
people throw money at it. However, if it turns out
the startup doesn't have the ability to succeed in the
public marketplace, while all of this can then come crashing down.
Shareholders can lose confidence in the company, They can sell
off their shares, share prices can fall, that big old
pile of money can start to shrink, and it's almost
(21:19):
like skipping all those steps that are intended to make
sure that companies can make the transition from private to
public in a sustainable way might be a bad idea.
That's what happened with Babylon, at least to some extent.
A SPAC called al Kourie Global Acquisition Corporation acquired Babylon
in October twenty twenty one. The SPAC, in turn had
(21:40):
the backing of palanteer Is, Peter Thiel's big data analytics company.
It was one of many investors that were part of this.
Babylon's valuation was estimated at four point two billion with
a B dollars, and it is wild to think that
just two years later Babylon would get sold off for
(22:03):
parts as the value of the company had totally collapsed.
And by collapsed, I mean that eighteen months after being
listed in the New York Stock Exchange, the stock price
was ninety nine percent lower than where it had started off.
How did that happen? I'll explain more, but first let's
take another quick break. Okay, we have Babylon, a startup
(22:35):
from twenty thirteen that reaches incredible heights through this reverse
merger process with a SPACK and is worth or value
that I should say, four point two billion dollars. Well,
according to Brown's Piece and Wired, Babylon was actually already
in trouble. By the time it joined the stock exchange,
the company was running through cash very quickly in an
(22:58):
attempt to scale the business, to grow it beyond what
it already was. Now. As I mentioned at the top,
AI businesses in particular are really costly to scale, So
unless you've got really deep pockets, like the pockets of
one of the big five tech companies Microsoft, Google, Meta, Apple,
or Amazon, well, you'll likely find challenges in making your
(23:20):
money last long enough for you to scale properly and
be self sufficient. Babylon was spending way more money than
it was bringing in, so it was losing money year
over year, and as a publicly traded company, Babylon had
to share this information with the sec. You know, if
you're a privately held company, you don't have to talk
about how much money you lost. The public remains uninformed,
(23:43):
But with publicly traded ones, that information gets filed and
it becomes available to the public, so you can see
how much money the company is losing year over year. Well, clearly,
shareholders lost confidence. The stock price crashed, and just a
couple of years after having going public with that SPAC transaction,
Babylon went into administration. In the UK and bankruptcy in
(24:07):
the US. So administration in the UK is kind of
similar to bankruptcy here in the United States. They're not identical,
but they are similar processes. It's meant to try and
return as much value to investors as possible while a
business effectively shuts down. So Brown's piece gives more details
(24:27):
about what was actually going on within Babylon, but in general,
it was a case of a company spending money it
had not yet raised in the hopes of hitting that
sweet spot and delivering upon the company's value proposition. The
stories of Babylon sound kind of similar to what I
heard about Aharranos Now Farrannose was that infamous high tech
(24:49):
health company that absolutely imploded after an expose revealed that
the company's flagship product, a device that was meant to
analyze a tiny micro drop of blood and potentially run
hundreds of different medical tests on It turned out that
product just did not work as advertised, and in fact,
it might not ever work at all, at least not
(25:10):
to the extent that was being promised by the company.
No matter how much effort was put into it, it
was going to run up against some fundamental limitations that
meant it just could not work the way it was envisioned,
and that the whole company was essentially a house of
cards built upon this belief that ultimately tech can do
anything if you just work at it hard enough, and
(25:32):
it turned out that just wasn't true. Well, it sounds
like Babylon suffered a kind of similar fate. Now check
out Grace Brown's article on Wired to read a more
detailed story about that. But we need to move on
at this point. So Babylon ultimately goes out of business,
sells its various business divisions and assets off to other
(25:53):
companies to return as much value to investors as possible,
and goes by by Now we're going to talk about
a different digital health company that had AI aspirations, this
one called Olive, sometimes called Olive AI. So for this bit,
I'm referencing a few different articles that I found particularly
helpful while reading up on the company. One of those
(26:15):
is an article by Emily Olsen in healthcare drive dot com.
It was written back in November twenty twenty three. It
is titled health AI startup Olive to shut down. So
spoiler alert there, except you know, that's what this episode's
all about. So maybe not so much a spoiler. I
also used another article by Giles Bruce. This one was
(26:36):
for Becker Hospital Review. It was titled the Rise and
Fall of Olive AI, a timeline that gave some you know,
simple little moments in time of what was going on
within Olive. And there were others as well. There was
an article by Free Press staff of Free Press Columbus
is in Columbus, Ohio that was very useful and also
(26:58):
not at all unbiased. Let's I'll talk about so like Babylon,
Olive actually got its start well before the current AI craze,
not to mention before the pandemic. It launched back in
twenty twelve in Columbus, Ohio a guy named Sean Lane,
whom the Columbus Free Press said, developed quote shadowy and
(27:19):
shady AI software which promised to cut administrative costs for
healthcare providers in quote, led Olive AI for a little
more than a decade before the company totally collapsed. The
Free Press has a lot of things to say about
Sean Lane, and they are pretty darn critical. They pull
no punches in their take. For example, that piece points
(27:42):
out that Shawn Lane incorporated a new company on the
very same day that olive Ai announced it was going
on a business So they said, well, that doesn't sit
well with us. Like your company that you led for
more than a decade spectacularly fails, and on that same
day that it shuts down, you announce or not announced,
but you incorporate a new company. That seems kind of questionable.
(28:05):
That's what the Free Press was saying. So if you
want to read some serious shade directed at Lane and
olive Ai, check out the article in Free Press Columbus.
It's titled out of control venture capitalysts throw more millions
at disgraced Columbus CEO. But again, note that there might
be a teenc bit of bias in that reporting. I'm
not saying it's misplaced bias, but it's there, all right.
(28:29):
So oli Ai, let's talk about what the company's sales
pitch was. So this was a B to B kind
of company, meaning it would count other businesses as its customers.
It's a business to business company. It didn't interface with
private citizens or anything like that. And the company's main
product was a software package that was meant to help
(28:49):
healthcare companies automate certain processes such as keeping tabs on
patients insurance coverage, making sure that you know their insurance
is still active that thing, or processing authorization requests through
an automated system. So essentially, the idea was to streamline
the numerous and repetitive tasks that are involved in healthcare administration.
(29:12):
And this was a pitch that a lot of investors
loved because it suggested that healthcare companies would be able
to significantly decrease their costs and increase their efficiency while
passing savings on to customers. Oh no, wait, sorry, no,
I forget that last part. I actually meant while generating
massive profits that mean huge shareholder returns, customers the patients,
(29:35):
they would still see the same costs because, after all,
here in the United States, it's usually an insurance company
that's actually paying up. No one cares if an insurance
company has to pay the same amount for services that
are actually costing less because the hospital or other healthcare
service provider has found a more efficient way of doing business.
They don't care if the insurance company is still paying
(29:57):
the same amount, even if the services themselves technically cost less.
Of course, insurance companies might care, and then therefore insurance
customers are going to care because ultimately those providers are
going to pass those costs down to the insurance customers
and they're gonna the customers are going to see higher
(30:18):
deductibles and higher premiums that kind of thing. But never
mind all that, that doesn't matter to investors, right. So
the earliest version of ol of debuted back in twenty seventeen,
so this is like five years after the company has
been founded, and the company enjoyed support from investors throughout
its early years. But like Babylon and countless other digital
(30:39):
health companies, it was the pandemic that would send the
company's fortunes to the moon. That's when investors were just
pouring huge amounts of money into these digital health companies.
So in twenty twenty, all of Ai raised nearly a
billion dollars in funding. That's just in one year, and
this is after the company had been incorporated for nearly
(31:00):
a decade. So in twenty twenty one, Olive acquired another
AI focused healthcare company called Empiric Health, which itself was
a spinoff from yet another healthcare company called inter Mountain
Health out of Salt Lake City, Utah. Stuff gets really complicated,
not just from a technical perspective, So empiric Health focused
on clinical analytics and used artificial intelligence to identify potential
(31:25):
irregularities in clinical procedures. So essentially, the tool was meant
to isolate instances of unwanted clinical variation so that healthcare
companies could address any problems early on before they become
bigger issues. By the summer of twenty twenty two, Olive
AI was in a totally different financial position because the
(31:47):
economy was no longer booming. Olive had potentially over extended itself,
so the company did what countless others did in the
summer of twenty twenty two, it held extensive layoffs, so
around four hundred fifs if the employees at Olive were
let go. Things however, did not improve, and so like Babylon,
Olive ultimately would begin selling off components of its own
(32:10):
business to other companies it was so it was essentially
getting broken down for parts, and this might be one
of the reasons that the Free Press of Columbus is
so critical of CEO Sean Lane, because the layoffs affected
many people in Ohio, and a lot of people likely
felt that leaders like Sean Lane were exploiting the products
(32:30):
of labor so that they and other investors could hit
the eject button while avoiding the worst of the consequences.
You know, if you actually play your cards right, you
could end up better off than you did when you
started the whole thing. And sure, a whole bunch of
employees and former customers might not be able to say
the same, but you got yours. Gush darn it. At
least that's the feeling I get when reading the Free
(32:50):
Press article, which I could be projecting here. I'm sure
the truth of the matter is far less cynical than that.
By how much I don't know, But like Babylon, critics,
including former Olive employees, argued that a lot of the
AI powered components weren't really true AI when you got
down to it, or they were extremely simplistic automated processes that,
(33:15):
depending upon your perspective, don't actually meet the threshold to
be called artificial intelligence. Now, I would say that's a
slippery slope, because defining artificial intelligence is deceivingly difficult. Heck,
for that matter, defining human intelligence is actually really tricky.
So is an automated algorithm artificial intelligence? And if not,
(33:37):
how complicated does the system need to be in order
to qualify as AI does there need to be some
sort of decision making component to it in order to
be AI. I don't actually have the answers to these questions.
You know, what's AI to one person might not be
AI to someone else, Which is kind of like the
legal definition of pornography in someplace is where it said
(34:01):
I can't tell you what it is, but I know
it when I see it. It's kind of that similar
situation anyway. With Oli, the problem was that once the
post pandemic boom had settled, the company was facing high
costs of business and revenue just wasn't keeping up. Hiring
freezes and layoffs in twenty twenty two were followed by
some high profile departures from the company. The chief financial
(34:23):
officer and the chief product officer both left by the
fall of twenty twenty two. Olive also saw its client
base Diminish providers began to shop around to some of
Olive's competition, so the company began to lose customers and
the ending was not yet set in stone. As late
as March twenty twenty three, Olive continued to raise hundreds
(34:46):
of millions of dollars collectively, the company raised more venture
capital funding than any other health tech startup in history,
but that was not enough to make the business model
actually work, so Olive sold off different parts of its
business to various companies, and also faced a lawsuit from
Ohio's state Economic Development Department because the company had failed
(35:11):
to live up to an obligation it had made in
order to provide a certain number of jobs in return
for the considerable tax incentives that it had enjoyed. So
on Halloween twenty twenty three, all of AI shut down. Now,
these are just two examples of Heck, it's just two
examples of digital health companies with AI components to it
(35:33):
that shut down despite the huge boom and AI investment.
If we extend that to AI startups in general, there
are tons of examples of AI startups that have had
to shut down over the last year or so. And again,
that's not necessarily an indication that the business itself was
(35:56):
a bad idea, or that the service or product they
planned to provide just had no place. That might not
be the case. AI is inherently a difficult discipline to
get into and make it work from a business perspective,
It needs to work really well. It needs to be
dependable and replicable, Like you need to make sure you
(36:18):
can rely on the results and that if you ask
the thing twenty times, you're going to get the right
answer all twenty times. That's hard to do from a
technical level. But also, as I mentioned multiple times, just
the expense of running an AI centric business is so
high that in order to make enough money to cover
(36:40):
all the costs of operation and then make profit on
top of that, it's really hard. You either have to
scale up super fast so that you're able to meet
an enormous number of customers around the world, or you
have to price yourself at a level where you're going
to see a return, but then you run the risk
of no one buying your product or service because it's
(37:00):
way too expensive. Yeah, it might be AI powered, but
why do I want to spend ten times more than
I would if I go with a human powered company.
It's going to get me reliable results, it just won't
be AI driven results. So yeah, we're still in this
world where AI it's got incredible potential, Like I can't
(37:21):
even begin to imagine the potential AI has to transform
how we do everything. If it's applied properly. But the
challenges of getting there are considerable, and they're not going
to be solved overnight. And it doesn't matter how flashy
an AI company is or how excited investors are to
(37:42):
try and get in on that particular gold rush. It's
not going to make the AI powered future get here
any quicker. It might actually slow things down. So, as always,
I recommend employing critical thinking whenever you encounter anything, honestly,
but particularly when you encounter information or news about artificial intelligence.
(38:05):
Use critical thinking because again, I do believe there are
ways where AI is going to make a positive difference
in how we go about doing different tasks. But slapping
AI onto something does not automatically make it better, just
as I would tell the hosts of the podcast The
Besties that throwing the adjective super in a video games
(38:27):
title does not automatically make it better. That's just a
general joke at The Besties. I listened to an episode
recently where they were jokeingally suggesting that if you have
super in the title, it must mean that the game
is better. So great show. By the way, I have
no connection to the Besties. I don't even know any
of the people who are the hosts of that show.
(38:50):
But if you like video game discussions, you should definitely
check it out. That's just a free plug from yours truly,
and again I have no connection to them. They're not
an iHeart podcast than like that. It's just a show
I enjoy. That's it for this episode. I'll probably do
more episodes about AI startups and kind of talk about
the challenges they face, because I really do think there
are some startups out there, including in the digital health space,
(39:13):
that are trying to do really interesting, important work. But
in many cases, I think the folks who perhaps are
the leaders behind those companies may not have a full
understanding or appreciation of how hard it's going to be,
and that ends up falling on the actual experts in
the field, the computer scientists, etc. To try and realize
(39:37):
a vision that is inherently extremely difficult to accomplish, not impossible, necessarily,
but very challenging. So I'll probably do some more of
these in the future, not so that I could just say, Haha,
look at these companies that didn't make it, but to
get a deeper understanding of why didn't they make it,
(39:57):
or for the ones that do make it, what's set
them apart, because I think there's some valuable lessons to
be learned there. In the meantime, I hope all of
you out there are doing well, and I will talk
to you again really soon. Tech Stuff is an iHeartRadio production.
(40:19):
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Hosts And Creators
Oz Woloshyn
Karah Preiss
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