Clinical trial can take upwards of 10 years.
It costs on average 1 to $2 billion per drug to get to
market. These are huge numbers and huge
timeframes. Writing clinical grade
documentation where the stakes are really high, we're talking
about human lives and human safety and things like that is
incredibly challenging. And it just staggers me that how
many people still do this in Microsoft Word, use Microsoft
Word to essentially model much at the author of model these
trials. Patrick Lung is the CTO of
Pharaoh Health and a former engineering lead at Google
Duplex. So we published a paper with
Merck that sort of. To analyze like what is the
benefit of using the FIRE study designer to optimize clinical
trial design? And we found that the savings
over the course of the lifetime of the trial.
Can be north of $100 million. We have customers using this
document generation system and they're super impressed with
documentation that normally takes potentially months and it
can be generated in a matter of, in some cases, minutes.
We're also in charge on building the Google Duplex, right?
Did you already use something like LLM or is it using a
different technology altogether? It didn't use LLMS at all.
It predated that. People started freaking out when
we launched this product because it sounded so lifelike.
And there were even some people, prominent names of the press,
who thought that we faked the whole thing.
And yet it wasn't using any of this modern LLM architecture at
all. It was using other methods that
were much, much simpler. What do you think people should
do in this kind of era where things moving so fast?
AI won't replace humans per SE, but people who are able to
effectively wield AI will definitely replace people who
aren't. Hello guys.
Welcome back to another new episode of the Technology
General Podcast. Today I have with me the CEO of
Pharaoh Health, Patrick Leong. So we have been hearing a lot
about AI development recently. Viral health itself is trying to
apply AI on, you know, clinical trials and life sciences and
health and all that. I think it's going to be really
interesting to understand how AI can be applied in this type of
work as well. So Patrick, welcome to the show.
Thanks, Henry. Great to be here.
Right, Patrick, I always love to invite my guests to share a
little bit more about yourself, maybe by sharing any career
turning points that you think we all can learn from you.
Yeah. I mean, I guess the beginning of
my career was very entrepreneurial.
Like I joined a company that was in the very early stages and it
went all the way to an IPO and then back down to, you know,
near 0 again duringthe.com boom. And so that was a huge learning
experience. And then sometime after that I
made the big decision to join a big company.
I joined Google back in 2007. And so that was a huge career
turning point because I never thought I'd find myself.
At a big company. But against all odds, I did and
I ended up there for over 10 years.
So I think it's really interesting as well.
Back then when you were at Google, right, you work on some
of the AI technologies like Duplex, right?
So I think since then you have been working a lot on AI related
stuff even before this Gen. AI era, right?
So what do you see in the last, I don't know, maybe 10 years or
maybe 8 years, right. So what are the some
advancements that you can see working on AI things right in
the past compared to now? So what do you see are the
differences? Yeah, I mean, it's easy to
forget now because this whole large language model revolution
has just completely taken over everything.
And so when most people on this planet think about AI, they they
really kind of thinking about large language models as opposed
to AI in general, like data science in general.
And So what I saw when I was back at Google was there was
already an AI revolution that was well in full swing, which
was mostly related to image recognition.
Like because processing power kind of passed a certain
threshold, we became much more able to interpret images.
I'm using AI. And so this resulted in all
sorts of amazing advances that started showing up and things
like Google Photos and other products like that.
But now all that's forgotten because it got completely sort
of superseded or overshadowed by this more recent large language
model revolution. But there have been multiple
waves here. And that's been really, really
interesting to see how. As processing power hasn't
increased, there are these new forms of AI that come out and
start really solving certain problems really, really well.
Yeah, it's interesting that we know LLM is like LLM or
generative AI, right, The bigger categories like the all the
things that people talk about regarding AI.
So in the past I remember people are talking about machine
learning, recommendation system, image recognition, just like
what you said. It seems like all of those kind
of like toned down a little bit. Do you still see advancement in
those area or is generative AI is like the go to AI strategy
that we are going as a human species I guess.
Yeah, I think it's pretty extraordinary.
How? General purpose and powerful.
These models have become and of course, you know, with a lot of
the image generation, there are different methods involved
there. Like it's not necessarily LLMS,
it's more like diffusion models, but just these multi model large
language models are pretty incredible, like going beyond
human language into things like genetic coding.
And of course, you know, computer coding, like actually
programming, has become a huge. Use case for.
These language models, and they originated out of the transform
architecture that Google and others sort of introduced.
A while ago, which originally was applied to the use case of
translation from one human language to another.
So from that initial use case, it's become much more general
purpose. So it's pretty incredible.
But I do think, I continue to think that there's always going
to be many, many use cases out there for which other forms of
machine learning that have been sort of largely maybe overlooked
during this whole LLM revolution are going to continue to be
really useful for solving certain other types of problems
involving particularly involving quantitative information like
predicting numbers and stuff like that.
Right. So back then you were also in
charge on building the Google Duplex, right.
So when I remember back then when it was introduced in Google
IO or something like that, that was really, you know, like
incredible to me. Did you already use something
like LLM or is it using a different technology altogether?
Yeah, I mean, it's really interesting because it didn't,
it didn't use LLMS at all. It predated that.
And so in some ways, that was sort of like AI wouldn't say a
prototype. It was almost like sort of a
preview of what was to come because people started freaking
out when we launched this product because it sounded so
lifelike. And some people like the press
had this huge media frenzy around this, right?
Because Google introduces some kind of AI that sounds like a
human being. And it's like.
Oh, it's unethical or oh, the game over, you know, the bots
are going to take over the world.
And there were even some people, some, you know, prominent
members of the press who thought that we faked the whole thing.
So it was super interesting to be involved in sort of like a
media frenzy situation like that.
It was on the Good Morning Show, it was on, you know, Colbert.
It was like really hit this very, very big nerve in popular
culture. And yet it.
Wasn't using any of this modern LLM architecture at all.
It was using other methods that were much, much simpler.
And so since then it's just gotten way crazier in terms of
what these systems are capable of.
But it was really a privilege and just so much fun to be
involved in a system that was very much the forefront of the
time in doing this kind of thing.
Yeah, I think Google itself has to rebrand it, you know, those
kind of things like becoming like Google Assistant now Google
Assistants, becoming like Gemini, right.
I think all those speakers that they introduced probably is like
using some kind of things that you use in Google Duplex as well
because the human tone are so natural when I chat with like
Gemini using voice, right. So I think it's really cool
technologies, you know, that comes out of those innovations.
And today specifically we are going to talk how to apply AI in
health or life science and clinical development.
So your company, Vero Health, if you can share a little bit more,
how does Vero Health use AI in terms of, you know, like solving
your problem? First of all, just in brief,
Firo has developed the SAS platform for designing clinical
trials. So this is a really intensely
complicated process that can take, you know, like a clinical
trial can take. Upwards of 10 years.
It costs on average 1 to $2 billion per drug to get to
market. Like these are huge numbers and
huge time frames. And so we developed a platform
that allows essentially biotech companies and pharma companies
to design better clinical trials.
So we're applying. AI in a couple of different
ways. First of all.
One of the big time and cost thinks in the whole development
process is clinical writing. So writing really complex
clinical document like the clinical protocol that
essentially defines the drug trial like so it lays out the
whole design and the schedule and all the things that need to
happen, all the details. So this is a really complex
document that can be north of 100 and 5200 pages and typically
takes an entire team of people to write.
It takes months and. We.
Starting last year, we started applying large language models
to essentially automatically generate various sections in the
clinical protocol document and. You might think, well, what's
the big deal? You know, it's easy to ask GPT
to go and generate a document, right?
I would do a little time. It's a write better emails or
they're, you know, do my homework or this particular.
Use cases, right? But it turns out.
That writing clinical grade documentation that passes master
with regulatory groups like the FDA and where the stakes are
really high, we're talking about human lives and human safety and
things like that is incredibly challenging.
And so we went through many iterations when we started
designing this system to try to figure out the best way to
actually produce really high quality clinical output that
meets all the needs and requirements of a clinical
protocol document. Yeah, this resulted in a lot of
new architecture and new processes and all sorts of
interesting things in order to make that happen.
Right. It's very interesting that you
mentioned applying like generative AI or LLM to solve
something that is, you know, highly regulated.
And it is also kind of like mission critical, right?
So to speak, Right, Because it involves people's lives.
So people, you know, these days know about the danger of
hallucination and all that. How do you actually tackle these
kind of things, applying this to kind of like this highly
regulated and mission critical kind of a problem?
Yeah. I mean, at first we sort of
naively assumed that we could just adjust our prompts.
You know, every time we saw a hallucination, we're like, oh,
we need to go back and change the prompt.
But it turned out that what we really needed was to evaluate
comprehensively the output systematically, right.
And so when you generate. Say a PK sampling section or ECG
section. Like some highly typical part of
the protocol dock, you need to actually evaluate the output
according to a whole bunch of criteria.
And do this. Per subsection, because
essentially each subsection is so different and has different
sort of technical details that are fed in through our fire
study designer tool. So we have this whole staff
platform that's modelling the trial in great detail and we
pass that data into the LLM. To generate the protocol
document sections. And so we found that we had to
really rigorously sort of query the results and say, well, did
you include this? Did you have this?
Tone, did you sort of like phrase this in this way?
And we have this very specific checklist for every single
subsection that we run to make sure that the quality is high.
And we've also designed this in such a way that customers can
actually make their own checks. So if they have additional
requirements that are particular to them, they can augment or
change the ones that we ship our product with.
So we've made it very highly configurable to match the
customer's needs. So this is what we found was
needed. And over time, that initial
evaluation architecture has evolved into more of an agentic
architecture where there's all sorts of different agents that
are examining the content of the generated clinical protocol
section and looking at it for stylistic consistency with the
rest of the document and tone and deep level of detail and
formatting and stuff like that. In addition to the factual
content to prevent hallucinations and also.
In addition to. Hallucinations.
We also found that sometimes the LLM just misses things, so it
doesn't make things up, it just misses things.
And so we have to sort of check for that as well.
So there's a whole bunch of examination that occurs once you
generate the output, and then you feed that feedback from the
evaluation models back into another iteration of the
generation cycle. O we developed this whole
architecture that's way more complex than just sort of
sending queries to chat GT in order to get the system actually
working. Yeah, you mentioned about
missing, you know, information and also again, like the danger
of hallucinating, right. So how do you actually do these
kind of checks? Is it like still human involved,
like at the end someone who is, you know, maybe capable enough
or expert enough, like reviewing the output?
Because I would imagine, right, these kind of documents is
highly technical, right? Sometimes you probably need a
little bit more time to research whether it's factual or accurate
or not. Or do you completely rely on AI
agents to do that for you? And if so, like how do you to
build this kind of like evaluation using AI?
Is it like something like eval model that people are using to
solve, you know, certain kind of a programming challenge and
things like that? Yeah, I mean in this.
Respect. What we've built with clinical
protocol writing is kind of similar to other AI systems I've
worked on in the past, where in the early stages when you're
first getting the AI model to kind of start producing
interesting content, you need to be very, very hands on and have
a human examine everything. With a fine tooth comb.
And then after a while you can codify that feedback into these
checks and into queries that you can then use to sort of automate
the process. And after a while, you build
enough confidence against a range of different inputs, you
know, looking at different types of therapeutic areas to make
sure that the content is right. Because looking at a cancer
trial like an oncology trial versus say an immunology trial
or infectious. Disease trial, it's just kind of
really different. And so we want to make sure that
the models we build are applicable across a range of
different types of clinical trials.
And so once we reach a level of confidence that, oh, it looks
like, you know, in the 80%. Case the content is pretty good
then we're confident enough to share this with our customers,
our early adopted customers and sort of get them to evaluate
themselves and they probably come up with some issues as
well. And then we sort of gradually
improve the quality of the model using that process.
But we have a quite an in depth process involving engineers, you
know, data scientists, clinical writers, QA, all sorts of
different people involved in this to sort of bootstrap the
process and get everything running.
And then after a while it becomes more automated as we
refine the model. Thanks for sharing that.
So apart from like, content writing, right, is there
anything else where you apply AI as part of the work?
Oh, yeah. So we're also looking at
actually developing what we're calling an AI research
assistant. And so the idea is that, you
know, right now actually designing the clinical trial is
quite laborious, like choosing what kind of activities you want
to include in the schedule, designing the population schema,
all these kinds of things, objectives and endpoints.
These will require a lot of care and in many cases a lot of
research like going out there on the Internet, you know, checking
out the latest clinical research that being published, taking a
look at identifying and taking a look at comparable, the trials
that are out there, learning from them, reading through
written hundreds of pages. This is super laborious and we
can actually automate a lot of that.
So we are applying AI to essentially help to
automatically identify trials that are similar to the one that
you are working on. And then you know, aggregate
metrics and stats do comparisons.
Look at what happened with these trials.
Did they require amendments? Like why were they able to
enroll patients? Were they able to, you know,
stay on track? Did they result in a successful
outcome or not? And so clinical developers who
are or clinical scientists who are working on these trials can
essentially really accelerate the process by which they come
up with an optimal model. And of course, you know, we can
provide insights, we can say, oh, here's your patient burden
of this proposed trial. Here is your, you know, the site
complexity and cost lots of different metrics that help
guide what you're doing and help you come to the right balance of
patient experience, site feasibility, and overall cost
and time to market. So this is a really sort of
multivariate kind of thing that people are trying to optimize
when they put together these trials and it just staggers me
that how many people, many groups still do this in
Microsoft Word. It's like sort of my sort of
catch cry is like not even Excel.
You've used Microsoft Word to essentially model much author of
model these trials and I think that you know, people deserve a
better tool than that. So that's what we're developing
with the help of AI is like really modernizing and shifting
the way that people develop these and design these trials in
such a way that it makes it much, much quicker and with much
better outcomes. Yeah.
So since this is like a very highly niche, you know, specific
kind of a problem domain, right. So I'm just assuming like people
know things like Chachi, PT and Gemini and all that, right.
It's more like a general purpose AI models.
Do you actually have to fine tune or even build your own
model to work on this specific niche?
Because like one of the most important things when using
general purpose AI, it's like, what kind of data are they
trained on, right? For example, if you've never
seen this kind of problem before, I'm sure AI will
hallucinate even much worse than, you know, if you train it
with specific model and data. So do you actually have to tweak
or fun tune the model or even come up with your own model?
Yeah, we haven't found the need to do that yet.
So the good news is that a lot of the major LLM AI vendors like
Google and Anthropic are actually focusing quite a bit on
the life sciences space and producing models that are kind
of specific to that domain. And so we've designed our system
in such a way that it's kind of agnostic to the model, like
we're not fixed on GBT or Gemini or Anthropic or anything like
that. And so because we have this
evaluation system, we can pretty easily evaluate new LLMS and
figure out which ones are going to produce the best clinical
output. I would say it's quite possible
that we might in the future want to potentially have our own
model with respect to maybe vectorization like which is a
process that all the text kind of goes through before it gets
indexed. So we built this kind of
retrieval augmented generation RAG system that allows customers
to sort of upload documents that we can then parse and use to
generate certain pieces of the protocol document that require
really technical information. And so that's an area where we
might want to develop our own vectorization scheme that's
really oriented around clinical terminology.
But so far we haven't. Seen the need to even fine tune
an existing model. Even fine tuning these days can
be quite an expensive and laborious process.
And so we haven't really seen the need to do that yet.
What we've really seen is that this evaluation model can
combined with the ability to actually sort of fine tune
prompts is enough to be able to produce clinical grade
documentation. It might change in the future as
we get into different documents and and domains and so on, but
so far that's been really good for us.
Yeah. So what I know when you do
clinical trials, right, like you mentioned, it could be, yes,
right. It could be a decade or even
more. Not to mention, you know, like
having to do the testing and wait, you know, for the effect
in multi years. So using this kind of
technologies, what do you see potentials, time savings or
maybe even effort, right. You mentioned like $2 billion
just to finish the whole clinical trial.
So maybe what are some of the things that you think can be
reduced or optimized by having this kind of AI tools?
Yeah, I mean, the interesting thing is that when you're
talking about a project that takes, you know, upwards of 10
years in many cases or more to execute, it means that small
changes in the design upfront can actually result in huge cost
savings. So we actually published a paper
with Merck. Maybe we can include a link to
it. It's on my.
It's somewhere on my LinkedIn profile that.
Sort of analyzed like what is the benefit of using the fire
study designer to optimize clinical trial design?
And we found that the savings over the course of the lifetime
of the trial can be north of $100 million.
So obviously this was a really big Merck is a huge, you know,
top ten, top 20 pharma company and so on.
And so that they do these really significant trials, but it just
gives you an idea of what's possible, right?
And this was even before some of these AI features that we have
coming up like clinical writing and so on.
So the true savings actually could be even more than that
when you factor in the time savings as well of accelerating
the clinical writing process and making the output better.
Because getting to market faster is hugely, hugely valuable to a
pharma company, right? Like every day is like hundreds
and hundreds of thousands of dollars of additional revenue
that you can realize sooner when you divorce the drug faster.
So it's not only cost savings, it's also getting to market
faster with the new treatment. So we're really excited about
that because there's also the potential to really over time,
as this technology becomes widespread to really sort of
like for a smaller players to come in here and really try
things out. And it's going to kind of enable
a wider range of different treatments and trials to become
more feasible. And we're really excited by that
because there's so much innovation going on in the very
early stage drug discovery realm, but it gets sort of
blocked or delayed when it comes to actually going through
trials. And so we want to really widen
the pipeline there. And so hopefully there'll be a
lot more treatments for more conditions and lives saved and
suffering alleviated all the good things.
Yeah. And hopefully reduce the cost of
the kind of the medicine that is produced out of the clinical
trials as well, so that people can get much more options in
terms of availabilities and also like the cost is not less
expensive. So you mentioned about
regulations. So I think in some parts of the
world, they are still kind of like cautious about applying AI
technologies again, especially if it affects a lot of
population. So what do you see some of the
stance from regulations or do you even have to convince them
hard to actually ensure that the process is robust enough?
I think that by their very nature, regulatory bodies are
very, very oriented around evaluating the quality of what's
presented to them. And so at least in my mind, like
if the quality of the output is good, at least as good as human
generated protocols and we strongly believe that it will
be, then the regulatory bodies will be OK.
I think there are various risks involved in using AI.
There's many, many different types of risks.
And so we can mitigate a lot of them.
Like for instance we are running our AI models in a sort of a
private. Environment where data is not
shared with any other groups and so on.
So we don't have any of those risks of leakage and people
being able to kind of discover what other companies are doing
or submitting. So there's that risk that we can
mitigate. I think there's a bunch of
others involving potentially bad actors coming in and trying to
sort of discover things they shouldn't be discovering by
manipulating queries and stuff like that.
That's mitigated by the fact this is an enterprise tool.
It's not a consumer tool. And so only people who have
signed up as Faro, you know, customers will be able to use
the. System so we just have to go
case by case and look at all the different risks involved in AI
and mitigate them and we have AAI governance policy that we've
been working really hard on to sort of like really.
Alleviate our own customers concerns because however
concerned the FDA is, you know, our customers are even more
concerned about all these things because it's their crown jewels,
it's their intellectual property that they're, you know
essentially developing using our platform and so we take this
very seriously and. We want to actually at some
point probably publish our AI governance policy so that other
people can learn from it and we can help raise the bar across
the whole board in our industry because we just think this is
really important to. Everybody to get right.
Thanks for sharing that. So any kind of a cool success
stories that you have done so far, you know, with Farrow or is
there anything that is already applied in the, you know, real
world or in the life science industry?
Yeah, I mean, I think that again, the MOC study is sort of
what we have as far as publicly available case studies of just
how impactful this technology can be.
I would say just anecdotally, like we have customers using
this document generation system and they're super impressive how
quickly it's able to come up with documentation that normally
takes potentially months and it can be generated in a matter of,
in some cases minutes. That's a startling extraordinary
kind of multiplier in terms of productivity.
We're just really excited about that.
You know, like, I think that in some ways the industry is
waiting for a really widespread and impactful application of AI
because often times when you get past the surface level of like,
oh, I play with GBT and, you know, it's really cool and
interesting. Like once you really start to
get serious about applying AI, then that can rapidly turn into
disappointment or sort of like, oh, you know, it doesn't go deep
enough. And there's all these stories of
people who start building with AI, but then they kind of get
disillusioned because it turns out to be way harder than they
think. And so that's where I guess, you
know, companies like Faro come in, is that we'll take it to the
next level. You know, we will figure out all
the hurdle and the challenges and the risks involved in
actually applying AI to do something really impactful, like
want to make clinical, you know, protocol documentation or like
optimize a trial design. And so that our customers don't
have to do that themselves because that requires a lot of
data science expertise, software engineering expertise, clinical
design expertise, and so on that they may not want to sort of
invest in becoming a tech company themselves.
And so we're really excited about that.
Yeah. So I think these days some
people also kind of like envision the AGI thing, right?
That could happen in some years. I don't know whether it's short
term, medium term, long term. So for someone who has worked on
this field, AI fields for quite some time now, how do you feel
about this AGI? Is it going to happen?
Are you bullish about it or do you have some kind of
scepticism? So maybe share a little bit of
your thoughts here. Yeah, it seems like, you know,
in the past, everybody thought AGI was really, really far away.
Like the joke is that for the last 50 years of, you know, AGI
has been 20 years away. So so, you know, sort of like
always in the future. But now there's a lot of very
prominent people in this field who believe that it's actually
only a matter of like three to five years away, maybe even
shorter. Who knows?
And I am not in that camp. Like, I just think that there's
a test out there called Arc AGIARCAGI and it's sort of
presents itself as being like, oh, this is a test, you know,
for AGI. And you look at what this test
actually involved. And it's the kind of thing that
many of the questions are sort of like a bright kid could
answer these questions, you know, like moving blocks around
on a grid, you know, in such a way that the block sort of fills
in a space, stuff that smart kids should be able to do.
And I sort of to me, and you know, there's systems that is,
that is gradually getting better at this, but it's not yet human
level. And I just to me, that just
means that we're so far away from having something that's
genuinely creative and intelligent.
Now, I will say that there have been really brilliant moments in
the history of AI, like if you remember, you know, Alphago,
which was this non LLM based system was using reinforcement
learning, was using other methods of AI that are now
completely sort of forgotten in the wake of the LLM revolution.
But Alpha Go there was at the moment when it was playing
against the world human champion and it made this move that
nobody in the 3000 year history of this game had ever seen
before. And people were just all the
experts were like gasping like, Oh my God, this is incredible.
What a brilliant move. Like how did I come up with
that? And so, you know, AI can, under
the right conditions, be capable of behaving like a genius.
But it's such a special that was such a specialized example.
And so I'm sure as time goes on, there'll be more examples of
that where, oh, there's a flash of brilliance and AI came up
with some mathematical proof that was beyond the, you know,
like what any scientist has come up with.
But I think in terms of just having an intelligent
conversation and really being inspired and looking at a piece
of art and being truly like this belongs amongst the classics, I
think we're a ways away from that.
But I could be wrong. I mean, this definitely the
progress even since these live language models are first
introduced has been really nothing short of amazing in
terms of the increasing capabilities and so on.
But I think AGI in terms of like this is a peer to humanity.
This is a, you know, like a system that we can have a really
truly intelligent conversation about life and philosophy and
things like that. I'm not so sure.
And I sort of, I guess I come down on the young Likun side of
things where just knowing a little bit about how these
systems work, it is sort of like a super glorified, super, super
well read autocomplete. And I know that there are
efforts out there to introduce more real world knowledge and
factual information into these LLMS to give them some grounding
in reality. But I think that's going to be a
long path to make sure that those that grounding is actually
sort of super accurate and meaningful.
But we'll see, Yeah. And I mean, those kind of the
LLM Gen. AI leaders, thought leaders,
right, think that maybe the path to AGI is using LLM.
I'm personally also not very convinced since simply because
of the way it works right at so many probabilistic thing that
could go wrong as well. So do you think LLM or
generative AI kind of things is going to be the path to AGI?
Or is it more like combining all different, you know, AI, machine
learning, kind of like capabilities that we have?
And I haven't really heard about people, you know, trying to
combine, combine different models from, you know, LLM or
maybe other things, neural networks and all that being
combined to kind of like solve a particular problem.
So do you still see some rooms where we can apply, you know,
more AI advancements to the part of AGI?
Yeah. I mean, I find it hard to
believe that an LLM based architecture alone would be
capable of producing AGI. However, as I said before, you
know, there are groups that are looking to merge in knowledge
systems that are aware of facts in the world and how they relate
together. Kind of going back to some of
the really much older architectures for AI where they
were attempting to like CYC, where they were attempting to
actually model reality, like create this big huge knowledge
graph that links together all these different concepts that
collectively model reality. This was sort of a path that AI
took a long, long time ago that was kind of abandoned when deep
learning came along in favor of a more statistical probabilistic
methods, including LLMS. Ultimately.
So I think, you know, I can't help thinking and I think I'm
not alone in this. I'm pretty sure well others
might agree, like some kind of Harvard where we have this
fantastic language generation capability of the LLMS combined
with an actual. Sort of factual.
System that helps to guide the LLM to not really say stupid
things sometimes that ignore reality.
And we'll just have to see how that goes.
You know, like it's one of those things where the more computing
power and the more efficient these algorithms and
architectures become, the more things we can try out.
And of course, there's a whole wild card of quantum comuting,
like once quantum comuting reaches a level of maturity
where it's able to be practically applied to this and
all bets are off as far as what's possible.
Yeah. So at one time, I feel excited,
but at the other time, I feel a little bit scared as well.
You know, so many people are thinking about jobs being
changed. You know, people's roles are
going to be diminished and all that.
And obviously, you also have dealt with this kind of worries
as well. What do you think people should
do in this kind of era where things moving so fast every
other day? I think you might have heard
about cool things being applied using AI to improve
productivity, remove redundancies and all that.
So, yeah. What do you think people should
do in this era? Yeah, I think first of all,
maybe a little bit of an inspirational story.
Like I love to give this example of radiologist.
So, you know, seven or eight years ago, Geoffrey Hinton,
who's a brilliant guy, like one of the forefathers of deep
learning, one of the people who really helped make this whole
AI, the first AI revolution really happened.
He made this prediction that, you know, there would be no more
radiologists within a few years, that we should just, you know,
forget about radiologists because they're going to be
replaced by computer vision based systems.
And Fast forward to today and there's more radiologists than
ever. So how could that be?
And the answer is, well, you know, radiology is a high stakes
kind of field in the sense that there are human lives at risk
here. And so there is a need for a
human in the loop. And the fact that we automated
so much using machine learning means that essentially there's
just more and more people who are out there looking to
actually have radiology based procedures performed.
Like these days, if you get an injury, it's like, Oh yeah, just
get an MRI. You know, like just get a
diagnostic MRI to find out if you might have some sort of
problem. Like it's.
Become so commonplace that could not have happened, I don't
think, without these computer vision sort of advances.
And it's resulted in actually more radiology jobs because
there's still needs to be a human in the loop.
And so it's kind of a volume thing where even though a lot of
tasks would have made it, the volume increased.
So that's point #1 and I think point #2 is there's this quote
by, I think a former CEO of IBM saying, you know, AI won't
replace humans per SE, but people who are able to
effectively wield AI will definitely replace people who
aren't. And so if you're listening to
this, if you're going to take anything away from this whole
conversation, that would be really immerse yourself, like
look into how you might be able to use AI to make your own job
better, to make the way you do your job better.
There was a study, actually a very recent study published by
the Harvard Business Review called the Cybernetic Work
Companion, something like that. If you Google have a business
review and cybernetic, you'll find it.
And it's super interesting. Like they actually quantified
this and said, oh, you know, somebody who's with the help of
AI, suitably trained, can actually perform the work of two
people. And I think that actually might
be sort of higher than that, but it just goes.
To show like wow, you could be at least twice as productive
potentially using AI so. Figure out how to do that,
because if you don't, someone else you know might.
And so that's what I have to say about that.
Yeah. So definitely you have to give
it a try, right? And know what are the
possibilities? Because sometimes like even out
of our imagination, our current imagination, right, you wouldn't
think that these kind of things can be solved by AI or some kind
of a tools that can actually shortcut, you know, the time and
effort required, right? So knowing just that
possibilities, I think it's also really important and especially
if you have used the system right.
I mean, as you can imagine, we've been really looking into
the use of AI internally, not just in our product, but
internally the way we developed our technology and our software.
And at first I was sort of a bit of AI would say a sceptic, you
know, thinking like, hey, it's one thing to create a proof of
concept or an early stage prototype using AI, but actual
production code, no way. You know, we're still going to
need a whole bunch of software engineers doing things pretty
much the way they've always done them to do that.
But now I'm not so. Sure, like we've had engineers
do extraordinary things. Using the latest version of
these code generation tools. And I really am starting to
fully believe that the Harvard Business Review articleized, I
have mentioned before about how people can be twice as
productive. It absolutely holds for software
engineering. And so, yeah, I mean, we're
practicing what we preach. We're using AI to develop
software a lot more, you know, productively and faster than
before. And it's a pretty exciting time
because the underlying tools also are evolving so fast.
We're finding that the latest versions of the code.
Generation tools are just way more capable than the ones even
just from a few months ago, so that's also really exciting to
be surfing that wave of advancement in AI in that way as
well. Yeah, personally, as someone who
have been around in the industry for quite some time, right, I
find myself during this transition also a little bit
skeptic at once, but actually seeing maybe the junior people
doing stuff that I couldn't imagine.
Sometimes I feel like, wow, amazing.
Like sometimes I'm having to, you know, switch my perspectives
to be more optimistic rather than skeptic, right?
I think this is also maybe some challenge for those of you who
have been around in industry because we used to think this is
how we solve things. But now I think the
possibilities are there, right? So as long as you can imagine,
you give it a try and maybe I could help you in some parts of
the job. And I think your productivity
can be proof as well. So you mentioned about
engineers, right? So let's switch a little bit to
that side. So is there any difference
managing or leading AI engineers versus typical software
engineers, You know, like things like back end, front end and
those kind of things? Yeah.
I mean, even before the LLM revolution came along, it's
always been quite different managing data scientists or ML
engineers as they sort of used to be called, right?
Simply because a lot of what you're doing is exploration.
Like, I love what you're doing is you had this idea like, oh,
you know, we can use data science to solve some problem,
but it's not like traditional software engineering where you
have this idea, you design it, you specify it, and then you
sort of build it all and according to spec.
And it's a very deterministic process.
With data science in general, you know, you're just going to
find out, like, is this feasible?
OK, I have some idea. I can predict, you know, let's
just say I can look at the whole bunch of historical trials and I
can start to predict things like amendments or outcomes of any
kind. That's a thesis that you need to
go out there and just test kind of like science, right?
That's why they're called data science.
And so that inherently is quite different because you have to
embrace more uncertainty. Now I think maybe people also
want to sort of know what about all this AI vibe coding or AI
enabled coding? Like how does that change?
And I think that what it does is essentially it's a force
multiplier for people. So maybe to build a prototype
these days, you don't necessarily have to be a
software engineer anymore. You can get to a certain point
without any software engineering skills whatsoever.
So that's kind of different. What we're observing out there
actually is that there's a lot of entrepreneurs and even sort
of like groups that are out there selling who are basically
building prototypes where before they would have been presenting
marks or, you know, conceptual sort of descriptions of what
they intend to build. And now it's like, no, we
actually just went ahead and built it because these coding
systems, these automated coding systems make things so much
easier. So there's this huge shift
towards essentially what a lot of the people who are really
behind the agile process we're already advocating, which is you
just got to prototype stuff, try it out and test it with real
customers. And I think in the past, a lot
of people didn't do that because it was just too much work.
It was like, well, you know, prototyping, it just takes so
much work to try to make all these ideas real, like, and then
potentially throw them away. It's just like we don't like
throwing things away. Whereas now the bar is so much
lower as far as what the amount of effort it takes to produce a
working prototype. That it's much more.
Feasible to do what people like Marty Kagan, you know, out there
say, which is just pretty like a whole bunch of stuff, you know,
within two weeks or less. Before you fall in love with the
idea, just test it out and throw it away if it doesn't work.
So that philosophy and that process will become way more
feasible with AI, which is really exciting too.
Yeah, definitely makes sense, right?
So the process, the iteration now can be much faster and even
people are not skilled enough in development now can do software
development in some sort of form, right?
So as Acto yourself, you mentioned about vibe coding.
What is your view about vibe coding?
Do you see your engineers doing some kind of vibe coding?
And if so, right, is there any danger that you foresee and how
do you actually mitigate that? I think vibe coding is great.
Meaning just using these AIF systems to generate code that
you don't even necessarily look at, you just prompted and
prompted and prompted until something works.
I think that's great for doing proof of concept initial 0 to
one kind of prototyping. I don't think that's a viable
path to actually building enterprise software simply
because when things go wrong, as they inevitably will, you know,
with AI generated code. Like I started playing with some
of these vibe coding tools myself and very quickly I dug a
hole. Like once I passed a certain
threshold of complexity, it just became really hard to add new
features. They had this magical button
called like try to fix it. And I was pressing that button a
lot. And so I don't know, like I, I,
I think it's got a place and I think it's wonderful that people
who don't have a computer science background or haven't or
not, you know, experienced software engineers can now
actually build technology like that's magical, that's
incredible. But it only goes so far.
And I think in the future, sure, vibe coding will become more
powerful. It will become better.
But I do think at the end of the day, if you are producing
enterprise grade software that's used by real paying customers,
you're going to want to have people who actually understand
the technology, even if they're using AI to help them code.
To actually fix things when they go.
Wrong and to. Ensure that things are designed
in a good way. At least that's my view.
Yeah. So you mentioned like building
enterprise system, right? So the things that are complex
and maybe need to evolve over time, right?
Because I still don't know whether you know, just simply
doing vibe coding can actually evolve your system, you know, in
a predictable manner, right? So rather than, you know, keep
prompting AI until it gets fixed, right?
I think the code that gets produced also can be quite
terrible if human are looking at it, right?
Because simply they don't maybe adhere to a certain design
pattern. There's no refactoring done in a
concerted effort, I guess. So I think vibe coding is
definitely is wonderful for things like POC or things that
you're not looking at or something that is more siloed.
I guess that you can just change the whole thing once you think
the quality is not there. So I think also another thing
when we hire engineers, right? Do you see any kind of
difference these days in terms of, I don't know, attitude,
skill set or non-technical skills that you see when you
hire AI engineers? Yeah, I mean, I think that we're
still at the early stages of this.
And what I do observe as a sort of a hiring manager is there's a
lot of resumes out there with AI on them because everybody wants
to be as perceived as someone who's versant in this because
they know that there's a lot of demand.
But when we start interviewing, when we start testing people's
actual coding capabilities, because this is still relatively
recent, it's still kind of difficult to find people who
really do have experience with this and who have demonstrable
ability in this area. And so I think it's going to
shift over time, just like in the old days, you know, it was
kind of hard to find like way back in the 90s was hard to find
people who knew HTML and he knew like how to build full and then
later to who really knew how to build full stack applications.
And before too long, within a few years, everybody did.
It was the norm. And so I think this is just a
natural technology adoption curve where we're sort of moving
into a more of a mainstream situation where everybody's seen
the value and. You know the majority of.
Software engineers out there are going to be conversant in this
technology and before too long it'll be ubiquitous.
But we haven't quite seen that in the hiring pool yet.
But I think, as I said, it's sort of inevitable.
Maybe from your existing engineers that you have in the
companies, Do you see someone that stood out simply because
you know they can combine all these possibilities and also
with a certain attitude, do you see some kind of persona of
engineers that stood out that you think you can share with us
here? Like, what makes a better
engineer now that AI technologies are there?
Yeah. I mean, I talk a lot about
career development and about the qualities that I've observed
over the many years that I've been working in this industry
that lead people to become successful and sort of more,
quote, senior in some way. And one of the big ones is kind
of curiosity. There's this thing kind of
inherently excited about learning new things and trying
things out. And that's a quality that I
think really, really serves people well in this new world of
AI, because a lot of it is kind of unknown.
It's like just trying to apply AI in ways that.
Have been done before. Like we're doing here at Faro.
And so I do think that that's an example of a quality that is
really a strong one. And I think also people who can
span multiple domain by people who are able to think in terms
of product, like what makes a good product, what makes a well
designed product like usability wise, along with the ability to
sort of decompose that into commands that you can give to
the AI that it's going to understand.
So this is a combination of different skill sets.
It's not just like you do a computer science degree and
you're good. It's more like, hey, there's a
part of this which is customer facing, like knowing what
customers really would respond well to this.
Part of this, which is communication, being able to
kind of express those requirements well, like a
product manager does. And it's part of this which is
like UX designer and of course part of it, which is straight up
programmer. So I think the people who can
think cross functionally also will do really well in this new
world because they now have the force multiplier, you know, in
the form of these automated coding assistants.
And so on to really be able to do the work of multiple people
potentially. Yeah.
So definitely, those skills are still kind of like the most
important things, right? Especially if you are engineers
listening to this and worrying about your jobs getting
disappeared simply because getting replaced by a IS.
So how about leaders themselves? So I know some leaders maybe
have decades of experience. Maybe they are up and coming
leaders as well, like engineering managers and all
that. Do you see a certain skill set
or attitude that must change in this EI era as well?
Yeah, I do think that every time there's a technology disruption,
it can be a little bit scary simply because perhaps the
processes and tools and behaviours that have served you
really well in your career to date, it might be sort of under
threat or, you know, about to be transformed.
And that can be exciting. And it can also be kind of scary
and threatening like, Oh my God, what do I do?
I can't depend on the same set of skills and habits and
behaviors that have got me to where I am in my career.
And I think that the people who can sort of take that
uncertainty on board and re step into the unknown are the ones
who can thrive. And I think that applies at a
company level as well. Every single company out there
at this point is wondering like what do we do about AI?
And I think it's the ones that are bold and are willing to
disrupt themselves. Like I, I just spoke to the
company earlier on this week and said we're essentially
disrupting ourselves. Like we started this company as
a traditional SaaS provider. Now we're becoming an AI company
and that is a process of disruption.
And I would look at Apple in the years of Steve Jobs, if you want
to look at an example of a company that's has the courage
and the sort of the fortitude to disrupt itself repeatedly like
Apple did that you remember, like the iPod that used to be
the best thing ever, like Apple disrupted their own product with
the iPhone. And I think that that is the
kind of thinking that is needed here when this new technology
wave comes along at the company level as well as the management
individual leader level is giving your teams.
First of all, have possessing that courage to step into the
unknown yourself, but also inspiring your teams to do so
and saying hey, this is you know, if we don't do this,
someone else will and we will get disrupted.
And you can look at the history of this, like if you look at
things like the Innovator's Dilemma, you know, Clayton
Christensen's book that talks about this is like a natural
law, like companies get disrupted because not all of
them have the fortitude to actually jump on new
technologies and disrupt themselves.
Yeah. So I think thanks for pointing
that out, right? So definitely it's something
that for some of us leaders, right, have to think about
disrupting yourself, disrupting your team and disrupting your
company, right? So I think those things can be
healthy, especially if it can also revolutionize the way
things are done. So how about the juniors here,
juniors who probably are thinking whether software
development is going to be their career or someone who is
studying software engineering. But I think that they now have
to switch. Do you still think there's a
room for them to actually enter the industry?
Because I heard so many times that people say simply because
you can be more productive now, maybe we need less junior
engineers. So what is your view here?
I think that there's never a shortage for people who are able
to create technology, and AI makes that easier.
And so, yes, you could argue the field of programming is becoming
more commoditized because of all this automation.
But like I said before, AI can't necessarily take the place of
having good product sense or having the combination of
programming skills and design skills and so on.
There's different ways of framing this, but I would say
thinking of it as being a force multiplier, like you're able to
do more, whether it's as an entrepreneur or whether it's as
an engineer who's part of a larger organization.
If you're able to wield these tools well, that'll serve you
very well. And I think we're still also in
the early stage of this. So if you are able to, for
instance, start a site project, a personal project, and put that
in GitHub or put that on your resume and show that you have
the ability to do this. Even if it's some personal
project you just happen to be really excited about, definitely
do that. Because that shows that you can
do it and that shows that you can adapt and that you have some
level of mastery or facility in this area and that will serve
you well as you look for jobs. Because the employers that we're
looking for people who can do this now and they're still kind
of hard to come by. Maybe not for too many more
months or years, but there's an opportunity to jump in there if
you really are proactive and potentially stand out from.
Other candidates because of that.
Yeah. So I think definitely don't get
this heartened especially if you're really passionate about
software engineering, right, creating application products
and even like enterprise software still kind of like the
go to things that you are passionate about.
So I think that don't get this heartened and maybe a try out
stuff just like Patrick mentioned.
So we have talked a lot about you know, AI in clinical trials,
AI in general and also software engineering and engineering
teams. Is there anything else that you
think you want to share with us today that we haven't talked
about? Maybe just coming back to the
theme of career development, like some of the most successful
and interesting careers I've seen of all the people I've
worked with have been people who really focus on just trying
things out. There.
Was this one maybe an inspirationalist degree?
Like there was this one really junior product manager I worked
with when I first joined Google and I encouraged them to sort of
become a user of our application, which at that time
was this kind of e-commerce product.
And so I said, hey, you should probably sign up as a merchant,
start your own little sort of fake business and kind of try
out the product. And he ended up really getting
into e-commerce. Like he built this company that
ended up becoming successful and he ended up sort of becoming AVP
at Shopify. And I just kind of, I was giving
my talk earlier on this year at the company on career.
Development and I actually spoke to him and just said, hey, just
how would you? Sum up like your success because
it's like such an incredible. Career path that he followed.
As an entrepreneur and now senior executive and.
Like all these things. And he just said, you just got
to try things out. And he reflected back to me,
like AI just enables so many more people to do this now.
Like to do the kind of things that he did that at the time
required software engineering expertise, machine learning
expertise. Like now you can potentially
vibe code some of the stuff that he did really easily.
And so the opportunity for people who have ideas that they
observe problems in the world, the gap between observing that
problem and trying to solve it is now much, much smaller than
it was. And so whether you're trying to
impress some big company that you want to get a job at or
whether you actually want to start a new company.
It's just much easier to do that.
So I would. Sort of, you know, learn from
his experience and maybe be inspired by his story because,
you know, he was really, really understoring the fact that it's
just much easier to try things out these days.
And trying things out is often what leads to great inventions
and innovations in the world. Thanks for sharing such
inspirational story. I think definitely worth to
think about, right? So especially you having cross
functional skills back when you mentioned about cross functional
thing, right? If you have cross functional
skills, definitely the opportunities and the
possibilities are there for us to give it a try, right?
And vibe coding, yeah, it could be one way to actually build
POCS and kinda like test the market early before you actually
build the actual product. So Patrick, thank you so much
for sharing today. So I learnt a lot, especially
from those inspirational stories that you mentioned.
But before I let you go, I have one last question that I always
ask my guests. I call this the three technical
leadership wisdom. You can think of it just like an
advice that you want to give to the listeners.
What are the top three things that you want to share today?
Definitely, I'll return to the curiosity.
Like that's super important. I would say definitely following
your excitement and all things like just don't settle.
For doing work that you don't find and personally inspiring.
That you don't feel like, oh, I'm excited to get to work in
the morning. Like if you don't feel that way,
then think of other things that you might get more excited
about. And it sort of sounds obvious,
but I think that people get conditioned after a while to not
doing that and just settling. And I think that it's important
to really be excited about your work for a number of different
reasons. And I think the third thing
would be definitely, I always advise people to find a mentor.
And that also sounds kind of obvious, but I think the
majority of people don't actually actively seek out a
mentor. And it can be super, super
rewarding. Like I keep coming back to some
of the people in my own. Career who have really helped
me, even if it's just a sentence.
Or a catch phrase or a certain just way of doing one thing.
It can change your life over time really by just helping to
reframe problems or deal with, you know, communication issues
or whatever the case may be over time in your career.
That can have a really compounding effect.
So seek out at least one mentor, ideally multiple over the course
of your career. And it's really.
Going to help you. Yeah, thanks for pointing that
out. So you mentioned like even a
simple sentence phrase and all that could actually change
someone's lives, right, And can change your life as well.
So I think for people who have been around in the industry as
well. So be a mentor, I think I would
say as well, because you never know how much impact that you
can make to someone's lives. So, Patrick, if people love this
conversation, they wanna reach out to you or maybe learn more
from you. Is there a place where they can
find you online? Yeah, The best way is LinkedIn.
You can find me on LinkedIn pretty easily.
Right. OK.
So thank you so much for sharing today, Patrick.
I really learned a lot. And again, thank you so much.
It was a real pleasure to be here with Henry.
Thank you.