Do you know what AI actually stands for?
It's Artificial Intern. And that's how
you have to work with. It's an intern who strangely knows a lot
of stuff. So they're straight out of university and they have
studied pretty much everything, but they,
they don't have any, any, any real world experience. They don't
have the intuition that, that you have as a business person or a
developer or whatever you do. They can do a lot of
the repetitive work, a lot of the menial work of
analyzing stuff, analyzing text, analyzing information,
or writing templates, classifying something and
kicking off backend algorithms. This is what they're really good at.
But in most cases, if they make any
decisions or produce any facts,
you should always double check and make sure.
Welcome to Startuprad IO,
your podcast and YouTube blog covering the German
startup scene. With news, interviews and
live events,
AWS is proud to sponsor this week's episode of startup raid
IO. The AWS team compromises former
founders, CTOs, venture capitalists,
angel investors and mentors ready to help you prove
what's possible. Since 2013, AWS has
supported over 280,000
startups across the globe and provided US$7 billion
in credits through the AWS Activate program.
Big Ideas Feel at home at AWS and with access
to cutting edge technologies like generative AI, you can quickly
turn those ideas into marketable products. Want your own
AI powered assistant? Try Amazon Q. Want your own
AI products? Privately customize leading foundation
models on Amazon Bedrock. Want to reduce the cost
of AI workloads? AWS Trainium is the silicon
you're looking for. Whatever your ambitions, you've already had
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Visit aws.Amazon.com
startups to get started. Dennis Straub is a developer
advocate at aws, where he guides companies through the safe
adoption of emerging tech. With a deep background in cloud
security, developer enablement and generative AI
integration, Dennis helps teams test, iterate and
learn without putting the data or business at risk.
Today we unpack the AWS playbook for starting
with gen AI. Even if you're just getting curious.
Denis welcome to StartupRate IO and for every
podcast aficionado, we may add that you have been the
original voice of the German AWS podcast.
Oh, thank you Joe. Thanks everyone for listening. I
can't. I don't. Is it. Is it even still true with the AWS podcast?
Podcast? It has been. That was during. That was during COVID I
started that during COVID I put out I think 50 episodes or so
until Traveling started up again and unfortunately I wasn't able to
continue, but a few of my friends and colleagues here in Germany actually
picked it up and are still continuing it.
Anyway, thanks for having me on. On the show and
right in the, in the introduction, you mentioned something I think that's really,
really dear to my own heart and probably to most of your listeners.
What's the ROI in
AI? I think that's a question that many people have, including myself,
quite often. So I'm happy to talk about this today.
When people talk about AI, what comes to mind is
ChatGPT doing everything with it, but
it's a chat window. Plus what,
what has been in the news on and off is Elon
Musk's croc for either very great or very
bad answers. So everybody who's only heard about
that, how could you get started
safely with Gen AI?
Well, I think, most importantly, first of all, it's
important to understand what generative
AI actually is, how it works. Not in detail.
You don't have to. I don't have a PhD in math. I don't really understand
math. But you don't, you don't need to have that. But it's important
to have a foundational understanding of how these models work
and specifically what they are not. They are not people.
They are not human beings, even though
they talk like human beings. And
Andre Karpathy, one of the, one of the people who do a lot of foundational
work in AI, he actually said,
LLMs are like stochastic
simulations of people. So they behave like
people in a certain way in terms of putting out text, saying something,
but they behave like this friend that some of you may have
had in the past. I certainly did. That person who knew
every, everything. And when you, when you ask them anything,
they, they would have an answer. And they were so convincing with what they
said. But once you started questioning, you might have
realized, well, maybe, maybe it's not
really what they're saying, or maybe they are not as sure as
they think they are. So I try to, I try
to compare AI models, generative AI
language models, compare them with this kind of friend who
would like to know a lot and probably knows a lot as well, but sometimes
confuses things and isn't really aware or
doesn't want to, doesn't want to show any weakness and tries
to bring across whatever they come up with as convincing
as possible. And that's what's really important. AI
does not know anything. AI has been trained
on, on the entire Internet, basically on a lot of text
Material and what they do internally is just whenever
you type something, whenever you send something into the
language model, it looks at what you wrote
and then it compares it to what it has read in the past.
And then it comes up with, well, when I had this sentence,
most of the time the next word was this.
So it learns during training, it learns to
relate concepts with each other without actually
understanding the concept. Take a cat and the word
flurry. And an LLM sees these
words together very often when being trained on the Internet.
And then it knows with I'm doing air quotes here, it
knows that cats and flurry somehow
relate to each other and
may create text that puts these two
words together. This is very simplified, but that's effectively
how it works. It does not understand anything.
It is extremely well trained in terms of
pattern recognition. And it repeats patterns
that it originally saw. And many of these patterns
have been scientific papers,
lexical articles and all kinds of information where
people convincingly describe what they are
talking about because they are convinced. Because most of the time it's
actually true. And the model just
adapted this way of communicating. That's why it sounds
convinced. It is hard to
find any text on the Internet where somebody says, I don't know. This
is why most models also do not respond with I don't
know. They just come up with stuff.
And that's what's really important.
AI. AI models know
a lot, but have often have a hard time to
really put the things together in
a way that's that it's really, that it's really factual. And that
is something that you should be basically aware of. If you
know that, then you can deal with it in a certain way, then you know,
I shouldn't rely on it. It's not that they're not good enough
yet. The way these models work, they
will never have actual
understanding of what they talk about. They will always
be pattern recognition recognition
algorithms. And if you understand that, you can work
with them. Like I like to think about them as
another thing is like, do you know what
AI actually stands for? It's
artificial intern. And that's how you have to
work with. It's an intern who strangely knows a lot of
stuff. So they're straight out of university and they have studied
pretty much everything, but they don't have
any real world experience. They don't have the intuition
that you have as a business person or a developer or
whatever you do. I have
the exact example for this. For example, I'm using
a lot of chatbots for
many, many different functions and I've come to use them
for evaluating pitches, guest pitches, for the very
simple reason. I get up to 30 a week
during summer, and during winter it can be 60 to
100. That only makes sense for me to reply if it's
template, if it's not AI generated. And
so you mean people, people approaching you because they want to be on
your podcast. Okay, exactly, exactly.
And so basically, at first I was copying
simply in the email and
the AI of choice gave me back a potential reply, but then
I told it, okay, I want you to first evaluate
what this actually is, how likely is
it that it's written by an AI, what percentage is
by human, and then give me a pretty fair
assessment of X, Y and Z and this and this and that.
And then only when I know that there is less
than 50% AI involved, I look into the
email, tell the AI what to do, what kind of reply
I want to send, and then it goes out. And that's the
thing. One of the things that these language models are really good
at is classifying text, because they have
seen so much text during their training that it's very
easy for them to classify text if you help them understand
what the premises are, what the conditions are, what the
requirements are that you're looking at so they can classify. What they're
really bad with is coming up with facts because
they do not understand the concept of facts.
They just, they're internally, they're just numbers.
And texts can be represented as numbers, which is why
summarization, classification and similar tasks are very
easy, but there is no actual understanding.
And the use case you just described is,
I would be interested in understanding how many, how many false positives you
have in terms of how many supposedly AI generated
pitches you unfortunately sort out. Because
maybe the model or maybe the person actually writes like an
AI and it's, it's getting, it's getting harder to
actually distinguish between well trained
AI language models and actual human beings.
There are, there are some indicators right now
they may be out of date by tomorrow because things evolve, and
especially people who build systems based on AI, AI to
actually obfuscate the fact, to
hide the fact that they're using AI, they're working on this
as well. But let's get back to the intern, the artificial intern,
like every intern, especially if they know a lot,
they're fresh from university, they are really excited about
the job, and they are really excited about learning about what they
can do about your industry, about your use case, your products, your customers.
At the same time, they have no Intuition and they don't have any real
world experience that they can reflect on
whenever they approach a new task or have a new challenge to
reflect on and say, well, I saw something similar sometime in the past
and it worked this way. And they can start abstracting
and mapping and matching and coming up with solutions.
They just know their textbooks, they know what they studied,
they don't have the intuition. So it's your job. If you work with, with
an AI, it's your job. You can give them a lot
of tasks, but you always have to double check. They can do
a lot of the repetitive work, a lot of the menial work of
analyzing stuff, analyzing text, analyzing information
or writing templates, or kicking off,
kicking off classifying something and kicking off backend
algorithms. This is what they're really good at. But
in most cases you should really have a look at what
if, if they make any decisions or produce any
facts, you should always double check and make sure.
What I found is they're really, really good in for example, you
give them like 10 bullet points and a lot
of keywords and say, okay, write me a text from that. They're excellent.
They, they spare. They saving me a hell lot of time by doing
that. I see an email, I say, okay, I want this, I want this,
I want that. Because when you get tired and English is not your
native language at 10pm it gets really, really
difficult to formulate a straight, easy to read
email. And that's when it comes in quite handy. But
I wouldn't necessarily hand over my
mailbox to Gemini or ChatGPT or Claude. And that's
the point. I just want to know. We cannot hand over
the mailbox. So how do we get to really start
safely as a company with AI?
Most importantly, don't give any AI system the
keys to the kingdom. And your mailbox, especially if
you're a founder, most likely is your key to
the Kingdom. There are many, there are
many agentic orchestration systems out there
that allow to just connect to your Gmail
account or to Outlook, whatever,
or your calendar to connect to your data sources.
It's useful as long as
you don't let it act on this information
or at least not let it act without you double checking before it actually
acts on this information because in fact it would
be able to delete all your emails,
including important information.
It may be able to actually send an email to
somebody and it may not be the mail that you want
this person to get. So there's certain risks whenever you
give an AI or anyone like the Intern. Would you give
your intern access to your, to your mail account?
I just had in mind, as when he said,
for example, my wife has a pretty common first name, and
instead of sending her an invitation to date, I send it to
a potential client. The AI could
do that because it's the same first name. Right, right, right,
the AI could do that. But on the other hand, there's another risk,
and that's actually data, data privacy and security.
Because if
you give a language model access to your
email account, it has access to
all private information that's inside this email account. This
includes private information about you, but it may also
include pii, personally identifiable information of
customers of other people. It may even include
specific sensitive information like health information or,
or like, like relationships between a lawyer and
client. And there are certain pieces of information that, that, at least in
Germany, there's even more protection
around it. And you can actually go to jail if you, if you
expose your client's health information
or your customer's health information, you can go to jail for that. And it's
you, it's not your intent, intern who goes, it's you who goes to jail for
it. So what's, what's really important to acknowledge
is the fact that in your emails, there's a lot of personal
information, whether it's sensitive or not,
it is personal information. And as soon as
you send it to a model, you need
to be aware of how the provider of this
model interacts with that data,
whether they store it anywhere, whether they send
it somewhere else, whether they keep it secure,
whether they just throw it away and not store it at all.
This information is really important. So as soon as you give a language model
access or an agent access to your mailbox,
you give the model provider access
to personal information. You give your model provider
technically access to all the private information
that your customers, that your family, that you
yourself have entrusted your mailbox with. And
I mean, even if your customers wouldn't care, it would
be a GDPR headache because as soon as you sent that to
OpenAI or any other public provider
that provides like a public interface,
maybe even without any price tag,
you send that information somewhere else. And you as a company
have no information about what happened to this
data. You lose control over this data. And, and
by losing control over the data, you
effectively violate GDPR and maybe
other laws too. So it's really important to understand that
if you build an AI system
that has access to your MA box or any other
proprietary or private information,
you need to make sure that you understand the terms and conditions of,
of the model provider that you're working with. You need to understand
how, how, what do they do with your data? Do they, do they use
it for model training? Do they store it somewhere to
make it available to authorities when they
ask for it, for instance, or
maybe even outside the European Union? This is a very important piece of information,
especially if you work with publicly available APIs
and even more so if you work with APIs or
providers that don't charge you.
I mean, it should be a fairly well known
fact nowadays that if you don't pay with money, you usually
pay with data, especially with services on the Internet. And
that's most likely what happens with
many providers. I'm not going to name any
names. It is up to you to have a look at the
actual conditions and there are ways to work around that.
Most major model providers provide
ways to use their models that are GDPR compliant
or that allow you to use these models. GDPR
compliant? The models themselves are never. Or a tool that you use is
never GDPR compliant by itself. It's always about the way
that you use it. But most commercial model providers
actually have options that allow you to build
GDPR compliance systems with their models. But
it's usually not their chat interface. That is
exactly because what I was going for, because
why wouldn't you start with a chatbot? And how,
how would you look in a company, like on a meta level for
the first real project they can
use, they can do with AI. And I
have to admit that made me a little bit nervous because
currently I have somebody coding an AI based chatbot from a
website here. So there's two
questions. The first question why I wouldn't use a chat,
why I wouldn't build a chatbot. And the
second question is what I would look at when I would go
into a company or I would think about a use case that would actually make
sense. And that's an interesting question. First of all, I would not
necessarily say I wouldn't build a chatbot. A chatbot can be a
good use case, but most of the time it isn't.
The thing is, let me step back just one a little
bit. We, as
in everybody who's trying to use AI or trying to figure
out how to use AI in a useful way, are making,
we're falling in a certain trap and it's
completely normal to do that. We are trying to solve
problems that we have.
Most of them we have already solved
or the problems are inside of what we can
imagine fairly easily. And that reminds Me of back in the
day, back in the 90s when the world Wide Web became a thing.
I don't know if you had been around, I mean, Joe, you probably have been
around. I have been around. I don't know about the listeners, but if you have
been around. Back in the day, when we started building
websites or web pages or home pages as we called
them back then, back then we were mostly trying to
replicate what we already knew. So
we had yellow pages on the Internet where you could
find web pages like yellow pages with
classification systems that were based on
traditional libraries. Everybody had a
homepage which was more or less a business card.
And we tried to replicate print material
onto the screen, which was really hard because most MySpace,
even before that, even before that, most screens only had 800 by
600 pixels. Early HTML didn't really allow you
to position stuff. It's still hard nowadays, but back then it just didn't
work. The lineup lines, so the
connections to the Internet were so slow that you couldn't really
use images. And it was really hard. So everybody said,
every company said, well, we know we need to be on the
Internet now, just like they say nowadays, we need to use AI,
but it doesn't really work. And where's the
ROI in this? Where's the ROI in
putting my brochure, my print brochure onto the
screen? And it's terribly slow for, for customers to
load and display, but. Or what sense does
it make? Do you know what came to mind when you
talked about terribly slow? The sound of a dial up connection.
Right, exactly,
exactly. And that's the thing. We tried to
solve traditional problems with this new tool with this
new technology. We tried to use
traditional means and just map them onto the
screen. And that didn't work because the screen is not
made for printed stuff. The screen is
not a thick yellow pages, a tome
of addresses, of phone numbers for businesses.
That's not what it is. And over time, more and more people, and took
a few years, more and more people started realizing there's a
completely new way of thinking about things. And that's where
Google started. And Google started replacing traditional yellow
pages. And that's when Amazon started,
Amazon started replacing traditional brochures
on the Internet where you could read what you could buy and then
pick up the phone or send an email to the
retailer to tell them, well, I want this as a mail order.
That's when ebay came around, when Wikipedia or Wiki
as a principle started to come around, when things like Facebook and
Twitter came around, when we started to embrace
the new medium and actually use it for
things that we couldn't even imagine before. Traditional
classification systems, like in libraries, they are
important in libraries because they have to deal with shelf space.
They have to put the book somewhere and they cannot
put the book everywhere. But with the Internet, with
hyper hyper data, with hypermedia,
you can put the book literally everywhere.
You don't need classification systems, or you can have adaptive
classification systems, you can have dynamic classification systems.
You can even create something that looks at how many
people actually read your book and cited from
it, which effectively is Google. How many people actually
visit your homepage, your website, your application,
and actually link to it from their page. That is what Google looks at.
And it's the same situation all over
again, in my opinion. With AI, we're still trying to solve
old problems with the new tool, and we
haven't really figured out
what's the exciting new thing that we
can build with this tool that was
prohibitively costly in terms of money or in terms
of time, so that we didn't even think about doing it.
Imagine back in the day before we had the Internet and mobile phones.
Imagine back in the day. You live in Germany. I
do. When I called my relatives in the US or when my family
called relatives in the US that happened once a year
on Christmas. And every family members had about
five seconds to talk to them because it was an
intercontinental call, which was so expensive. So we never
talked to our relatives except on Christmas when I
went on vacation and I sent a postcard back
home. Most of the time the postcard arrived two weeks after
I arrived back home. And right
now, with mobile devices, with the Internet and everything,
we talk to people all over the planet all the time.
Yes. I vividly remember what a revelation it was
when I was studying in the US or working in China, when I could
use Skype to call people for, for
local, for local rates. So
how could a company really identify
what should be the first project? Because I wouldn't
necessarily recommend to have like this really big
hairy goal for the first AI
project, but rather something small, something
that really makes sense, takes a lot of maybe repetitive
work out of the job of the employees. Right? And
that's the most important question.
What use case? What workflow? What
item on your to do list gets
never done?
What are the painful things in your business that
nobody ever took care about because it
would take too much time, or because it would take too many
people to work on it, or because it would be just too
costly to do it? What are
the things that
if you had a magic wand and you could make them go away.
So the daily tasks, the menial things, the things
that bother you all the time, but
they need to be done or they should be done,
but I don't get around to doing them because I have so much to do.
What is that thing that you would like to be to get
done and it never gets done because there's no time for
it. Make a list of these things, write down your
most painful things that you have to deal with every day
because they don't get done and they don't get done. And then have a
look at it at them and think about, is this something
I could hand over to an AI
safely? Hand over to an AI, right, safely. Maybe not
in its completeness, maybe just a small part of it.
And if you want to build a startup for a certain industry,
talk to the people in this industry, talk to them, ask them
this, this question. What is the one
thing that has been bothering you for the last
30 years since you started in this industry? What is the
one thing that's bothering you
but nobody ever took care of it?
And then think about
whether this could be something that you could hand over
either in part or maybe even completely to an AI
and well, of course, don't start with the big hairy
goal, don't start with the big thing. Try to find a small
painful thing, create a solution for it
and then go back to the customer, go back to the market, go back to
the person you talk to in the industry or go back to yourself if it's
for yourself and see does it actually.
Do you know, Dennis, what my consultant mind was making of what
you say? Basically you put a lot of your employees into
brainstorming session, they come up with 20 problems,
you cut it down to 10 problems that are really
efficient if you could automate or partially automate them. And
then you start with the easiest. Yes.
And it's not only about they would be most
efficient, but we could finally address
them through automation. It wasn't possible before.
That's the thing. If we try to address problems that we already
automate and we could make them more efficient and that's not
innovation, that's optimization. That's a good thing. I'm
not saying we shouldn't optimize, we should optimize, but that's not
innovation, that's not the breakthrough, that's not the next Google,
that's not the next unicorn startup. The next unicorn startup
will solve a problem that everybody has, but
nobody even knew that they had it or nobody
even thought of solving it because actually solving them, it
wasn't even possible before. And this can be a small
thing. This can be a tiny, small thing.
It doesn't need to be a big thing. It can really be a small, tiny
thing. And if you have something like this, you effectively
have a money printing machine because everybody's going to tell, whoa, I didn't
know that's possible. Right. I don't have the
solution for you. So I cannot tell you it's this or that
thing. That is something that you need to look into with your specific
expertise, with your intuition, with your background,
with your creativity. Creativity. But what we've been
doing most of the time with AI in the last two
or three years really was just trying to, trying to, trying
to use AI to solve problem that we are already solving
and making them more efficient, making them least
less costly, reducing cost, unfortunately, firing people
and replacing them with AI to then figure out,
well, maybe. That was needed those people. Right.
Maybe it was the best idea. Maybe we shouldn't have listened to the
promises of AI now replacing everyone.
AI is something that can help you solve new problems
and AI shouldn't be used to solve a problem that's
already been solved unless you are in the
optimization stage. Big enterprises may be in that stage and
big enterprises may be doing the right thing when they're looking at their
processes and workflows and everything and think about well, where are the
bottlenecks? Can we apply this to individual bottlenecks in
here to make the overall process more efficient or more
scalable or whatever. But especially in the startup space,
you want to innovate and innovation. Innovation is creating something
new or solving a problem that everybody
thought was not solvable or didn't even think
about solving because it didn't. Yeah, we didn't.
We didn't think about calling our relatives in the US every
day because it just wasn't possible. It was
too expensive. Right, I see.
I would be wondering what for our
audience would be their first gen AI use case
idea and what's holding them back from trying it. To top
you, drop your comment or DM us on LinkedIn.
We'll be back after a very short ad break.
So let's talk a little bit more
specific about the problems here.
What do you think is the biggest misconception of non
technical founders they have about AI, especially around
model choice and privacy?
There's two misconceptions, one I mentioned earlier that
is that we mistake these things for
humans because they use human language and that's the way our brains work.
If somebody talks to it or to us or uses human language,
our brain automatically thinks it's a human being. And by
thinking this starts to make
assumptions. And many of these assumptions just aren't
true. And these assumptions lead us down a
path where we get disappointed, where we get
frustrated, where we feel like, well, it just
doesn't work for me. AI just doesn't work for me. It isn't there yet.
It will never be a human being. It will never be able to actually
replace a human being in that sense.
However, its capabilities
are incredible, but they are slightly different. So that's
the first misconception. And one of the things that we tend to do is give
it names, which makes it even harder
for us. Like I remember we had. I can say
it because I don't have a device in here. There's Alexa from
Amazon. Alexa, the device which uses human
language, it talks to us. And
when I talk to Alexa, at least the original version,
not Alexa, when I talked to the original person and I asked it something
and it didn't know or it didn't understand me because it was just rule based,
more or less, it would say, I can't answer
that question. And I would get annoyed. I
would get, I would feel frustrated because due to the fact that
it was speaking to me with a human voice, something inside of
my brain thought, it's a human being. And
the next thought was not even consciously, but probably
unconsciously. How stupid are you? Why don't
you understand? And that is, that is
there's a break in communication happening
because my brain makes some assumptions that the
technology doesn't fulfill. So I was frustrated.
The technology doesn't care, but I was frustrated. I felt
like that doesn't really work until I really understood. Well, it works in a different
way. I cannot, I cannot project
human consciousness into it. And
that's a misconception. Projecting human consciousness into the
thing is a misconception. This is something to be really aware
of. The other misconception, and that's an entirely different, different thing,
is that you need the most capable model.
That you need to really make sure that you get
the most capable model to get started.
That is a way, and that's a kind of procrastination
because all the models are really capable nowadays. Sure, if you
look at the benchmarks, the models are different and every day there's a new one
which beats some specific capability over all the others.
There's a lot of progress going on. But if you wait for the perfect
model you'll never get started. You can use
literally any of the frontier models nowadays. It could be one of
the open weights models like Llama or Mistral.
It could be one of the commercial models like
GPT or Claude or Nova.
It doesn't really matter. These models are
capable enough to experiment with the first
use cases. And once you've experimented and once you've
found a product market match, once you found
a use case that really works, then it makes sense to think
about, well, does it make sense to maybe use a different
model that's a bit more capable
in this specific use case? Or maybe it makes sense to
introduce a second model which is less expensive for
part of the use case? Because for instance, for
summarization, I don't need any reasoning capabilities,
I just need a good summarizer model. And for
the actual workflow orchestration, for the agentic
workflow maybe that I'm going to build, I need a model that's actually
able to do planning and reasoning. These are two very different
capabilities and they have very different costs. So
I might need, at a point in the future, I might want to look at
different models and at their price, structure, at their capabilities. But to
get started, just pick one. Just pick one.
If you have GDPR or other privacy issues that you
need to take into account, pick a model service,
a model hosting provider that provides you
this functionality that guarantees you, that tells you we don't
store your data and all the data is being encrypted and we don't use
the data to train our models and we don't send the data to anybody else.
If you come to AWS to use a model on Bedrock,
and no matter whether you use our own Nova models or you use Claude or
you use Llama or any of the other models,
we host these models ourselves. We're not a
gateway to the actual model provider. We're not a gateway to LLAMA
or to the Llama API or to the Anthropic API or anything.
We host versions of these models in air
gapped accounts.
Nobody gets into this. These models and these models
don't send anything anywhere. Everything that
happens is just your request gets
sent into the air gapped account, gets handed over to the
model. The model itself is stateless, it doesn't store anything. It
just takes your data, loads it into its GPU along
with the model algorithm, with the model weights and processes
that, and then it sends the response back
and everything else just goes back to sleep. There's nothing that we
store and well, we
do store, obviously we store telemetry, we store that
you actually called the model and how many tokens you use because
that's how you ultimately pay for that. But
we don't do anything with your data.
We even have models running in Frankfurt that you can use so that
you don't even have to send your data to the us.
We even provide access to these models through our own
backbone, so you don't even have to use the public Internet if you
want. That's what model provider or non
model providers, model hosting providers
like AWS provide. It's a little
more costly than just going to ChatGPT or to Claude
AI or to the Llama API. It's more costly.
But on the other hand, we have different terms and
conditions and we make sure that you will be able to build
GDPR or HIPAA or whatever compliant workloads
using these models. And that's an important point. If you have
pii, if you need to take GDPR into account from
day one, make sure you work with one
of the providers that actually give you these capabilities,
give you access, give you encryption, make sure that they don't use
your data in any other way so that you can safely say in
your own audit and to your own customers, I know
where your data is going and I guarantee
that it's not being handed over to somebody without my knowledge
or without your knowledge as a customer. That's the important thing.
But to get started, I might even really start
with a use case that
doesn't even need these complexities because it introduces
complexities. And that's the thing. As soon as you need to
work with sensitive data, you have
to think about these things. You may have to think
about. Even if you
have a workflow that uses data from
your database which goes through a model in a GDPR
compliant way and gets displayed somewhere
to a client, you still need to make sure that
the data isn't being displayed by accident
to somebody who shouldn't have access to them. So you need to be able to
ensure authentication, authorization. You need all the
security and compliance mechanisms that make sure that not a
random person on the Internet or just a random person inside
of your company is able to just use the agent and access
your customer data.
I see. I was wondering for our audience, if you
could safely test any AI idea without
risk, what would you build, tag us or reply to us
on substack or with your moochart? I have
two final questions for this interview, Dennis, because we are already
recording for more than 45 minutes. But.
But I do believe they're very important thoughts
before you even start thinking about
applying AI. And we already know you guys support different
models. You are more the infrastructure provider for
something like this. But I was wondering, have you seen any
clever AI adoption stories where companies
started small and then scaled rapidly?
The first thing really that I would look at is
do I even need AI for that? Many of the things that we're trying to
solve with AI nowadays, they have already been solved
and probably in a good and much less expensive and much less
ecologically impactful way.
If you have a calculator that can add up to numbers,
use a calculator. Don't ask an AI to do it for you. First of all,
it isn't very good at it. Well, they're getting better at math. But
why would you start up an entire cluster of big
Nvidia GPUs to get
the sum of two numbers? You shouldn't be doing
that. So first of all, don't try to solve already
solved problems. And the second thing really is
again, look at
the painful things that nobody ever tackled.
Look at something that has been bothering
you or your customer for a long time
and it hadn't been addressed because
everybody said, well, it doesn't just doesn't work and we don't have the time to
do it ourselves.
It's actually pretty good closing words.
We will be back for one, the Founders
Vault for our premium subscribers on substack and YouTube. And second,
you'll be back for a second interview where you get more
hands on when you go through all the thoughts you had
that you need to think through before you can even get started on
AI. Great. I'm looking forward to it. Me too.
Have a good day. Bye Bye.
That's all folks. Find more news, streams,
events and
interviews@www.startuprad.IO.
remember, Sherry is caring.