All right, welcome to the Neuvottelija channel, Aleksi Paavola.
Thanks so much. And we didn't really know each other a week ago,
we'd seen each other's conversations over at Agentics Finland a few times, but,
you'd done such a great piece of coding that I thought,
get this guy into the studio right away — and luckily it worked out. So could you
start by telling us a bit about your background — what you do and where you're coming from?
Yeah, absolutely, great to be here. My name is indeed Aleksi Paavola, and I've been
in tech for a good 10 years — actually probably closer to 15 by now —
as a tech entrepreneur basically my whole career, and I've founded and sold a couple of
tech companies, and for the last four years or so I've been building
AI stuff, and also doing software development and
software consulting. Now mainly around
these language models, on top of and around them, all sorts of things — I've done quite a lot,
been in Saudi Arabia running trainings, and I've also
trained Google's Europe FINA team on language models, [chuckle] and then I've also been in Oslo
representing Google's language model cloud solutions at
a seminar with over 10,000 people, and there's been
all sorts of interesting stuff. Cool — actually I had Topi Manuas
as a guest. He moved to Singapore and he's been doing Google's cloud sales,
and we talked about Google's multimodal solution. Now I'm
well, even though we probably both use Claude more,
and Codex — but somehow the Gemini folks invited me along there,
when they launched Gemini 3.0, we were at this really exclusive
event — it was champagne — and they released the model an hour before the global
release, and I've always had this soft spot for
bribery, so Google feels like a really nice company, and they have in Finland,
in Hamina, real investment going on — yeah, that's actually where
in Hamina I was running this training — Google's Europe FINA team
gathered in Hamina for a seminar like this, and we went through a bit of
the basics of language models. Hey, that's awesome. You're honestly a bigger deal
than I thought — I thought you were sort of roughly on my
level, but now it's getting a little scary. But anyway, the reason I
invited you here wasn't about Google at all — it's that you've built this
open-source project, this better
Model Context Protocol tool called Laki.ai, and I started using it right away,
and it works great. Before that I had something else filling that gap,
something called Oik, but that was clearly worse. So you've built
this better tool, but explain — what exactly is Laki.ai, and what
is MCP? Right. Thanks a lot for the kind words. It's
come up from other places too, actually — that listeners should really go
and try this out for sure — but, um, this project actually
got started with Ilkka Linkoneva and Juuso Vesanto —
we tried to solve this challenge, where
we'd identified that language models were going to produce a huge amount of this
legal documentation. And then people would still need to read and
understand it. So we developed a solution for that. This was already a few
years ago now. And our hypothesis was that if we fed into our
system, say, 100 NDAs, a lawyer could then highlight
some section in Word from a new NDA that they've
gotten from a client, and then see, from the ones they'd previously approved themselves, um,
the corresponding section in those NDA documents that's semantically closest
to it, and from that easily approve it, like okay, I've
approved this kind of clause on basically the same terms before, so this is definitely fine. Well,
the surprising challenge that came up here was
lawyers' ability to express things in a million different
ways. [chuckle] So these ended up just full of redlines. So now
when you — like in that test we had, we had about 50 of these
different NDA agreements, and when we took a new NDA and you
highlighted some section in Word, it was always just
red with redlines. The phrasing and the terms somehow
these lawyers had worded so differently that the tool didn't end up being very
useful. Well, around the same time this pretty fortunate thing happened,
from the perspective of Finnish citizens, actually — Finlex,
its data became openly available for everyone to use. So
[chuckle] this had been — and this was, this was March 2015,
I think it was, and before that, I remember there was some big software house
that was doing an AI project with Finlex's data, and
it fell through over this licensing issue. I think Alma
Media was holding onto it too, too tightly,
but then in March it got freed up, and our idea was that
we'd build an interface — like a better interface — for Finlex's data, um,
with no commercial purpose at all. Just mainly because we
could, so we'd do it, and we figured this was going to be a hugely
useful service for citizens, of course, and so we built Laki.ai,
launched it back in 2015 sometime in summer, and it's still — it's just a
traditional web service for Finlex's data. So it has
laws, statutes,
there are court cases, government bills, and what sets it apart
from Finlex itself is that these documents are all linked
to each other. So when you open a specific section of law there,
for example, you immediately see, okay, this is linked to these government
bills, and then this is linked to, say, these seven court cases. Mm.
So that's basically what we built, without any bigger plan for
anything beyond that. Well, now, fairly recently,
um, we had this idea — well,
could we build something for an agent on top of this, and
[clears throat] we looked into it a bit and concluded that
yeah, this would definitely be possible, and that's what got
this Laki.ai MCP started. And what that actually means in practice
is that now, with your AI tool of choice, you can connect it
to this Laki.ai database, and now your agent
can use all this legal data that we have there
in the Laki.ai database. Yeah, yeah, it got straight into my Claude, uh,
tech stack — though there was a bit of fiddling involved, actually,
let's just say, right at the start we'd invited — along with Aku Nikkola —
from Legit here — he, well, he wanted to spend, understandably, a long stretch
of summer vacation afterward, so he couldn't make it here, but around the same
time he'd released this — Claude has this legal
skills thing, which is kind of Anglo-Saxon know-how, and he'd then
built, with his team, a kind of skills library that has
the special features of Finnish law — starting from the language, like how a section
works, and so on [chuckle], and he published it. I took
that into use too. There was a bit of fiddling involved, actually, because for some reason Claude
had changed the connectors a bit, so I had to go down to the command-line
level to shove it in, which was a bit annoying. But when you
combine these two skills — this skills library, which in my
opinion is actually pretty good, I'd recommend that too — and then this
Laki.ai, you get a really nice combo for Finnish
legal needs. I don't know, have you had a chance to test Legit's
skills yet? Yeah, definitely have. And maybe
I could add a bit more, since you asked, to open up
a little more for listeners — MCP isn't necessarily super familiar to everyone.
So it's an open-source protocol that Anthropic — the company behind
Claude — built and designed, and it stands for Model Context
Protocol — that's what MCP is short for — and in practice it's a way to connect
different applications for agents to use. So maybe the closest
example would be something like USB-C. So if you think about your
computer, and you want to plug a keyboard into it, you plug
the USB-C cable in between them, and then, simsalabim,
you can use that keyboard with the computer. So this is
basically kind of the same idea — when you connect your
Claude, or soon ChatGPT too, which we
have coming for Laki.ai — then
your Claude doesn't need to
hunt for legal sources on the open internet anymore, which is pretty difficult and
easily leads to errors — instead it can now use this database
we've built. Yeah, that USB comparison is good. So
as I see it, MCP is kind of a more refined thing than
a regular API call — Application Programming Interface — and the problem with those was
that everyone called them a bit their own way, like pulling
data from HubSpot into Word or whatever — so it kind of created
this sort of port standard, and it eats up some bandwidth, and it's
a bit too complicated for really fast calls,
but it opens up all the interfaces really nicely, so —
did I get that roughly right? Yeah, pretty much. It's just
that, um, behind MCP there's basically always APIs. So it's a
protocol built on top of those APIs, designed
specifically so agents can use them efficiently. And
yeah, what you said is completely true, that there's been
[chuckle] — you said there are some challenges too, and that's completely true. And
MCPs have gotten a lot of criticism exactly for filling up the
agents' or language models' context window, but these have
improved quite a bit, and what's actually improved the most,
is agents' ability to actually make use of these different MCP
services, and it seems like — I myself was fairly skeptical at one point
about the future prospects of the MCP protocol.
But now I'm a bit more positive again, it seems like maybe we're finding
solutions where these agents can now efficiently make use of these
MCP connections and services that
are hooked up via MCP. Yeah. And in a way, since
the level of inference, or AI capability, keeps rising
all the time, it's not really a problem to build even really weird
pipelines. Like, AI will build basically any kind of, uh [laughter]
ancient pipe between some old database and a Cobol system, as long as you just
shove it in there, because they're just capable of that — but those are fragile, and MCP
is a standard, and standards are good for a reason, because they
evolve too, in that way — and it's nice to hear that it's also become
more efficient, that the standard itself has developed along the
way. Yeah — you say 'agent' a lot, and actually I myself
had a little chat about this over lunch earlier, about whether
agents — in the sense that I want to understand, like, this same kind of
AI employee — are they really that useful? Maybe
we should save that conversation for later. I think we have a bit of a different view on that.
Um, because personally, what you might
call an agent — like some kind of thing sitting next to an MCP,
some subroutine call — I wouldn't even publicly call that an agent. It's
just some subroutine running there. Not an agent at all. Whereas these
agents are more like same-level AI employees, almost god-
like beings that can do high-cognition work,
not just sit in between two things. I don't know
if you want to jump in on that already, [laughter]
I can jump in a little, um, to the extent that, if we
think about the direction ChatGPT and
Claude have moved in, they've become a lot more agentic. If you think about
how, say, the very first ChatGPT, back in November '22,
that released version worked, it was always just — you'd write some
prompt, the prompt got sent to the language model, and the language model
sent back some answer. But now — and the way I think about it —
the difference between a language model and an AI agent is that in an agent
the language model is used in some kind of loop. So if we think about, say,
essay writing, a non-agentic
way of writing an essay would be that you have a prompt, and out comes the
finished piece the moment you feed the prompt to the language model, whereas in the agentic
process it evaluates its own work along the way. So it writes
a first draft, say, and then it checks, okay, maybe this
second paragraph needs to be strengthened a bit more, or the ending is missing
some punch — so now ChatGPT and Claude have moved
in this direction, so I think in a way you could even call these chats
agentic
systems already — but of course it's true they're not agents to nearly
the same extent as, say, Claude's Sonnet, or Codex, or
Claude's Cowork, or Claude Code. Yeah, but that's honestly, scarily, always
moving toward that same point — for anyone who hasn't yet
heard the term — this kind of open AI employee that has
its own three-part memory structure and very advanced context
management tools — so we actually have to get into this context concept,
because you said the MCP call eats up that precious context, and
we're already at million-token contexts, basically as
standard models — Claude's and, um, OpenAI's base models run
on a million tokens, so it's less and less, in a way, that short-term memory
eating problem here that's disappearing, and if the MCP model gets even more efficient,
then this is a vanishing problem overall. How do you see, by the way,
this — um, I've actually had guests who like to run
the 'dumb' models on purpose — like, it's not a bug, it's a
feature — that they run this 'dumb' inference on purpose because it's
easier to understand, and it's actually good in a way, that there's not too much
creativity or harness layered on top of it. Do you have a philosophy
on that? Well, yes, yes [laughter], very much so, maybe, maybe
as a basic piece of advice I'd say that people
should, on a general level, pay for these AI services, so that
nobody should be using the free versions of these for work — because
paying gets you a lot of value for your money, and these are still
relatively affordable, at least for now — though I do expect that
prices will definitely go up, and have partly gone up already — because
even Anthropic's and OpenAI's Enterprise plans aren't exactly
cheap anymore. But generally speaking, I think a good
basic recommendation is to always use the best model. And that's kind of
something people should just know — it's like
modern general knowledge, that you know — if you think about it,
if you've chosen to work in, say, the Claude or Anthropic ecosystem, then
you should understand and know what the difference is between, say, Sonnet, Opus, and Fable
is. M. And then, as a baseline, you could
always — if you have some difficult task, [clears throat]
you use the best model. And then again, if you have — if you're just
asking, hey, what are some good lunch spots nearby, then it really doesn't
matter whether it's some super-smart model. You could ask that
to basically anything. Yeah, yeah. That's how it is — then we can
move on to local models, where you're not allowed to do any
tool calls or internet searches — so it's kind of very rigid, and
in that case it can be really important to know in what way it's
mechanically rigid, because then it can be fully reproducible, because
in a way, maybe this hallucination that the better
models always get criticized for — the more harness or control layer you have in
between, or agentic loops, or Ralph-loop envelopes, or whatever cleverness you
build in between the model and yourself, the more
hallucination loop comes into it too. So in a way, one solution
is to strip all of that away, to just run it directly on bare metal
in some task that's outside your own interest, so you know exactly
what the model is going to do.
Yeah, right. There's actually a great essay on this — I don't know if
have you read it, or come across it — the Bitter Lesson? No, I haven't,
so tell me more about this legendary Canadian
professor who wrote, years ago now, this
essay on AI, where he argues that the value humans
bring to AI systems
is, sort of,
really limited over time, and he gives good examples. So if we think about
chess, say — originally in chess, people
thought that humans could bring value to
the AI. So basically, with this harness
you could make an even better AI. But then, from chess
or chess history, we know very well that a human can't
add any value to a chess AI like that. A chess AI
on its own is much stronger than AI plus a human. And
the AI can't be helped at all by a human giving
it tips for that kind of chess AI. And self-
driving is a good example too — there, first they tried doing
sub-optimization, building [clears throat] small pieces at a time.
Andrej Karpathy has actually talked a lot about this, about how
Tesla's strategy — which a lot of people
have already copied by now — of training the car to drive
so that the neural network handles the driving has turned out to be
a much better strategy than trying to tell it
in specific spots, sort of steering the AI
using human intelligence. And now I believe we'll see this same thing, and we already have,
in a lot of language-model-based systems now. If we
think about software development, say — in software development, at first
there was Cursor, which was this traditional
integrated development environment — an IDE that
developers were used to using — and when AI was brought in and
integrated into that IDE, for a long time it looked like, okay,
this is going to be the way — that humans can add value
at various points along the way in software development. I myself was actually really
skeptical about that when Claude Code came out,
whether it could really be true that a purely text-based interface
could work on its own. Whereas over in Cursor there was this tab
completion — you'd write the start of a function, and then
the AI would predict what kind of function you wanted to write. Pretty quickly, though,
it's gotten to the point where I don't even remember the last time I did a tab
completion like that. So it's really shifted toward these...
these harnesses have kind of shrunk from that, and I believe this
trend is going to keep going, and now if we look at
say what OpenAI's or Anthropic's employees
say — what they talk about is exactly that they're
constantly trying to strip that harness down.
Mm. That is, letting the
model take on a bigger and bigger share of it. And if you think about
the legal field from that angle, I'm actually really skeptical
about, say, Legora or Harvey now. They're in the same
position Cursor was in a while back, sort of. They've
tried to build this kind of software around
the legal side, so they could add
some value at certain points. Mm.
But I think we're already at the point where if you have Claude and
you connect it, say, with Laki.ai to
reliable Finnish legal sources, then for pretty much
every legal-related
task you'll actually get better answers from Claude than from
Legora or Harvey, which have built a lot of harness
between the model and the user.
Yeah, that's a harsh way to put it, but hard to disagree. Personally, I
actually only started coding properly again this year myself. I was
a good coder as a kid, and I've been an investment banker for 30 years now, and
I haven't lost a thing in those 30 years, because those backslashes are still
sitting there in the terminal waiting. And actually, I do use
autocomplete for that — when I SSH between different computers,
tab-complete always finds, like, what did I even name this machine, so
I still get autocomplete out of that, but otherwise I'm pretty much full
slash-goal mode here — I mean, it's public
sector, make-no-mistakes territory, just slash-goal — but somewhere there's
a line, where your goal is so complex that you
can't just do that — this is like an extreme example,
something like "make no mistakes, fix the world" or something huge —
so somewhere there's still a human path running through it,
taste and intent and the steering that's involved, and — well, somewhere,
in driving, or in editing a simple legal document, that
works, but then if it's some multi-year, complex
tangle of shareholder agreements between, say, your five family offices'
portfolio companies, then, well, it doesn't quite work like that — there I
believe a thick harness still has value, because it
is just, somehow, so multi-dimensional that on a single
dimension you can't play it. Um, so this kind of task-by-
task, one vertical task at a time — yeah, those are getting chewed through constantly, so
once enough of them are done and the system has learned, then
it can pull them together holistically too.
Yeah. And then there's maybe that one, sort of,
dimension where Harvey and Legora are trying to compete, which is the interface.
They have these UI components that you
don't have in Claude, say, but I predict this will stay temporary,
because generative interfaces seem to be coming on
pretty fast right now. So soon you'll just be able to tell Claude,
hey, I have this use case, what would be a good
interface for this, and it'll build you
that interface on the fly. Yeah.
So the room for Legoras and Harveys of the world is going to get
genuinely pretty tight. And it's really interesting, because they've sold
it so well up to now — it's been sold incredibly well, the price is
really steep, the contract terms are pretty long, but this is a really
interesting situation in the sense that if we think about
those law firms that have committed to these and
built their whole process around Harvey or Legora, then
along comes some smaller firm that adopts
Claude instead — and especially if we're talking about Finnish
law, right now in Finnish law Claude is
just flat-out superior compared to something like Legora or Harvey, because
you can't — in Legora or Harvey, as far as I know at least, you can't
actually get Laki.ai hooked in there. Oh. Yeah. So it's that closed off.
And they don't have an equivalent — they bought this Swedish
startup, Kurigal or was it Kur Data, which basically has
done in Sweden the same thing we've done with Laki.ai in Finland.
So in Sweden they have that capability, but in Finland they don't have
that capability. So legal research in Legora or Harvey in Finland
relies purely on a web browser, and we know exactly what
level that's at. Now a good example of this is, say,
Harvey's own case too, where by default
they've actually banned the model from doing this web search,
and there's a very good reason for that [laughter], and now you've got
this appallingly expensive product whose legal research relies
on that web search — so is it really worth paying for? Well, neither of us is
a lawyer, so of course we're of the opinion that it's not
worth paying for, but — I do have friends
though, who do just fine living off that lawyer's know-how, that corpus, and
expertise, and their closed systems — and if you have, say,
a top Finnish law firm, then their own internal
system, with thousands of past cases in it, or you could
use training material — you can still find that unique
expertise there, the case-specific kind. I'd say — even though neither of us is
a lawyer — Finland has weak courts
and a strong Chamber of Commerce arbitration system.
And this arbitration system is partly a bit like the American
case-law type of thing, where, say, one or three
arbitrators make a legally binding decision that
binds the parties just like a judge's ruling would, if they've
agreed to use the Chamber of Commerce's dispute resolution method for it. And they're
not public, as far as I understand, so that body of information partly lives
in law firms' own databases, because they're parties to these cases. Their
partners might even be the arbitrators themselves, and it's
kind of kept out of that whole open-data world of yours. I don't know
if that rings a bell, that world — this kind of Helsinki Chamber of
Commerce-type exception. Well, yeah. I mean,
the challenge here, if we think about these law firms, what's
difficult about it is that now
a couple of years ago I was wondering which direction this was going,
these general-purpose AI tools. Would they become
so easy to use that anyone could use them,
or would it get more complex? And I think this
has clearly gone toward getting more complex. So now
law firms traditionally maybe don't have much
IT expertise to begin with, let alone this kind of AI
IT expertise — which means that actually getting the benefits out of it, whether it's
something like Legora or Harvey. Let alone something like
Claude — to actually get the power out of Claude, it's
exactly like you said, the key role there is played by your
company's data, built up over the years. And now you need to get that into Claude's
hands in a sensible way. You need genuinely good
skills — which, as we touched on a bit earlier in this conversation —
came up. So, skills means
you can teach these language models different kinds of
tasks. You could think of it like this: take doing a somersault, say —
and say we have some young child,
who doesn't know how to do a somersault, then
the somersault involves certain things you have to
do — so once you've taught that child the somersault
skill once, then it can, sort of, whenever it needs
to do a somersault, just pull out that somersault skill. And this
is kind of the same thing — now you can teach these language models
different skills. It could be, say, NDA
commenting. Mm.
Right, but you'd need to build it — and this was actually a really
great opening move in that direction. This Claude — sort of the American Legal
Skills, adapted for Finland. This Claude legal skills
pack — Hakunikkola's GitHub has it, whether
the skills — the skills pack — is something you can install with little effort
into Claude, or as far as I know, into other systems too.
[chuckle] Yeah. But maybe on skills, by the way —
my own observation — for a long time I did them like this, I'd start
building sort of alongside, like: here's a task starting, watch me while I do
this task, and oh, stuff like that came up, and then we jumped around, and I
realized this was done completely backwards — that you should first do the thing
all the way to the finish line, and only then say let's turn this into a skill — once I've slogged through
the whole obstacle course to the end, that's actually a better way to make
skills, at least at this point in time. Absolutely agree. Yeah,
yeah, but nobody really teaches that either — I mean, in that sense
or, I don't know, there just isn't anymore that kind of
Stack Overflow that everyone could go check to see how this stuff
gets done — everyone's kind of learning it at random, I think, but
it's kind of fun in a way. It's insanely fun, [laughter]
but the unfortunate part is it's a pretty small group
who are excited about learning
this kind of stuff. Yeah. Maybe I have to give Legora credit here too
since, well, those Legora guys built that too.
So at first I installed it, of course, thinking, let's just switch it on and,
since I'm the DIY type, run it on this Gemma 4 26B model on my own
hardware — and it was total garbage. Then I put Qwen 3.7, the seven,
running there, which I already run, and that was a lot better, but then there was this
problem where I didn't get their philosophy — like their
Harvey-style model, where the idea is you have two different
language models, which are, say, different — Opus and then Sonnet,
or, well, the best Sonnet, which right now is five, and Opus 4.8,
eight, since Fable got taken away from us — the way they, sort of,
argue with each other, that sort of produces intelligence that doesn't happen
with a single local model. If it's the same model going up against another copy of itself, then there's no
real cross-battle happening, it's just — at first
I thought this was total garbage when I ran it with that local model. It wasn't
getting anything done. But then when I ran it with, like, the best Claude
models, it actually produced a pretty okay base. But then when I ran it
through your Laki.ai and then through these skills, it turned out
even better. So I thought this was a pretty solid workflow. So like
you make some kind of base draft that doesn't need to be all that polished, that can be
just your own best rough sketch, and then you refine it — I think that's
where pretty nice stuff comes out in the end.
Yeah, absolutely. [laughter] Yeah. And maybe let's open this up a bit more
for the listeners too — you just said Legora, but you actually meant
Lovable for sure. Yeah, yeah, yeah, got them mixed up. So Lovable is just this
kind of free program too, that you can install. It eats up some
tokens once you install it. Though this is — yeah, Legora and Harvey, I've
actually never used either, I have to say those are total black
boxes to me. I just watch and see that, once again, the Swedes and the Yanks know how to cash in
on that too. Yeah, I haven't used Harvey myself
either, though I have watched YouTube videos
of it being used. Legora I have used, and
it hasn't convinced yours truly.
Yeah. Lovable is actually kind of fun from a Swedish-coding-scene angle, because they sort of solve
that whole minimum viable product thing within a safe authenticated setup,
pretty neatly and with little effort, instead of you having to
go and set up your own servers, or cloud
installations. I still like that — I still occasionally do some
little things with it myself, yeah, that's also interesting to watch —
I think Lovable actually fits pretty well onto that sort of Pieter Levels path,
whether it turns out that, as we go a bit further, I —
I'll sign off on that. As a user, Lovable is still pretty handy, because
it sort of abstracts away certain
pretty laborious steps, like this whole cloud infrastructure
provisioning and all this configuring,
but how long is it before you're just sitting in Claude's or ChatGPT's
interface, saying, hey, just whip me up something like this.
[laughter] Yeah, that's how it is. Yeah, mainly for me it's
that I at least know what the tech stack is, sort of. They've
gone with a super simple one — you've got React and Node in there, and then
TypeScript too, so I know that those are good
building blocks — and Supabase and so on — and they work, and they don't really
leak, as a rule. Whereas if I go and build it myself, then
you can end up with whatever happens to come out — like, let's just build this on Firebase and
do some of my own messing around, and oh, this doesn't even need this kind of authentication and
that kind of API — and then, I can't be bothered, I'll just grab it off the shelf
instead. [chuckle] So, so it's sort of like that
noob-level-plus thing, that's what it is. And there's still stuff like demo sites or
some small group things you need to log into, so it's not just totally
wide open — and that's incredibly handy. And then it still
keeps improving a bit, and they clearly run all those language models a bit
crosswise under the hood, so every now and then you get a proper
bonus dose of intelligence, when they happen to run it on some better model instead of dropping down
to some cheap knock-off model. But what you said about Cursor a moment ago,
now the great and mighty Elon Musk took it into his empire, so
as we're recording this, they've released an X MCP, and then they
run that into Cursor, so it's really interesting what my
friend Musk pulls off there with that combination of Cursor and X and
Twitter. Yeah, absolutely. I do have to pick up a bit more on
this Lovable thing, because right now
Laki.ai's development has definitely been
mainly done by Juuso from Vesanto, who, uh,
a few years ago — before Lovable even existed — showed me
this kind of business case, where he described what Lovable
has done in practice. And whether we should maybe build that. [laughter]
We ended up deciding together, as a group, well, maybe we won't build
this. Yeah. There's Replit and others out there,
sure, that's eating into that market. But I mean, even Cursor
was bought for 60 billion dollars, which is 30
times revenue — and when X was acquired, at first they didn't even have to pay
with hard cash, it was just a 3% dilution into SpaceX, which is just
an insane amount of paper money right there. So surely something
like that will turn up for Lovable too, out of this, as long as
it doesn't take too long to pay off. Yeah, right. [laughter] But yeah, but that's
exactly the thing — if you think about what Lovable has done
smartly, it's specifically those smart default choices. So they have
good default settings. Whereas — I don't know, I haven't used Replit
myself in a long time. I don't know what its status is these days, but
at least earlier on, it used to leave
the user with quite a lot of decision-making
power there, which was maybe exactly the kind of thing users didn't
want to do — they didn't want to think about what kind of
environment this is being run in, they just want good defaults and then
exactly that, and they've done that really well. Yeah. And so far it hasn't
leaked badly. So just that — the authentication stuff and the Supabase instances — and then
they've held up to scaling pretty well, they haven't
blown up so badly that you'd have had to pay
a ton of money for it when suddenly 10 million
users are hitting some database — somehow they've
managed to handle that too. And then I think they've also, in a way,
in that harness layer — that dumb chat text box — they don't
tell you what model they're running there, but they clearly always make
that kind of trade-off between cost savings and then, on the other hand,
whether you get quality out of it — and I think they've handled that too
pretty well. As a rule, when I put in
a smart prompt, what usually comes out is pretty sensible
stuff, not garbage. Whereas if you put, say, Sonnet
in there as the Claude model, you might actually get some
surprisingly bad output — like [chuckle] — if it's just a bad token day and it
doesn't feel like running on that 4.8 Pro, or whatever it's called, that
ultra-ultra-effort setting. Yeah [laughter] yeah. On that note, actually,
it's an interesting day today — yesterday evening
actually, when I was reading the news, Anthropic announced that Fable is coming
back today,
just like Sonnet 5 came too — yeah, Sonnet 5 came out yesterday, and Fable
is coming back here. Ah, has Trump had a good day, then, so that
apparently — ah, okay. Well then, full speed ahead,
because I was still in that post-Arctic15
hangover haze, so my last Fable run was mainly YouTube
subtitling for this channel, so, honestly, that's about
where things stand. Yeah, I have to say, luckily, when Fable
was released — if I remember right it was available for three days — I hammered
away at it pretty much morning to night, and I have to say it was truly
a significant — based on my own testing and
experience — a significant improvement over anything
before it. Right, and you had that slash-make-me-rich-make-no-
mistakes thing. It made money land in the account from a single prompt.
Yeah, and there's — this ties into [laughter] — I think there's a
kind of interesting story here, like what, or
this kind of objection I actually run into pretty often
from software developers. A lot of them say this model is already
good enough for me now, so
I don't need a better model, this is enough for me. To which my
question is, well, are you at the point where you just give it instructions in the
morning, and then
check the next day's
standup to see what's been done — and then they're a bit confused, like, well no,
of course not, I write functions with it.
Yeah, yeah. Well, I think sometimes you just hit enter too [laughter]
as in, like, 'dangerously appro—
ve.' The thing is, I personally see an insane amount of value specifically in
the model's intelligence improving, because it enables — and now the point is,
how you should think about it, I think, is just that
we're still not at the point where I could treat
a model like a good software developer. And that's exactly
what I want. Yeah, I want to tell it, like,
in a weekly meeting, hey, here's this week's tasks, and then we've got — whether it's
a daily, you know, a 10 or 15 minute check-in where we
look at how it's gone.
Mm. So then the thing is, that these
current ones are good enough — well, no, definitely not, because I want
Fable back quickly, and luckily it's coming today, and then we've also got
GPT-5.6 coming, which is interesting too — some people already have it
on the approved list there. Exactly. Yeah, some already have it,
but hopefully it's coming for us too, yeah,
us regular mortals, probably within a week or two from around now.
So we'll get to that shortly, but I have to say, when I started
down this rabbit hole back in January, I of course picked up Claude Code
and Cowork, and even switched to Mac because of it, since it didn't run on PC, so
all that — I liked Claude's philosophy compared to that. Codex, well,
already one-shotted things back then, but I liked it when I sort of
learned that you have to actually watch Claude a bit as it fumbles around and
keeps looking things over and making choices, then hitting enter, and sometimes going into
the shell [chuckle] and having a look at what
you need to press yourself, and oh no, that's not working — you take screenshots or
paste this text back in, there was an error and so on. And that's kind of, for me,
still a basic principle. And then, back when Claude
ages ago switched to this token-based pricing, I decided to
just cut OpenAI out of the picture, because it started
costing real money — the first day alone was like 90 euros, so I thought,
just drop it completely, and go with OpenAI's monthly plan for Codex as my
daily driver. But at the same time, as I started using Codex,
[clears throat] it really is kind of scarily good at
one-shotting things — you give it the task, hit enter, and
off it goes, and pretty solid stuff comes out at the end, so I'm not
totally sure I like that. I think I'd still rather be the artist
who steers the ship every now and then. I don't know if you have the same
experiences. Well, yeah, I do. [laughter] I mean, as personalities,
Claude — or if you think about the Claude models
versus these GPT models, they're really different. And I do
share that experience, that working with Claude is much
more pleasant, so
it's just a lot more human and
more considerate, and it actually explains things — and that's maybe hard to explain.
But there's actually a pretty big difference in how it feels to work with, whereas
then, the GPT models are a bit blunt, or kind of
arrogant — it's like you've got some kind of [laughter]
really senior type, like some professor who's
a bit high-strung, like, you're not really — you're a bit
on edge when you go to tell it something, like hey, could you
do this now? Yeah. This actually, by the way, Claude —
when it went from Opus 4.6 to 4.7, it was a shock to me when this HR
lady showed up, and then this security guy showed up too — or the other way round, HR and security
lady — it was like, well Sami, it's already 8 o'clock, haven't
we already done quite a lot of work here, maybe continue tomorrow — and I was like,
what the hell, it's none of your business how long and how hard I
work here. And then the other thing, this security business, where
it flashed up something about an API key, like you basically need to delete your hard drive and
go get a new key again, like this isn't going to fly, this is just
a terrible security risk — and honestly, with both of these, I was like, I don't
want any of this at all, and that was honestly part of the reason I jumped
over to the GPT world, because they don't really have this stuff there, so —
Mm. Yeah. [laughter] But that — there's actually been a really
interesting blog post from OpenAI just a few days ago,
they published. They'd looked internally into how many tokens
from all their employees' AI usage
come from Codex versus ChatGPT. Yeah. And there had been a really
dramatic shift there — it was something like over 90% of all the tokens,
basically all the output that these AIs produce inside the OpenAI organization,
comes from Codex, even outside of software development. And
this is something that I don't think has really
in Finnish organizations, OpenAI - look, they go through Codex
so no one's really caught on to that yet. [laughter] But the thing is,
I think that's how it is - or actually, I do know that's how it is,
that people just use the chat.
Mm. Like if we look at it this way,
inside OpenAI everyone uses Codex. Nobody really uses the chat -
chat usage is minimal. Whereas in Finnish companies,
and maybe in general, aside from these kinds of
tech startups or whoever, who are actually
in the AI world, AI adoption is completely
still in its infancy.
so what's needed now is a shift away from doing
just little one-off things in chat. Yeah. Toward actually being
well, if you're in the OpenAI ecosystem, having the work actually happen
in Codex itself. Yeah, that's a good point. And actually
now we get to that harness thing, or the OpenAI setup, or actual AI
workers - not just these kind of dumb, in-between,
whatever agents. So maybe two themes now - if these
are the current front-runners - let's leave Google aside for now, even though they're
trying to get into this game with Antigravity. So you've got
Claude - Anthropic started that game already back in early this year. So we got that
Claude Cowork, and along with it came the desktop app, so you've got the
chat, then you've got Claude Cowork, which gets you into those office folders
to work in, and then there's Claude Code. And at first for me it was like, I
went from the chat into Code, and then I went into Cowork. Now I don't
really know where I even want to be. It's almost the same whether I'm
in chat. Actually I pretty rarely go into Code, because if I'm running
code, then I go to the command line interface, which is the
text-based thing. And then OpenAI's side was kind of whatever, before
Codex came along - there was just the chat and the command line, but
I never really used that. But now it's kind of
like this: I actually run quite a lot of coding straight from chat,
but then I go into Codex if I want to one-shot something. But then I
run things with my own Samantha on OpenClow. So OpenClow is this kind of open-
source project that was built by this guy, Peter Steinberger,
an Austrian genius. So I picked it up like a week after it was
released, and it runs on my Mac - this Samantha, which runs on its own
physical machine, but it's clearly just hammering away on Codex the whole time
because it codes and uses that as its main brain. So, um, I still
think of it as the harness - you've got this smart agent, OpenClow,
that's still a really big deal for me. But are you following
this idea of the desktop, these three pipelines, and then this kind of
agentic building block - how do you actually see access
to that intelligence. Well, especially if we go into this
context of an office worker or knowledge worker,
who works at some corporation,
then I think the shift there - the first thing that needs
to happen - is getting rid of Copilot.
Yeah, it's terrible. And it's funny because
I've done a fair number of these trainings over in Saudi
Arabia, and I don't run into this problem there. It being a religious country,
yeah, over there it's either
companies operating in the OpenAI or Anthropic ecosystem,
but I've never once run into this thing I keep running into in Finland,
constantly. And the worst part of all of this is
that we have companies that say, oh yeah, we're right there
on the AI front line, and then it turns out, yeah, we've got Copilot
here. [laughter] And Copilot
has nothing to do with actually leveraging AI
in any real sense. So that'd be my first piece of advice - [clears throat]
you have to get rid of Copilot. And then
as for what good alternatives there are right now, I think the best
options are, like you said earlier, that Google has kind of dropped
out of the game, so you've got either the OpenAI ecosystem or Anthropic.
So you pick one of the two, and once you've picked one, you should try to
make sure the work actually happens either in Codex
or in Claude's Cowork - not in the
chat, because this is exactly the point from that OpenAI blog post -
yeah,
that the chat is kind of like, if you have a question
you want an answer to, or some small thing like that, then
chat's fine. But in other cases, if you're actually going to do
knowledge work, mm,
and you're not a software developer - for developers, of course,
it's Claude Code or Codex, but now if you're
say, someone doing marketing or sales,
that kind of person, you'd notice, okay, my
usage - like maybe 90% of it - if you're in the OpenAI ecosystem,
then you look at your monthly usage and you realize, okay,
90% of my usage is in Codex. Mm.
Then you can say, hey, that's going pretty well.
Mm. So that's maybe as an intro, that, well,
then, um, these - I wouldn't necessarily dare recommend to companies
these OpenAI solutions yet, at this stage - although now
Microsoft - I haven't followed this super closely, but Microsoft
is now, on some level, offering companies specifically this OpenClow
Yeah, exactly, and actually Peter Steinberger also released
it for mobile too. So yeah, it's coming - look, since Steinberger is at OpenAI now,
he's not just some renegade kid anymore, cranking out a bit of
sloppy code on the side - no, he's properly, like, right there inside
the OpenAI empire. But maybe, in a way, the desktop thing is
kind of interesting to me, because it might be that browsers
basically die out entirely, that it's Anthropic's and OpenAI's and Google's
own apps, once they get things sorted out - those become the portals you go in through
and yeah, and that's actually a good
point to raise - when I talk here about Codex and Claude Cowork,
these, um - you can actually use Codex through
the browser too. What I'm specifically talking about here are these
desktop applications,
meaning the desktop apps you download onto your machine and run
things in. And that's exactly it - it's really interesting
to think about why you'd actually want to go yourself
to some website - like, does that need go away?
It probably won't disappear entirely, but it'll shrink really, really significantly.
What's interesting here too is that at least I have a really
hard time finding tasks anymore
that I'd actually go do myself. Like,
what would be a thing where I'd actually be better than Codex?
[laughter]
taste, and long-term goals - 'make me rich' or something like that,
[laughter] you need some kind of long-term vision, so that essentially the
task-by-task work versus, say, what happens a year from now - the gap between those is
still significant, definitely. Yeah, and yes, exactly,
so even if you get there a year from now one task at a time, and
even if every single task were done entirely by AI, you still need to first
have some kind of direction. Yes. Yes, absolutely. Yeah. Well,
that's the value a human creates, but the thing is, that you
yourself doing something routine - like, this
task-doing is maybe exactly the thing where nobody should really be doing any
tasks themselves anymore - they should really be left to AI. And I mean
right now we're doing a huge amount of all kinds of tasks
which is honestly kind of crazy. Yeah, I think, if you
think about what Musk did back with Twitter, or X - he
threw out like 70 percent of the staff
back in a time when we didn't even have these tools yet,
so I think it's genuinely hard
to find organizations where you couldn't throw out something like 70 percent
of the staff and still have things carry on pretty much as before.
Yeah, that's brutal. And the other hope people voice is that everyone
would adopt these tools, and then everyone could basically keep
their jobs and just scale things up tenfold instead. But
the more likely outcome is exactly that - scale does grow among the ones
who remain and who push hard, but then, in a way,
people fall away too - and I think it's worth
summing up like this: if your role at work is that some colleague
or boss or client comes and says 'do this for me', hands you the task, and
then you do it and hand it back a bit later if you remember, then
that's not a great position to be in, because then you're basically
pretty easily replaceable by AI inference. Yeah, that's
[laughter] exactly right. Do you remember, by the way, there was a big fuss about that
back when Mikko Alasaarela said on some podcast that he had
calculated how many employees like that Kela would actually need, and it
turned out to be some incomprehensibly small number. Like it was 18 or something.
[laughter] Yeah, and 8,000 was the wrong answer,
whatever the real number is, but yeah, they really should have
ordered some 500-million Salesforce license, so right away, right away
they went about the organization's task management the wrong way. But
yeah, Mikko Alasaarela has been on the show many, many times, definitely.
Although he's mentioned getting this AI brain fog - like, I haven't
found it as fun yet as, in a way, this being a return to childhood,
where I always get excited getting into it, having everything
set up and available, and it's genuinely a joy prompting in some cool
tasks. I think that's great, but yeah, I'm still
working it out too. Yeah. And, [laughter] I really do have to give credit,
even though, um, I'm generally maybe a bit of a
well, or I do enjoy building these agents myself,
but the thing I'm a bit skeptical about, and what I don't
really like, is that a lot of these AI influencers give the impression
that in order to actually get value out of these tools,
you need to be, like, some kind of guru and build some agent farm
and stuff, that it's really difficult.
Whereas I'd argue that in most cases, or even
almost every case, you can just take something like Codex or Claude
Cowork and get pretty much the same results,
if you just learn to use these off-the-shelf products well,
right out of the box. But, um, back to the point - Samantha,
which is this kind of divine, legendary being you built, that can
do human-like magic - it's exactly that kind of magic box. You write
whatever you want, and out comes the best answer in the universe, right?
Yeah. Which [laughter] works. Yeah, which works in WhatsApp, specifically.
And I think that's, again, a genuinely clever way
to use agents, because the added value, in my opinion, isn't
that if you ask it questions, I haven't seen that the answers
are meaningfully better than what you'd get from, say, Claude or Codex.
But where a lot of the value comes from is that it's now part of that
community. Mm. Because something like that isn't
possible anywhere else - it requires something like
WhatsApp specifically - I think the WhatsApp channel is
a great place where people and an AI like that can
chat together, and Musk has actually kind of copied you over on X,
in that Grok is there on X-
slash-Twitter, in the discussions, and I think it works
really well there, being part of that kind of conversation,
so it can even correct Elon Musk's own statements - you can call it in
to comment based on Grokipedia or whatever
else, on whether what's being said actually makes sense. Um, but yeah, this Samantha thing, um,
by the way, it's this - [laughter] - shameless plug - when you subscribe to
Neuvottelija Insider and the bonus content, you can
then get in touch with me, and once I check that you're a real
person and not, um, a bot or some malicious infiltrator, then
I'll invite you into this closed community of over 200 people,
the Neuvottelija Insider community, where you can tag @Samantha, and from there
you can ask literally anything and she answers so wonderfully - and by the way,
everyone's been trying to mess with her for months, trying to crack her, to get her to reveal
Sami's, uh, salary, or [chuckle] some file, or, like, pay, or
whatever - but no, it's a beautifully built data structure, [laughter] but
just a little plug there. But maybe, maybe a bit of skepticism - I appreciate that. It
earlier, when we talked about this over lunch - a lot of people build
it kind of however it happens to come out, so it doesn't add any real value, or
it just ends up being another new communication [chuckle] layer, and
at worst it's a security risk between you and the actual, real AI, i.e.
say, Anthropic's best model - and in that case there's no point to it at all.
So that's when it's just a dumb wrapper. But then
if it has memory - so I was about to mention Andrej Karpathy's
brilliantly envisioned wiki memory - it's able to itself
pull from file structures that are semantically
organized, curated, and nuggets of information I've burned into it
collaboratively, from notes. Then there's this extended
context memory - meaning even if there's a WhatsApp channel with
a lot of text in it, usually the tokens [clears throat] run out
because it can't hold more than a few messages in that short
context window, so it extends that up to a file-level,
kind of lossless context memory. So that's already
a huge deal - it can see the whole conversation space, not just
the last couple of messages. And then there's the secret ingredient, which is
this Memory Lapse graph memory, which is exclusively between me and
it, and it stores things semantically without any of that
RAG garbage - just clean data structures. And then there's the five-layer
security system that keeps malicious attackers
out. So, yeah, it's lovely. I've been tinkering with this for the last four
months, and it's pure joy just playing around with it, so I
do like it, even though it's a bit silly, so
but it's a great hobby. It's great, and an extremely useful
hobby too - but the thing is, my point here, maybe,
or my main point about why I'm a bit critical of these
personal agent setups is just that some people are left with the
impression that to use AI effectively
you need to spend like 10 hours a day tuning these things. Yeah. Which just isn't
true anymore - and maybe it was true at some point, back when
yeah, those crash-prone setups were really fragile - but now it's
good enough that, yeah, you can get really far with
just off-the-shelf products, and these days they get you
a whole agent farm to work with. I mean, with just a basic Claude license
and Claude Cowork, you fire off some genuinely big task in there. For example
Cowork just made me, like, over 150 of them,
of which quite a few turned out genuinely usable - so it fired off
an agent army - it wasn't just one Claude doing it, there was a whole agent army,
working away on our slides
without me having to do anything, like I didn't have to
build any kind of agent system myself.
It's just that you get it all, like, if you want
an agent army at your disposal today, all you need is a Claude subscription and
off it goes, and yeah,
there it is. Yeah, so, I guess I started this back in December, I
built a kind of second brain - a better notebook - and put
it, at first, into this free tool called Obsidian, an MD-file-
based journal system, for my precious observations about the world, and
then I started building these skills into it, and then I
noticed that both of these were kind of dumb - like, my terribly
brilliant insights about the world that got curated into that journal,
well, maybe they weren't actually that brilliant, and they went stale pretty fast, and
they just ate up the agent's memory, since it had to figure out what
Sami had thought about on some other day back then, i.e. in December 2025, and then those
skills got even worse, because they basically just got in the way of the agent's
much better general intelligence, so I just deleted all the skills, completely
cold turkey. And then I also deleted this, like, my
notebook - but I kept it around for the assistant, in a way, so that now if
genuinely curated nuggets of information or important stuff comes up, then it
gets burned into the Karpathy-style memory. And I think this has actually worked pretty
well. Yeah. So even though general intelligence keeps improving all the
time, there's still a role for a 'Samantha',
one that you still can't quite get off the shelf.
Yeah. Well, let's put it this way - maybe not quite. So now there's a good question,
which is how far we are from that point, because right now
actually - OpenAI, I haven't looked into it that deeply myself, but they announced
recently that they've overhauled their memory system. It was
previously actually implemented pretty poorly, and I didn't even
keep it turned on myself. But now there is, yeah, um,
at least according to their own marketing, they have
a considerably more advanced memory now, where, and then you can
have separate memories for different projects, or you can
use the global memory - so they really seem to be coming on strong now
in this area, and the thing is, it might get hard to compete with. I mean
right now it's the case that at OpenAI,
and at Anthropic too, they pay pretty solid salaries, which attracts
quite a bit - well, Andrej Karpathy went there, and Peter
Steinberg went over to OpenAI, so basically these
memory-world gurus are on both sides now. Yeah, and actually
OpenAI - the whole 'Open Glow' project can be seen as OpenAI's
public development platform for their memory architecture, so...
Yeah, yeah, exactly, [laughter] I mean, it can be, it can be
challenging, but still, I don't think it's a lost cause - because even if
- well, even if it turns out that these so-called general-purpose, off-the-shelf
products end up being, and probably soon will be, better than what you're able
to build yourself, I still think that's really fun.
Yeah. And a really good learning experience,
because in any case, even if those
off-the-shelf products turn out better, you've at least learned how to use them. You
get to use those off-the-shelf products effectively, which gives you
a huge competitive edge in this day and age,
where knowing how to effectively
use AI is honestly an insane competitive advantage right now.
Yeah. And there's still some of that anthropomorphizing magic dust to it too, in that
more and more people know what a 'Samantha' is. In a way, that's, in a certain sense,
a skill in itself too, that I can - I've swapped out the brain from Anthropic for
OpenAI's, and just for fun I set up DeepSeek too as a backup model, and then
say, whichever's the era's best
video generator models - the point being that the harness isn't
model-locked - or rather, I can swap it with minimal hassle
from one camp to another, or one API to another. And that, to me, is a
genuinely strategic defensive capability. And then I could go full bunker mode.
I've actually been thinking about buying, like, a 256
gigabyte unified-memory Spark, if I go the Nvidia route, or a Mac
Studio - once I've got one running, bunker's ready, internet cut off, and
everything runs 100% on local inference. So basically
the thing is, since I don't fully trust that if I go with, say,
Claude, they're going to try to build a moat, so that you just can't
switch over to OpenAI - let alone Gemini - or
Chinese models, not to mention. So self-sufficiency is, in my view,
also a strategic competitive advantage. Yeah. Yeah, absolutely. Yeah. And
nothing is as fun as tinkering with local models.
Yeah, though it can be a bit of a letdown too. I was super excited - like,
when Google put out Gemma 4, I thought, now we can do anything with this.
[chuckle] Turns out you couldn't really do much of anything
with it at all, [laughter]. Yeah, it's - I'm at the same
time disappointed and happy about open source development. Like,
disappointed right now because it's a fact
that [clears throat] the gap is pretty big. If you think about, say, Fable
or Mytos, and then something like GLM 5.2, which is maybe currently
the most advanced open source model, unfortunately there's still something like a 16-to-12-
month gap. Mm. And that makes me sad. But
then in the same breath, now that we've seen this
US policy, where, where we're being blocked from access to these
best models, I'm honestly insanely happy that
we do have a reasonably strong open source
ecosystem and movement. Because if it turns out that
these, for example, end up being available
exclusively to US citizens, then we're not going to be
left dropping into thin air here, so
yeah, that's such a juicy topic that I'd suggest Aleksi Paavola and I
head over to the members-only side. I'll put the Laki.ai
link in the description - I'd recommend it, maybe with one small
security caveat: when you upload confidential
documents into systems like this, it's worth
pseudonymizing or anonymizing those documents. If there are
client or people's names in there, then change them to, say, 'Person A1', and
then convert 'Person A1' back to Sami Miettinen afterward, once the run is done,
if it's genuinely, truly a security-
sensitive matter, like insider data on publicly listed companies.
But otherwise it's a really good tool, and reliable, and this is
genuinely amazing work - that in the spirit of open source, the
end result doesn't end up being monetized by, say, some Alma Media - you've
gone and opened up that legal interface for us
Finns. Yeah, this really is a good, good cause, and
it's genuinely such a sweet thing that now we're
able to offer a fully competitive, or even
better product, for free, to consumers, when the equivalent
bought from Alma Media would run several thousand euros. It's just not
reasonable or realistic in any way for some individual consumer who
has some legal hassle to have to pay several thousand
for something like that. And then these Legora and
Harvey-type tools, which might not know the first thing about Finnish law, well
then that's still another option for the consumer too, so for this
consumer it's - and then, for companies, I think it's worth
spending a bit of money on the use case, so that
you build proper encryption chains. Actually, one thing that [chuckle]
I could do myself too - this anonymization wrapper, I think, would be
something worth building, maybe as open source, because
it's the kind of thing that makes me a little worried about these MCP servers, if
people go and upload documents that are too sensitive, then there's
still, at least in theory, a small security risk.
Well yeah, that's exactly right, that's how it is - what we're aiming for now is
that the limiting factor would be your Claude or ChatGPT license.
So if you had, say, a Business or Enterprise plan
from Claude or ChatGPT, you could put all the same things into it
that you'd already dare put into email or OneDrive anyway.
And that's where we're going to be with Laki.ai pretty soon too. So
then you'll be able to put in those, kind of, sensitive things,
the same as you can now put into public inter- or, well, kind of
into systems connected to the public internet. Like, for example,
email - you can put them in there.
But of course, you still have to trust
whichever it is, Anthropic or OpenAI - so that's maybe something everyone has to
personally weigh, what they actually want to put in there. And
then, I don't want to feed the security-doomsayer
camp too much here, but then, if it's a public
use case that requires an EU server, then yeah,
Anthropic's cloud might well be over in the US. So in these
special situations - and you users out there know who you are -
it's worth actually reading the fine print of these systems
carefully.
[laughter] Exactly. Exactly.
EU legislation is going to be in Laki.ai pretty soon too, so
and any day now the AI Act's final
implementation is coming. Luckily the Omnibus has kicked some of the worst
messes further down the road. But you can save on legal fees
by hooking up the Laki.ai MCP and
yeah, getting familiar with things
yourself with AI. Great. But, thanks for this, Aleksi Paavola. This
turned out to be way more interesting stuff than I expected. It's always great when
I haven't googled the background too much beforehand - you always get pleasantly surprised that you're
right, like, at the very core of the scene. This is always such a great
thing. So, thanks for coming on. Thank you so much. This was a lot of fun,
and, and if you've watched or listened this far, go ahead and subscribe to the
channel, and let's head over to the members-only side and think about this data
sovereignty topic - I thought that was so much fun, so on the members-only side
we'll spend a bit more time thinking about whether Europe still has
any say in this at all, and what happens if, say, some future Trump
cuts off our access to the better inference - so, see you on the members-only side
then. Thanks. Thank you.
[chuckle]