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All right, welcome to the Neuvottelija channel, Aleksi Paavola.

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Thanks so much. And we didn't really know each other a week ago,

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we'd seen each other's conversations over at Agentics Finland a few times, but,

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you'd done such a great piece of coding that I thought,

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get this guy into the studio right away — and luckily it worked out. So could you

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start by telling us a bit about your background — what you do and where you're coming from?

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Yeah, absolutely, great to be here. My name is indeed Aleksi Paavola, and I've been

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in tech for a good 10 years — actually probably closer to 15 by now —

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as a tech entrepreneur basically my whole career, and I've founded and sold a couple of

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tech companies, and for the last four years or so I've been building

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AI stuff, and also doing software development and

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software consulting. Now mainly around

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these language models, on top of and around them, all sorts of things — I've done quite a lot,

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been in Saudi Arabia running trainings, and I've also

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trained Google's Europe FINA team on language models, [chuckle] and then I've also been in Oslo

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representing Google's language model cloud solutions at

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a seminar with over 10,000 people, and there's been

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all sorts of interesting stuff. Cool — actually I had Topi Manuas

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as a guest. He moved to Singapore and he's been doing Google's cloud sales,

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and we talked about Google's multimodal solution. Now I'm

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well, even though we probably both use Claude more,

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and Codex — but somehow the Gemini folks invited me along there,

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when they launched Gemini 3.0, we were at this really exclusive

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event — it was champagne — and they released the model an hour before the global

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release, and I've always had this soft spot for

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bribery, so Google feels like a really nice company, and they have in Finland,

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in Hamina, real investment going on — yeah, that's actually where

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in Hamina I was running this training — Google's Europe FINA team

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gathered in Hamina for a seminar like this, and we went through a bit of

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the basics of language models. Hey, that's awesome. You're honestly a bigger deal

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than I thought — I thought you were sort of roughly on my

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level, but now it's getting a little scary. But anyway, the reason I

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invited you here wasn't about Google at all — it's that you've built this

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open-source project, this better

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Model Context Protocol tool called Laki.ai, and I started using it right away,

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and it works great. Before that I had something else filling that gap,

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something called Oik, but that was clearly worse. So you've built

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this better tool, but explain — what exactly is Laki.ai, and what

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is MCP? Right. Thanks a lot for the kind words. It's

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come up from other places too, actually — that listeners should really go

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and try this out for sure — but, um, this project actually

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got started with Ilkka Linkoneva and Juuso Vesanto —

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we tried to solve this challenge, where

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we'd identified that language models were going to produce a huge amount of this

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legal documentation. And then people would still need to read and

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understand it. So we developed a solution for that. This was already a few

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years ago now. And our hypothesis was that if we fed into our

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system, say, 100 NDAs, a lawyer could then highlight

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some section in Word from a new NDA that they've

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gotten from a client, and then see, from the ones they'd previously approved themselves, um,

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the corresponding section in those NDA documents that's semantically closest

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to it, and from that easily approve it, like okay, I've

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approved this kind of clause on basically the same terms before, so this is definitely fine. Well,

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the surprising challenge that came up here was

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lawyers' ability to express things in a million different

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ways. [chuckle] So these ended up just full of redlines. So now

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when you — like in that test we had, we had about 50 of these

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different NDA agreements, and when we took a new NDA and you

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highlighted some section in Word, it was always just

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red with redlines. The phrasing and the terms somehow

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these lawyers had worded so differently that the tool didn't end up being very

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useful. Well, around the same time this pretty fortunate thing happened,

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from the perspective of Finnish citizens, actually — Finlex,

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its data became openly available for everyone to use. So

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[chuckle] this had been — and this was, this was March 2015,

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I think it was, and before that, I remember there was some big software house

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that was doing an AI project with Finlex's data, and

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it fell through over this licensing issue. I think Alma

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Media was holding onto it too, too tightly,

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but then in March it got freed up, and our idea was that

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we'd build an interface — like a better interface — for Finlex's data, um,

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with no commercial purpose at all. Just mainly because we

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could, so we'd do it, and we figured this was going to be a hugely

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useful service for citizens, of course, and so we built Laki.ai,

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launched it back in 2015 sometime in summer, and it's still — it's just a

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traditional web service for Finlex's data. So it has

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laws, statutes,

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there are court cases, government bills, and what sets it apart

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from Finlex itself is that these documents are all linked

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to each other. So when you open a specific section of law there,

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for example, you immediately see, okay, this is linked to these government

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bills, and then this is linked to, say, these seven court cases. Mm.

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So that's basically what we built, without any bigger plan for

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anything beyond that. Well, now, fairly recently,

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um, we had this idea — well,

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could we build something for an agent on top of this, and

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[clears throat] we looked into it a bit and concluded that

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yeah, this would definitely be possible, and that's what got

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this Laki.ai MCP started. And what that actually means in practice

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is that now, with your AI tool of choice, you can connect it

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to this Laki.ai database, and now your agent

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can use all this legal data that we have there

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in the Laki.ai database. Yeah, yeah, it got straight into my Claude, uh,

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tech stack — though there was a bit of fiddling involved, actually,

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let's just say, right at the start we'd invited — along with Aku Nikkola —

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from Legit here — he, well, he wanted to spend, understandably, a long stretch

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of summer vacation afterward, so he couldn't make it here, but around the same

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time he'd released this — Claude has this legal

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skills thing, which is kind of Anglo-Saxon know-how, and he'd then

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built, with his team, a kind of skills library that has

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the special features of Finnish law — starting from the language, like how a section

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works, and so on [chuckle], and he published it. I took

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that into use too. There was a bit of fiddling involved, actually, because for some reason Claude

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had changed the connectors a bit, so I had to go down to the command-line

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level to shove it in, which was a bit annoying. But when you

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combine these two skills — this skills library, which in my

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opinion is actually pretty good, I'd recommend that too — and then this

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Laki.ai, you get a really nice combo for Finnish

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legal needs. I don't know, have you had a chance to test Legit's

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skills yet? Yeah, definitely have. And maybe

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I could add a bit more, since you asked, to open up

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a little more for listeners — MCP isn't necessarily super familiar to everyone.

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So it's an open-source protocol that Anthropic — the company behind

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Claude — built and designed, and it stands for Model Context

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Protocol — that's what MCP is short for — and in practice it's a way to connect

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different applications for agents to use. So maybe the closest

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example would be something like USB-C. So if you think about your

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computer, and you want to plug a keyboard into it, you plug

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the USB-C cable in between them, and then, simsalabim,

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you can use that keyboard with the computer. So this is

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basically kind of the same idea — when you connect your

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Claude, or soon ChatGPT too, which we

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have coming for Laki.ai — then

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your Claude doesn't need to

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hunt for legal sources on the open internet anymore, which is pretty difficult and

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easily leads to errors — instead it can now use this database

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we've built. Yeah, that USB comparison is good. So

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as I see it, MCP is kind of a more refined thing than

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a regular API call — Application Programming Interface — and the problem with those was

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that everyone called them a bit their own way, like pulling

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data from HubSpot into Word or whatever — so it kind of created

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this sort of port standard, and it eats up some bandwidth, and it's

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a bit too complicated for really fast calls,

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but it opens up all the interfaces really nicely, so —

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did I get that roughly right? Yeah, pretty much. It's just

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that, um, behind MCP there's basically always APIs. So it's a

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protocol built on top of those APIs, designed

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specifically so agents can use them efficiently. And

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yeah, what you said is completely true, that there's been

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[chuckle] — you said there are some challenges too, and that's completely true. And

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MCPs have gotten a lot of criticism exactly for filling up the

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agents' or language models' context window, but these have

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improved quite a bit, and what's actually improved the most,

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is agents' ability to actually make use of these different MCP

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services, and it seems like — I myself was fairly skeptical at one point

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about the future prospects of the MCP protocol.

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But now I'm a bit more positive again, it seems like maybe we're finding

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solutions where these agents can now efficiently make use of these

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MCP connections and services that

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are hooked up via MCP. Yeah. And in a way, since

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the level of inference, or AI capability, keeps rising

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all the time, it's not really a problem to build even really weird

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pipelines. Like, AI will build basically any kind of, uh [laughter]

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ancient pipe between some old database and a Cobol system, as long as you just

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shove it in there, because they're just capable of that — but those are fragile, and MCP

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is a standard, and standards are good for a reason, because they

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evolve too, in that way — and it's nice to hear that it's also become

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more efficient, that the standard itself has developed along the

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way. Yeah — you say 'agent' a lot, and actually I myself

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had a little chat about this over lunch earlier, about whether

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agents — in the sense that I want to understand, like, this same kind of

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AI employee — are they really that useful? Maybe

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we should save that conversation for later. I think we have a bit of a different view on that.

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Um, because personally, what you might

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call an agent — like some kind of thing sitting next to an MCP,

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some subroutine call — I wouldn't even publicly call that an agent. It's

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just some subroutine running there. Not an agent at all. Whereas these

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agents are more like same-level AI employees, almost god-

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like beings that can do high-cognition work,

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not just sit in between two things. I don't know

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if you want to jump in on that already, [laughter]

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I can jump in a little, um, to the extent that, if we

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think about the direction ChatGPT and

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Claude have moved in, they've become a lot more agentic. If you think about

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how, say, the very first ChatGPT, back in November '22,

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that released version worked, it was always just — you'd write some

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prompt, the prompt got sent to the language model, and the language model

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sent back some answer. But now — and the way I think about it —

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the difference between a language model and an AI agent is that in an agent

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the language model is used in some kind of loop. So if we think about, say,

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essay writing, a non-agentic

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way of writing an essay would be that you have a prompt, and out comes the

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finished piece the moment you feed the prompt to the language model, whereas in the agentic

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process it evaluates its own work along the way. So it writes

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a first draft, say, and then it checks, okay, maybe this

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second paragraph needs to be strengthened a bit more, or the ending is missing

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some punch — so now ChatGPT and Claude have moved

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in this direction, so I think in a way you could even call these chats

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agentic

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systems already — but of course it's true they're not agents to nearly

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the same extent as, say, Claude's Sonnet, or Codex, or

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Claude's Cowork, or Claude Code. Yeah, but that's honestly, scarily, always

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moving toward that same point — for anyone who hasn't yet

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heard the term — this kind of open AI employee that has

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its own three-part memory structure and very advanced context

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management tools — so we actually have to get into this context concept,

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because you said the MCP call eats up that precious context, and

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we're already at million-token contexts, basically as

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standard models — Claude's and, um, OpenAI's base models run

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on a million tokens, so it's less and less, in a way, that short-term memory

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eating problem here that's disappearing, and if the MCP model gets even more efficient,

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then this is a vanishing problem overall. How do you see, by the way,

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this — um, I've actually had guests who like to run

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the 'dumb' models on purpose — like, it's not a bug, it's a

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feature — that they run this 'dumb' inference on purpose because it's

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easier to understand, and it's actually good in a way, that there's not too much

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creativity or harness layered on top of it. Do you have a philosophy

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on that? Well, yes, yes [laughter], very much so, maybe, maybe

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as a basic piece of advice I'd say that people

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should, on a general level, pay for these AI services, so that

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nobody should be using the free versions of these for work — because

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paying gets you a lot of value for your money, and these are still

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relatively affordable, at least for now — though I do expect that

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prices will definitely go up, and have partly gone up already — because

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even Anthropic's and OpenAI's Enterprise plans aren't exactly

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cheap anymore. But generally speaking, I think a good

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basic recommendation is to always use the best model. And that's kind of

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something people should just know — it's like

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modern general knowledge, that you know — if you think about it,

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if you've chosen to work in, say, the Claude or Anthropic ecosystem, then

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you should understand and know what the difference is between, say, Sonnet, Opus, and Fable

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is. M. And then, as a baseline, you could

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always — if you have some difficult task, [clears throat]

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you use the best model. And then again, if you have — if you're just

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asking, hey, what are some good lunch spots nearby, then it really doesn't

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matter whether it's some super-smart model. You could ask that

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to basically anything. Yeah, yeah. That's how it is — then we can

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move on to local models, where you're not allowed to do any

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tool calls or internet searches — so it's kind of very rigid, and

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in that case it can be really important to know in what way it's

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mechanically rigid, because then it can be fully reproducible, because

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in a way, maybe this hallucination that the better

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models always get criticized for — the more harness or control layer you have in

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between, or agentic loops, or Ralph-loop envelopes, or whatever cleverness you

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build in between the model and yourself, the more

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hallucination loop comes into it too. So in a way, one solution

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is to strip all of that away, to just run it directly on bare metal

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in some task that's outside your own interest, so you know exactly

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what the model is going to do.

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Yeah, right. There's actually a great essay on this — I don't know if

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have you read it, or come across it — the Bitter Lesson? No, I haven't,

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so tell me more about this legendary Canadian

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professor who wrote, years ago now, this

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essay on AI, where he argues that the value humans

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bring to AI systems

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is, sort of,

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really limited over time, and he gives good examples. So if we think about

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chess, say — originally in chess, people

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thought that humans could bring value to

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the AI. So basically, with this harness

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you could make an even better AI. But then, from chess

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or chess history, we know very well that a human can't

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add any value to a chess AI like that. A chess AI

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on its own is much stronger than AI plus a human. And

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the AI can't be helped at all by a human giving

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it tips for that kind of chess AI. And self-

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driving is a good example too — there, first they tried doing

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sub-optimization, building [clears throat] small pieces at a time.

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Andrej Karpathy has actually talked a lot about this, about how

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Tesla's strategy — which a lot of people

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have already copied by now — of training the car to drive

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so that the neural network handles the driving has turned out to be

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a much better strategy than trying to tell it

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in specific spots, sort of steering the AI

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using human intelligence. And now I believe we'll see this same thing, and we already have,

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in a lot of language-model-based systems now. If we

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think about software development, say — in software development, at first

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there was Cursor, which was this traditional

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integrated development environment — an IDE that

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developers were used to using — and when AI was brought in and

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integrated into that IDE, for a long time it looked like, okay,

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this is going to be the way — that humans can add value

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at various points along the way in software development. I myself was actually really

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skeptical about that when Claude Code came out,

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whether it could really be true that a purely text-based interface

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could work on its own. Whereas over in Cursor there was this tab

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completion — you'd write the start of a function, and then

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the AI would predict what kind of function you wanted to write. Pretty quickly, though,

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it's gotten to the point where I don't even remember the last time I did a tab

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completion like that. So it's really shifted toward these...

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these harnesses have kind of shrunk from that, and I believe this

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trend is going to keep going, and now if we look at

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say what OpenAI's or Anthropic's employees

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say — what they talk about is exactly that they're

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constantly trying to strip that harness down.

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Mm. That is, letting the

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model take on a bigger and bigger share of it. And if you think about

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the legal field from that angle, I'm actually really skeptical

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about, say, Legora or Harvey now. They're in the same

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position Cursor was in a while back, sort of. They've

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tried to build this kind of software around

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the legal side, so they could add

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some value at certain points. Mm.

294
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But I think we're already at the point where if you have Claude and

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you connect it, say, with Laki.ai to

296
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reliable Finnish legal sources, then for pretty much

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every legal-related

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task you'll actually get better answers from Claude than from

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Legora or Harvey, which have built a lot of harness

300
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between the model and the user.

301
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Yeah, that's a harsh way to put it, but hard to disagree. Personally, I

302
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actually only started coding properly again this year myself. I was

303
00:23:28.559 --> 00:23:32.480
a good coder as a kid, and I've been an investment banker for 30 years now, and

304
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I haven't lost a thing in those 30 years, because those backslashes are still

305
00:23:35.840 --> 00:23:39.520
sitting there in the terminal waiting. And actually, I do use

306
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autocomplete for that — when I SSH between different computers,

307
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tab-complete always finds, like, what did I even name this machine, so

308
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I still get autocomplete out of that, but otherwise I'm pretty much full

309
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slash-goal mode here — I mean, it's public

310
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sector, make-no-mistakes territory, just slash-goal — but somewhere there's

311
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a line, where your goal is so complex that you

312
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can't just do that — this is like an extreme example,

313
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something like "make no mistakes, fix the world" or something huge —

314
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so somewhere there's still a human path running through it,

315
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taste and intent and the steering that's involved, and — well, somewhere,

316
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in driving, or in editing a simple legal document, that

317
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works, but then if it's some multi-year, complex

318
00:24:30.320 --> 00:24:35.039
tangle of shareholder agreements between, say, your five family offices'

319
00:24:35.039 --> 00:24:39.279
portfolio companies, then, well, it doesn't quite work like that — there I

320
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believe a thick harness still has value, because it

321
00:24:42.919 --> 00:24:46.120
is just, somehow, so multi-dimensional that on a single

322
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dimension you can't play it. Um, so this kind of task-by-

323
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task, one vertical task at a time — yeah, those are getting chewed through constantly, so

324
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once enough of them are done and the system has learned, then

325
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it can pull them together holistically too.

326
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Yeah. And then there's maybe that one, sort of,

327
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dimension where Harvey and Legora are trying to compete, which is the interface.

328
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They have these UI components that you

329
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don't have in Claude, say, but I predict this will stay temporary,

330
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because generative interfaces seem to be coming on

331
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pretty fast right now. So soon you'll just be able to tell Claude,

332
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hey, I have this use case, what would be a good

333
00:25:37.720 --> 00:25:41.240
interface for this, and it'll build you

334
00:25:41.240 --> 00:25:44.720
that interface on the fly. Yeah.

335
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So the room for Legoras and Harveys of the world is going to get

336
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genuinely pretty tight. And it's really interesting, because they've sold

337
00:25:54.039 --> 00:25:59.080
it so well up to now — it's been sold incredibly well, the price is

338
00:25:59.080 --> 00:26:03.159
really steep, the contract terms are pretty long, but this is a really

339
00:26:03.159 --> 00:26:08.520
interesting situation in the sense that if we think about

340
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those law firms that have committed to these and

341
00:26:13.919 --> 00:26:17.880
built their whole process around Harvey or Legora, then

342
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along comes some smaller firm that adopts

343
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Claude instead — and especially if we're talking about Finnish

344
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law, right now in Finnish law Claude is

345
00:26:31.640 --> 00:26:35.000
just flat-out superior compared to something like Legora or Harvey, because

346
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you can't — in Legora or Harvey, as far as I know at least, you can't

347
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actually get Laki.ai hooked in there. Oh. Yeah. So it's that closed off.

348
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And they don't have an equivalent — they bought this Swedish

349
00:26:49.159 --> 00:26:54.159
startup, Kurigal or was it Kur Data, which basically has

350
00:26:54.159 --> 00:26:58.399
done in Sweden the same thing we've done with Laki.ai in Finland.

351
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So in Sweden they have that capability, but in Finland they don't have

352
00:27:01.399 --> 00:27:06.679
that capability. So legal research in Legora or Harvey in Finland

353
00:27:06.679 --> 00:27:12.600
relies purely on a web browser, and we know exactly what

354
00:27:12.600 --> 00:27:17.200
level that's at. Now a good example of this is, say,

355
00:27:17.200 --> 00:27:20.600
Harvey's own case too, where by default

356
00:27:20.600 --> 00:27:25.200
they've actually banned the model from doing this web search,

357
00:27:25.200 --> 00:27:28.080
and there's a very good reason for that [laughter], and now you've got

358
00:27:28.080 --> 00:27:33.120
this appallingly expensive product whose legal research relies

359
00:27:33.120 --> 00:27:37.200
on that web search — so is it really worth paying for? Well, neither of us is

360
00:27:37.200 --> 00:27:39.840
a lawyer, so of course we're of the opinion that it's not

361
00:27:39.840 --> 00:27:42.960
worth paying for, but — I do have friends

362
00:27:42.960 --> 00:27:46.480
though, who do just fine living off that lawyer's know-how, that corpus, and

363
00:27:46.480 --> 00:27:50.600
expertise, and their closed systems — and if you have, say,

364
00:27:50.600 --> 00:27:54.480
a top Finnish law firm, then their own internal

365
00:27:54.480 --> 00:27:59.360
system, with thousands of past cases in it, or you could

366
00:27:59.360 --> 00:28:03.519
use training material — you can still find that unique

367
00:28:03.519 --> 00:28:07.399
expertise there, the case-specific kind. I'd say — even though neither of us is

368
00:28:07.399 --> 00:28:12.679
a lawyer — Finland has weak courts

369
00:28:12.679 --> 00:28:16.760
and a strong Chamber of Commerce arbitration system.

370
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And this arbitration system is partly a bit like the American

371
00:28:21.080 --> 00:28:25.320
case-law type of thing, where, say, one or three

372
00:28:25.320 --> 00:28:30.679
arbitrators make a legally binding decision that

373
00:28:30.679 --> 00:28:34.120
binds the parties just like a judge's ruling would, if they've

374
00:28:34.120 --> 00:28:38.240
agreed to use the Chamber of Commerce's dispute resolution method for it. And they're

375
00:28:38.240 --> 00:28:42.760
not public, as far as I understand, so that body of information partly lives

376
00:28:42.760 --> 00:28:47.960
in law firms' own databases, because they're parties to these cases. Their

377
00:28:47.960 --> 00:28:52.640
partners might even be the arbitrators themselves, and it's

378
00:28:52.640 --> 00:28:57.320
kind of kept out of that whole open-data world of yours. I don't know

379
00:28:57.320 --> 00:29:01.200
if that rings a bell, that world — this kind of Helsinki Chamber of

380
00:29:01.200 --> 00:29:06.360
Commerce-type exception. Well, yeah. I mean,

381
00:29:06.360 --> 00:29:10.600
the challenge here, if we think about these law firms, what's

382
00:29:10.600 --> 00:29:14.360
difficult about it is that now

383
00:29:14.360 --> 00:29:19.880
a couple of years ago I was wondering which direction this was going,

384
00:29:19.880 --> 00:29:24.799
these general-purpose AI tools. Would they become

385
00:29:24.799 --> 00:29:29.159
so easy to use that anyone could use them,

386
00:29:29.159 --> 00:29:32.559
or would it get more complex? And I think this

387
00:29:32.559 --> 00:29:36.840
has clearly gone toward getting more complex. So now

388
00:29:36.840 --> 00:29:40.200
law firms traditionally maybe don't have much

389
00:29:40.200 --> 00:29:45.320
IT expertise to begin with, let alone this kind of AI

390
00:29:45.320 --> 00:29:51.039
IT expertise — which means that actually getting the benefits out of it, whether it's

391
00:29:51.039 --> 00:29:56.240
something like Legora or Harvey. Let alone something like

392
00:29:56.240 --> 00:30:00.919
Claude — to actually get the power out of Claude, it's

393
00:30:00.919 --> 00:30:05.279
exactly like you said, the key role there is played by your

394
00:30:05.279 --> 00:30:10.519
company's data, built up over the years. And now you need to get that into Claude's

395
00:30:10.519 --> 00:30:16.120
hands in a sensible way. You need genuinely good

396
00:30:16.120 --> 00:30:20.799
skills — which, as we touched on a bit earlier in this conversation —

397
00:30:20.799 --> 00:30:25.600
came up. So, skills means

398
00:30:25.600 --> 00:30:31.279
you can teach these language models different kinds of

399
00:30:31.279 --> 00:30:35.120
tasks. You could think of it like this: take doing a somersault, say —

400
00:30:35.120 --> 00:30:38.880
and say we have some young child,

401
00:30:38.880 --> 00:30:41.399
who doesn't know how to do a somersault, then

402
00:30:41.399 --> 00:30:44.840
the somersault involves certain things you have to

403
00:30:44.840 --> 00:30:48.559
do — so once you've taught that child the somersault

404
00:30:48.559 --> 00:30:53.360
skill once, then it can, sort of, whenever it needs

405
00:30:53.360 --> 00:30:57.440
to do a somersault, just pull out that somersault skill. And this

406
00:30:57.440 --> 00:31:01.480
is kind of the same thing — now you can teach these language models

407
00:31:01.480 --> 00:31:05.600
different skills. It could be, say, NDA

408
00:31:05.600 --> 00:31:07.080
commenting. Mm.

409
00:31:07.080 --> 00:31:11.200
Right, but you'd need to build it — and this was actually a really

410
00:31:11.200 --> 00:31:16.760
great opening move in that direction. This Claude — sort of the American Legal

411
00:31:16.760 --> 00:31:22.200
Skills, adapted for Finland. This Claude legal skills

412
00:31:22.200 --> 00:31:27.120
pack — Hakunikkola's GitHub has it, whether

413
00:31:27.120 --> 00:31:31.240
the skills — the skills pack — is something you can install with little effort

414
00:31:31.240 --> 00:31:35.591
into Claude, or as far as I know, into other systems too.

415
00:31:35.591 --> 00:31:37.880
[chuckle] Yeah. But maybe on skills, by the way —

416
00:31:37.880 --> 00:31:41.600
my own observation — for a long time I did them like this, I'd start

417
00:31:41.600 --> 00:31:45.120
building sort of alongside, like: here's a task starting, watch me while I do

418
00:31:45.120 --> 00:31:49.240
this task, and oh, stuff like that came up, and then we jumped around, and I

419
00:31:49.240 --> 00:31:51.799
realized this was done completely backwards — that you should first do the thing

420
00:31:51.799 --> 00:31:54.919
all the way to the finish line, and only then say let's turn this into a skill — once I've slogged through

421
00:31:54.919 --> 00:31:57.799
the whole obstacle course to the end, that's actually a better way to make

422
00:31:57.799 --> 00:32:01.840
skills, at least at this point in time. Absolutely agree. Yeah,

423
00:32:01.840 --> 00:32:04.679
yeah, but nobody really teaches that either — I mean, in that sense

424
00:32:04.679 --> 00:32:08.960
or, I don't know, there just isn't anymore that kind of

425
00:32:08.960 --> 00:32:13.360
Stack Overflow that everyone could go check to see how this stuff

426
00:32:13.360 --> 00:32:16.320
gets done — everyone's kind of learning it at random, I think, but

427
00:32:16.320 --> 00:32:19.360
it's kind of fun in a way. It's insanely fun, [laughter]

428
00:32:19.360 --> 00:32:23.240
but the unfortunate part is it's a pretty small group

429
00:32:23.240 --> 00:32:26.840
who are excited about learning

430
00:32:26.840 --> 00:32:30.799
this kind of stuff. Yeah. Maybe I have to give Legora credit here too

431
00:32:30.799 --> 00:32:34.799
since, well, those Legora guys built that too.

432
00:32:34.799 --> 00:32:39.000
So at first I installed it, of course, thinking, let's just switch it on and,

433
00:32:39.000 --> 00:32:45.480
since I'm the DIY type, run it on this Gemma 4 26B model on my own

434
00:32:45.480 --> 00:32:50.720
hardware — and it was total garbage. Then I put Qwen 3.7, the seven,

435
00:32:50.720 --> 00:32:53.880
running there, which I already run, and that was a lot better, but then there was this

436
00:32:53.880 --> 00:32:57.240
problem where I didn't get their philosophy — like their

437
00:32:57.240 --> 00:33:01.399
Harvey-style model, where the idea is you have two different

438
00:33:01.399 --> 00:33:07.440
language models, which are, say, different — Opus and then Sonnet,

439
00:33:07.440 --> 00:33:12.240
or, well, the best Sonnet, which right now is five, and Opus 4.8,

440
00:33:12.240 --> 00:33:16.679
eight, since Fable got taken away from us — the way they, sort of,

441
00:33:16.679 --> 00:33:22.039
argue with each other, that sort of produces intelligence that doesn't happen

442
00:33:22.039 --> 00:33:25.720
with a single local model. If it's the same model going up against another copy of itself, then there's no

443
00:33:25.720 --> 00:33:31.399
real cross-battle happening, it's just — at first

444
00:33:31.399 --> 00:33:34.600
I thought this was total garbage when I ran it with that local model. It wasn't

445
00:33:34.600 --> 00:33:38.159
getting anything done. But then when I ran it with, like, the best Claude

446
00:33:38.159 --> 00:33:41.840
models, it actually produced a pretty okay base. But then when I ran it

447
00:33:41.840 --> 00:33:45.679
through your Laki.ai and then through these skills, it turned out

448
00:33:45.679 --> 00:33:50.399
even better. So I thought this was a pretty solid workflow. So like

449
00:33:50.399 --> 00:33:53.519
you make some kind of base draft that doesn't need to be all that polished, that can be

450
00:33:53.519 --> 00:33:57.159
just your own best rough sketch, and then you refine it — I think that's

451
00:33:57.159 --> 00:33:59.519
where pretty nice stuff comes out in the end.

452
00:33:59.519 --> 00:34:04.240
Yeah, absolutely. [laughter] Yeah. And maybe let's open this up a bit more

453
00:34:04.240 --> 00:34:08.879
for the listeners too — you just said Legora, but you actually meant

454
00:34:08.879 --> 00:34:13.480
Lovable for sure. Yeah, yeah, yeah, got them mixed up. So Lovable is just this

455
00:34:13.480 --> 00:34:17.200
kind of free program too, that you can install. It eats up some

456
00:34:17.200 --> 00:34:21.320
tokens once you install it. Though this is — yeah, Legora and Harvey, I've

457
00:34:21.320 --> 00:34:24.440
actually never used either, I have to say those are total black

458
00:34:24.440 --> 00:34:27.599
boxes to me. I just watch and see that, once again, the Swedes and the Yanks know how to cash in

459
00:34:27.599 --> 00:34:30.800
on that too. Yeah, I haven't used Harvey myself

460
00:34:30.800 --> 00:34:34.520
either, though I have watched YouTube videos

461
00:34:34.520 --> 00:34:38.879
of it being used. Legora I have used, and

462
00:34:38.879 --> 00:34:42.599
it hasn't convinced yours truly.

463
00:34:42.599 --> 00:34:46.919
Yeah. Lovable is actually kind of fun from a Swedish-coding-scene angle, because they sort of solve

464
00:34:46.919 --> 00:34:52.359
that whole minimum viable product thing within a safe authenticated setup,

465
00:34:52.359 --> 00:34:56.599
pretty neatly and with little effort, instead of you having to

466
00:34:56.599 --> 00:35:00.520
go and set up your own servers, or cloud

467
00:35:00.520 --> 00:35:04.640
installations. I still like that — I still occasionally do some

468
00:35:04.640 --> 00:35:08.520
little things with it myself, yeah, that's also interesting to watch —

469
00:35:08.520 --> 00:35:15.280
I think Lovable actually fits pretty well onto that sort of Pieter Levels path,

470
00:35:15.280 --> 00:35:20.240
whether it turns out that, as we go a bit further, I —

471
00:35:20.240 --> 00:35:26.280
I'll sign off on that. As a user, Lovable is still pretty handy, because

472
00:35:26.280 --> 00:35:28.880
it sort of abstracts away certain

473
00:35:28.880 --> 00:35:33.720
pretty laborious steps, like this whole cloud infrastructure

474
00:35:33.720 --> 00:35:36.800
provisioning and all this configuring,

475
00:35:36.800 --> 00:35:42.839
but how long is it before you're just sitting in Claude's or ChatGPT's

476
00:35:42.839 --> 00:35:49.734
interface, saying, hey, just whip me up something like this.

477
00:35:49.734 --> 00:35:52.720
[laughter] Yeah, that's how it is. Yeah, mainly for me it's

478
00:35:52.720 --> 00:35:56.520
that I at least know what the tech stack is, sort of. They've

479
00:35:56.520 --> 00:36:01.319
gone with a super simple one — you've got React and Node in there, and then

480
00:36:01.319 --> 00:36:04.319
TypeScript too, so I know that those are good

481
00:36:04.319 --> 00:36:09.040
building blocks — and Supabase and so on — and they work, and they don't really

482
00:36:09.040 --> 00:36:13.000
leak, as a rule. Whereas if I go and build it myself, then

483
00:36:13.000 --> 00:36:16.400
you can end up with whatever happens to come out — like, let's just build this on Firebase and

484
00:36:16.400 --> 00:36:20.240
do some of my own messing around, and oh, this doesn't even need this kind of authentication and

485
00:36:20.240 --> 00:36:24.760
that kind of API — and then, I can't be bothered, I'll just grab it off the shelf

486
00:36:24.760 --> 00:36:28.599
instead. [chuckle] So, so it's sort of like that

487
00:36:28.599 --> 00:36:32.560
noob-level-plus thing, that's what it is. And there's still stuff like demo sites or

488
00:36:32.560 --> 00:36:36.040
some small group things you need to log into, so it's not just totally

489
00:36:36.040 --> 00:36:39.560
wide open — and that's incredibly handy. And then it still

490
00:36:39.560 --> 00:36:44.480
keeps improving a bit, and they clearly run all those language models a bit

491
00:36:44.480 --> 00:36:47.359
crosswise under the hood, so every now and then you get a proper

492
00:36:47.359 --> 00:36:51.119
bonus dose of intelligence, when they happen to run it on some better model instead of dropping down

493
00:36:51.119 --> 00:36:55.920
to some cheap knock-off model. But what you said about Cursor a moment ago,

494
00:36:55.920 --> 00:36:59.599
now the great and mighty Elon Musk took it into his empire, so

495
00:36:59.599 --> 00:37:04.839
as we're recording this, they've released an X MCP, and then they

496
00:37:04.839 --> 00:37:08.760
run that into Cursor, so it's really interesting what my

497
00:37:08.760 --> 00:37:14.119
friend Musk pulls off there with that combination of Cursor and X and

498
00:37:14.119 --> 00:37:17.839
Twitter. Yeah, absolutely. I do have to pick up a bit more on

499
00:37:17.839 --> 00:37:22.839
this Lovable thing, because right now

500
00:37:22.839 --> 00:37:28.599
Laki.ai's development has definitely been

501
00:37:28.599 --> 00:37:34.079
mainly done by Juuso from Vesanto, who, uh,

502
00:37:34.079 --> 00:37:40.319
a few years ago — before Lovable even existed — showed me

503
00:37:40.319 --> 00:37:45.520
this kind of business case, where he described what Lovable

504
00:37:45.520 --> 00:37:50.000
has done in practice. And whether we should maybe build that. [laughter]

505
00:37:50.000 --> 00:37:54.119
We ended up deciding together, as a group, well, maybe we won't build

506
00:37:54.119 --> 00:37:56.480
this. Yeah. There's Replit and others out there,

507
00:37:56.480 --> 00:37:59.440
sure, that's eating into that market. But I mean, even Cursor

508
00:37:59.440 --> 00:38:03.800
was bought for 60 billion dollars, which is 30

509
00:38:03.800 --> 00:38:08.640
times revenue — and when X was acquired, at first they didn't even have to pay

510
00:38:08.640 --> 00:38:12.720
with hard cash, it was just a 3% dilution into SpaceX, which is just

511
00:38:12.720 --> 00:38:17.000
an insane amount of paper money right there. So surely something

512
00:38:17.000 --> 00:38:20.520
like that will turn up for Lovable too, out of this, as long as

513
00:38:20.520 --> 00:38:25.280
it doesn't take too long to pay off. Yeah, right. [laughter] But yeah, but that's

514
00:38:25.280 --> 00:38:28.640
exactly the thing — if you think about what Lovable has done

515
00:38:28.640 --> 00:38:32.119
smartly, it's specifically those smart default choices. So they have

516
00:38:32.119 --> 00:38:36.359
good default settings. Whereas — I don't know, I haven't used Replit

517
00:38:36.359 --> 00:38:39.560
myself in a long time. I don't know what its status is these days, but

518
00:38:39.560 --> 00:38:43.480
at least earlier on, it used to leave

519
00:38:43.480 --> 00:38:46.760
the user with quite a lot of decision-making

520
00:38:46.760 --> 00:38:49.880
power there, which was maybe exactly the kind of thing users didn't

521
00:38:49.880 --> 00:38:55.000
want to do — they didn't want to think about what kind of

522
00:38:55.000 --> 00:39:00.079
environment this is being run in, they just want good defaults and then

523
00:39:00.079 --> 00:39:04.119
exactly that, and they've done that really well. Yeah. And so far it hasn't

524
00:39:04.119 --> 00:39:08.920
leaked badly. So just that — the authentication stuff and the Supabase instances — and then

525
00:39:08.920 --> 00:39:11.880
they've held up to scaling pretty well, they haven't

526
00:39:11.880 --> 00:39:15.200
blown up so badly that you'd have had to pay

527
00:39:15.200 --> 00:39:19.599
a ton of money for it when suddenly 10 million

528
00:39:19.599 --> 00:39:23.040
users are hitting some database — somehow they've

529
00:39:23.040 --> 00:39:26.520
managed to handle that too. And then I think they've also, in a way,

530
00:39:26.520 --> 00:39:32.240
in that harness layer — that dumb chat text box — they don't

531
00:39:32.240 --> 00:39:35.720
tell you what model they're running there, but they clearly always make

532
00:39:35.720 --> 00:39:40.400
that kind of trade-off between cost savings and then, on the other hand,

533
00:39:40.400 --> 00:39:44.119
whether you get quality out of it — and I think they've handled that too

534
00:39:44.119 --> 00:39:47.160
pretty well. As a rule, when I put in

535
00:39:47.160 --> 00:39:50.000
a smart prompt, what usually comes out is pretty sensible

536
00:39:50.000 --> 00:39:54.560
stuff, not garbage. Whereas if you put, say, Sonnet

537
00:39:54.560 --> 00:39:57.760
in there as the Claude model, you might actually get some

538
00:39:57.760 --> 00:40:01.520
surprisingly bad output — like [chuckle] — if it's just a bad token day and it

539
00:40:01.520 --> 00:40:07.839
doesn't feel like running on that 4.8 Pro, or whatever it's called, that

540
00:40:07.839 --> 00:40:12.520
ultra-ultra-effort setting. Yeah [laughter] yeah. On that note, actually,

541
00:40:12.520 --> 00:40:17.640
it's an interesting day today — yesterday evening

542
00:40:17.640 --> 00:40:22.800
actually, when I was reading the news, Anthropic announced that Fable is coming

543
00:40:22.800 --> 00:40:24.800
back today,

544
00:40:24.800 --> 00:40:28.720
just like Sonnet 5 came too — yeah, Sonnet 5 came out yesterday, and Fable

545
00:40:28.720 --> 00:40:31.400
is coming back here. Ah, has Trump had a good day, then, so that

546
00:40:31.400 --> 00:40:34.680
apparently — ah, okay. Well then, full speed ahead,

547
00:40:34.680 --> 00:40:39.400
because I was still in that post-Arctic15

548
00:40:39.400 --> 00:40:44.359
hangover haze, so my last Fable run was mainly YouTube

549
00:40:44.359 --> 00:40:49.240
subtitling for this channel, so, honestly, that's about

550
00:40:49.240 --> 00:40:55.400
where things stand. Yeah, I have to say, luckily, when Fable

551
00:40:55.400 --> 00:41:01.440
was released — if I remember right it was available for three days — I hammered

552
00:41:01.440 --> 00:41:06.400
away at it pretty much morning to night, and I have to say it was truly

553
00:41:06.400 --> 00:41:12.560
a significant — based on my own testing and

554
00:41:12.560 --> 00:41:18.160
experience — a significant improvement over anything

555
00:41:18.160 --> 00:41:22.400
before it. Right, and you had that slash-make-me-rich-make-no-

556
00:41:22.400 --> 00:41:26.200
mistakes thing. It made money land in the account from a single prompt.

557
00:41:26.200 --> 00:41:29.480
Yeah, and there's — this ties into [laughter] — I think there's a

558
00:41:29.480 --> 00:41:35.119
kind of interesting story here, like what, or

559
00:41:35.119 --> 00:41:38.040
this kind of objection I actually run into pretty often

560
00:41:38.040 --> 00:41:42.119
from software developers. A lot of them say this model is already

561
00:41:42.119 --> 00:41:43.839
good enough for me now, so

562
00:41:43.839 --> 00:41:49.560
I don't need a better model, this is enough for me. To which my

563
00:41:49.560 --> 00:41:54.560
question is, well, are you at the point where you just give it instructions in the

564
00:41:54.560 --> 00:41:55.599
morning, and then

565
00:41:55.599 --> 00:41:59.800
check the next day's

566
00:41:59.800 --> 00:42:04.480
standup to see what's been done — and then they're a bit confused, like, well no,

567
00:42:04.480 --> 00:42:07.480
of course not, I write functions with it.

568
00:42:07.480 --> 00:42:10.920
Yeah, yeah. Well, I think sometimes you just hit enter too [laughter]

569
00:42:10.920 --> 00:42:13.400
as in, like, 'dangerously appro—

570
00:42:13.400 --> 00:42:18.240
ve.' The thing is, I personally see an insane amount of value specifically in

571
00:42:18.240 --> 00:42:22.559
the model's intelligence improving, because it enables — and now the point is,

572
00:42:22.559 --> 00:42:25.400
how you should think about it, I think, is just that

573
00:42:25.400 --> 00:42:31.559
we're still not at the point where I could treat

574
00:42:31.559 --> 00:42:35.319
a model like a good software developer. And that's exactly

575
00:42:35.319 --> 00:42:38.480
what I want. Yeah, I want to tell it, like,

576
00:42:38.480 --> 00:42:44.079
in a weekly meeting, hey, here's this week's tasks, and then we've got — whether it's

577
00:42:44.079 --> 00:42:47.640
a daily, you know, a 10 or 15 minute check-in where we

578
00:42:47.640 --> 00:42:49.319
look at how it's gone.

579
00:42:49.319 --> 00:42:53.640
Mm. So then the thing is, that these

580
00:42:53.640 --> 00:42:57.079
current ones are good enough — well, no, definitely not, because I want

581
00:42:57.079 --> 00:43:01.040
Fable back quickly, and luckily it's coming today, and then we've also got

582
00:43:01.040 --> 00:43:06.319
GPT-5.6 coming, which is interesting too — some people already have it

583
00:43:06.319 --> 00:43:11.079
on the approved list there. Exactly. Yeah, some already have it,

584
00:43:11.079 --> 00:43:14.119
but hopefully it's coming for us too, yeah,

585
00:43:14.119 --> 00:43:18.119
us regular mortals, probably within a week or two from around now.

586
00:43:18.119 --> 00:43:22.400
So we'll get to that shortly, but I have to say, when I started

587
00:43:22.400 --> 00:43:27.200
down this rabbit hole back in January, I of course picked up Claude Code

588
00:43:27.200 --> 00:43:32.040
and Cowork, and even switched to Mac because of it, since it didn't run on PC, so

589
00:43:32.040 --> 00:43:35.800
all that — I liked Claude's philosophy compared to that. Codex, well,

590
00:43:35.800 --> 00:43:39.480
already one-shotted things back then, but I liked it when I sort of

591
00:43:39.480 --> 00:43:43.599
learned that you have to actually watch Claude a bit as it fumbles around and

592
00:43:43.599 --> 00:43:47.359
keeps looking things over and making choices, then hitting enter, and sometimes going into

593
00:43:47.359 --> 00:43:51.160
the shell [chuckle] and having a look at what

594
00:43:51.160 --> 00:43:55.599
you need to press yourself, and oh no, that's not working — you take screenshots or

595
00:43:55.599 --> 00:43:58.480
paste this text back in, there was an error and so on. And that's kind of, for me,

596
00:43:58.480 --> 00:44:03.119
still a basic principle. And then, back when Claude

597
00:44:03.119 --> 00:44:06.839
ages ago switched to this token-based pricing, I decided to

598
00:44:06.839 --> 00:44:10.480
just cut OpenAI out of the picture, because it started

599
00:44:10.480 --> 00:44:13.960
costing real money — the first day alone was like 90 euros, so I thought,

600
00:44:13.960 --> 00:44:18.640
just drop it completely, and go with OpenAI's monthly plan for Codex as my

601
00:44:18.640 --> 00:44:22.605
daily driver. But at the same time, as I started using Codex,

602
00:44:22.605 --> 00:44:25.760
[clears throat] it really is kind of scarily good at

603
00:44:25.760 --> 00:44:28.839
one-shotting things — you give it the task, hit enter, and

604
00:44:28.839 --> 00:44:33.000
off it goes, and pretty solid stuff comes out at the end, so I'm not

605
00:44:33.000 --> 00:44:37.800
totally sure I like that. I think I'd still rather be the artist

606
00:44:37.800 --> 00:44:42.559
who steers the ship every now and then. I don't know if you have the same

607
00:44:42.559 --> 00:44:46.240
experiences. Well, yeah, I do. [laughter] I mean, as personalities,

608
00:44:46.240 --> 00:44:51.960
Claude — or if you think about the Claude models

609
00:44:51.960 --> 00:44:56.480
versus these GPT models, they're really different. And I do

610
00:44:56.480 --> 00:45:00.520
share that experience, that working with Claude is much

611
00:45:00.520 --> 00:45:02.119
more pleasant, so

612
00:45:02.119 --> 00:45:07.960
it's just a lot more human and

613
00:45:07.960 --> 00:45:14.240
more considerate, and it actually explains things — and that's maybe hard to explain.

614
00:45:14.240 --> 00:45:17.520
But there's actually a pretty big difference in how it feels to work with, whereas

615
00:45:17.520 --> 00:45:21.000
then, the GPT models are a bit blunt, or kind of

616
00:45:21.000 --> 00:45:24.680
arrogant — it's like you've got some kind of [laughter]

617
00:45:24.680 --> 00:45:29.520
really senior type, like some professor who's

618
00:45:29.520 --> 00:45:32.960
a bit high-strung, like, you're not really — you're a bit

619
00:45:32.960 --> 00:45:36.319
on edge when you go to tell it something, like hey, could you

620
00:45:36.319 --> 00:45:40.359
do this now? Yeah. This actually, by the way, Claude —

621
00:45:40.359 --> 00:45:47.760
when it went from Opus 4.6 to 4.7, it was a shock to me when this HR

622
00:45:47.760 --> 00:45:53.559
lady showed up, and then this security guy showed up too — or the other way round, HR and security

623
00:45:53.559 --> 00:45:57.960
lady — it was like, well Sami, it's already 8 o'clock, haven't

624
00:45:57.960 --> 00:46:01.680
we already done quite a lot of work here, maybe continue tomorrow — and I was like,

625
00:46:01.680 --> 00:46:05.960
what the hell, it's none of your business how long and how hard I

626
00:46:05.960 --> 00:46:09.079
work here. And then the other thing, this security business, where

627
00:46:09.079 --> 00:46:12.520
it flashed up something about an API key, like you basically need to delete your hard drive and

628
00:46:12.520 --> 00:46:15.920
go get a new key again, like this isn't going to fly, this is just

629
00:46:15.920 --> 00:46:19.079
a terrible security risk — and honestly, with both of these, I was like, I don't

630
00:46:19.079 --> 00:46:22.480
want any of this at all, and that was honestly part of the reason I jumped

631
00:46:22.480 --> 00:46:25.119
over to the GPT world, because they don't really have this stuff there, so —

632
00:46:25.119 --> 00:46:28.400
Mm. Yeah. [laughter] But that — there's actually been a really

633
00:46:28.400 --> 00:46:32.119
interesting blog post from OpenAI just a few days ago,

634
00:46:32.119 --> 00:46:37.760
they published. They'd looked internally into how many tokens

635
00:46:37.760 --> 00:46:42.559
from all their employees' AI usage

636
00:46:42.559 --> 00:46:48.160
come from Codex versus ChatGPT. Yeah. And there had been a really

637
00:46:48.160 --> 00:46:54.079
dramatic shift there — it was something like over 90% of all the tokens,

638
00:46:54.079 --> 00:46:58.920
basically all the output that these AIs produce inside the OpenAI organization,

639
00:46:58.920 --> 00:47:03.040
comes from Codex, even outside of software development. And

640
00:47:03.040 --> 00:47:07.599
this is something that I don't think has really

641
00:47:07.599 --> 00:47:11.200
in Finnish organizations, OpenAI - look, they go through Codex

642
00:47:11.200 --> 00:47:12.960
so no one's really caught on to that yet. [laughter] But the thing is,

643
00:47:12.960 --> 00:47:16.720
I think that's how it is - or actually, I do know that's how it is,

644
00:47:16.720 --> 00:47:20.960
that people just use the chat.

645
00:47:20.960 --> 00:47:23.520
Mm. Like if we look at it this way,

646
00:47:23.520 --> 00:47:29.160
inside OpenAI everyone uses Codex. Nobody really uses the chat -

647
00:47:29.160 --> 00:47:33.559
chat usage is minimal. Whereas in Finnish companies,

648
00:47:33.559 --> 00:47:37.760
and maybe in general, aside from these kinds of

649
00:47:37.760 --> 00:47:41.520
tech startups or whoever, who are actually

650
00:47:41.520 --> 00:47:47.000
in the AI world, AI adoption is completely

651
00:47:47.000 --> 00:47:48.640
still in its infancy.

652
00:47:48.640 --> 00:47:52.640
so what's needed now is a shift away from doing

653
00:47:52.640 --> 00:47:58.000
just little one-off things in chat. Yeah. Toward actually being

654
00:47:58.000 --> 00:48:02.640
well, if you're in the OpenAI ecosystem, having the work actually happen

655
00:48:02.640 --> 00:48:05.319
in Codex itself. Yeah, that's a good point. And actually

656
00:48:05.319 --> 00:48:10.319
now we get to that harness thing, or the OpenAI setup, or actual AI

657
00:48:10.319 --> 00:48:16.480
workers - not just these kind of dumb, in-between,

658
00:48:16.480 --> 00:48:20.240
whatever agents. So maybe two themes now - if these

659
00:48:20.240 --> 00:48:24.359
are the current front-runners - let's leave Google aside for now, even though they're

660
00:48:24.359 --> 00:48:28.160
trying to get into this game with Antigravity. So you've got

661
00:48:28.160 --> 00:48:33.040
Claude - Anthropic started that game already back in early this year. So we got that

662
00:48:33.040 --> 00:48:36.640
Claude Cowork, and along with it came the desktop app, so you've got the

663
00:48:36.640 --> 00:48:42.079
chat, then you've got Claude Cowork, which gets you into those office folders

664
00:48:42.079 --> 00:48:46.119
to work in, and then there's Claude Code. And at first for me it was like, I

665
00:48:46.119 --> 00:48:49.920
went from the chat into Code, and then I went into Cowork. Now I don't

666
00:48:49.920 --> 00:48:53.839
really know where I even want to be. It's almost the same whether I'm

667
00:48:53.839 --> 00:48:57.160
in chat. Actually I pretty rarely go into Code, because if I'm running

668
00:48:57.160 --> 00:49:00.359
code, then I go to the command line interface, which is the

669
00:49:00.359 --> 00:49:05.000
text-based thing. And then OpenAI's side was kind of whatever, before

670
00:49:05.000 --> 00:49:08.960
Codex came along - there was just the chat and the command line, but

671
00:49:08.960 --> 00:49:11.319
I never really used that. But now it's kind of

672
00:49:11.319 --> 00:49:15.240
like this: I actually run quite a lot of coding straight from chat,

673
00:49:15.240 --> 00:49:18.359
but then I go into Codex if I want to one-shot something. But then I

674
00:49:18.359 --> 00:49:22.559
run things with my own Samantha on OpenClow. So OpenClow is this kind of open-

675
00:49:22.559 --> 00:49:26.880
source project that was built by this guy, Peter Steinberger,

676
00:49:26.880 --> 00:49:30.319
an Austrian genius. So I picked it up like a week after it was

677
00:49:30.319 --> 00:49:34.319
released, and it runs on my Mac - this Samantha, which runs on its own

678
00:49:34.319 --> 00:49:38.040
physical machine, but it's clearly just hammering away on Codex the whole time

679
00:49:38.040 --> 00:49:44.440
because it codes and uses that as its main brain. So, um, I still

680
00:49:44.440 --> 00:49:49.559
think of it as the harness - you've got this smart agent, OpenClow,

681
00:49:49.559 --> 00:49:53.200
that's still a really big deal for me. But are you following

682
00:49:53.200 --> 00:49:56.520
this idea of the desktop, these three pipelines, and then this kind of

683
00:49:56.520 --> 00:50:02.280
agentic building block - how do you actually see access

684
00:50:02.280 --> 00:50:09.440
to that intelligence. Well, especially if we go into this

685
00:50:09.440 --> 00:50:14.079
context of an office worker or knowledge worker,

686
00:50:14.079 --> 00:50:16.640
who works at some corporation,

687
00:50:16.640 --> 00:50:21.839
then I think the shift there - the first thing that needs

688
00:50:21.839 --> 00:50:26.680
to happen - is getting rid of Copilot.

689
00:50:26.680 --> 00:50:30.119
Yeah, it's terrible. And it's funny because

690
00:50:30.119 --> 00:50:34.079
I've done a fair number of these trainings over in Saudi

691
00:50:34.079 --> 00:50:38.799
Arabia, and I don't run into this problem there. It being a religious country,

692
00:50:38.799 --> 00:50:42.920
yeah, over there it's either

693
00:50:42.920 --> 00:50:46.640
companies operating in the OpenAI or Anthropic ecosystem,

694
00:50:46.640 --> 00:50:49.680
but I've never once run into this thing I keep running into in Finland,

695
00:50:49.680 --> 00:50:54.640
constantly. And the worst part of all of this is

696
00:50:54.640 --> 00:50:59.000
that we have companies that say, oh yeah, we're right there

697
00:50:59.000 --> 00:51:03.760
on the AI front line, and then it turns out, yeah, we've got Copilot

698
00:51:03.760 --> 00:51:06.880
here. [laughter] And Copilot

699
00:51:06.880 --> 00:51:10.680
has nothing to do with actually leveraging AI

700
00:51:10.680 --> 00:51:13.319
in any real sense. So that'd be my first piece of advice - [clears throat]

701
00:51:13.319 --> 00:51:17.599
you have to get rid of Copilot. And then

702
00:51:17.599 --> 00:51:20.720
as for what good alternatives there are right now, I think the best

703
00:51:20.720 --> 00:51:23.760
options are, like you said earlier, that Google has kind of dropped

704
00:51:23.760 --> 00:51:30.079
out of the game, so you've got either the OpenAI ecosystem or Anthropic.

705
00:51:30.079 --> 00:51:34.920
So you pick one of the two, and once you've picked one, you should try to

706
00:51:34.920 --> 00:51:39.240
make sure the work actually happens either in Codex

707
00:51:39.240 --> 00:51:42.319
or in Claude's Cowork - not in the

708
00:51:42.319 --> 00:51:46.359
chat, because this is exactly the point from that OpenAI blog post -

709
00:51:46.359 --> 00:51:47.400
yeah,

710
00:51:47.400 --> 00:51:51.960
that the chat is kind of like, if you have a question

711
00:51:51.960 --> 00:51:56.079
you want an answer to, or some small thing like that, then

712
00:51:56.079 --> 00:51:59.599
chat's fine. But in other cases, if you're actually going to do

713
00:51:59.599 --> 00:52:01.079
knowledge work, mm,

714
00:52:01.079 --> 00:52:04.200
and you're not a software developer - for developers, of course,

715
00:52:04.200 --> 00:52:08.520
it's Claude Code or Codex, but now if you're

716
00:52:08.520 --> 00:52:11.920
say, someone doing marketing or sales,

717
00:52:11.920 --> 00:52:17.240
that kind of person, you'd notice, okay, my

718
00:52:17.240 --> 00:52:22.480
usage - like maybe 90% of it - if you're in the OpenAI ecosystem,

719
00:52:22.480 --> 00:52:26.319
then you look at your monthly usage and you realize, okay,

720
00:52:26.319 --> 00:52:29.119
90% of my usage is in Codex. Mm.

721
00:52:29.119 --> 00:52:32.079
Then you can say, hey, that's going pretty well.

722
00:52:32.079 --> 00:52:36.040
Mm. So that's maybe as an intro, that, well,

723
00:52:36.040 --> 00:52:44.160
then, um, these - I wouldn't necessarily dare recommend to companies

724
00:52:44.160 --> 00:52:49.520
these OpenAI solutions yet, at this stage - although now

725
00:52:49.520 --> 00:52:53.000
Microsoft - I haven't followed this super closely, but Microsoft

726
00:52:53.000 --> 00:52:57.160
is now, on some level, offering companies specifically this OpenClow

727
00:52:57.160 --> 00:53:00.839
Yeah, exactly, and actually Peter Steinberger also released

728
00:53:00.839 --> 00:53:05.319
it for mobile too. So yeah, it's coming - look, since Steinberger is at OpenAI now,

729
00:53:05.319 --> 00:53:08.839
he's not just some renegade kid anymore, cranking out a bit of

730
00:53:08.839 --> 00:53:14.640
sloppy code on the side - no, he's properly, like, right there inside

731
00:53:14.640 --> 00:53:19.400
the OpenAI empire. But maybe, in a way, the desktop thing is

732
00:53:19.400 --> 00:53:24.319
kind of interesting to me, because it might be that browsers

733
00:53:24.319 --> 00:53:30.200
basically die out entirely, that it's Anthropic's and OpenAI's and Google's

734
00:53:30.200 --> 00:53:34.520
own apps, once they get things sorted out - those become the portals you go in through

735
00:53:34.520 --> 00:53:37.119
and yeah, and that's actually a good

736
00:53:37.119 --> 00:53:42.480
point to raise - when I talk here about Codex and Claude Cowork,

737
00:53:42.480 --> 00:53:47.960
these, um - you can actually use Codex through

738
00:53:47.960 --> 00:53:54.400
the browser too. What I'm specifically talking about here are these

739
00:53:54.400 --> 00:53:57.160
desktop applications,

740
00:53:57.160 --> 00:54:02.559
meaning the desktop apps you download onto your machine and run

741
00:54:02.559 --> 00:54:06.119
things in. And that's exactly it - it's really interesting

742
00:54:06.119 --> 00:54:12.000
to think about why you'd actually want to go yourself

743
00:54:12.000 --> 00:54:16.400
to some website - like, does that need go away?

744
00:54:16.400 --> 00:54:20.760
It probably won't disappear entirely, but it'll shrink really, really significantly.

745
00:54:20.760 --> 00:54:25.119
What's interesting here too is that at least I have a really

746
00:54:25.119 --> 00:54:30.119
hard time finding tasks anymore

747
00:54:30.119 --> 00:54:33.520
that I'd actually go do myself. Like,

748
00:54:33.520 --> 00:54:37.657
what would be a thing where I'd actually be better than Codex?

749
00:54:37.657 --> 00:54:39.677
[laughter]

750
00:54:39.839 --> 00:54:44.862
taste, and long-term goals - 'make me rich' or something like that,

751
00:54:44.862 --> 00:54:48.280
[laughter] you need some kind of long-term vision, so that essentially the

752
00:54:48.280 --> 00:54:53.280
task-by-task work versus, say, what happens a year from now - the gap between those is

753
00:54:53.280 --> 00:54:56.359
still significant, definitely. Yeah, and yes, exactly,

754
00:54:56.359 --> 00:54:59.400
so even if you get there a year from now one task at a time, and

755
00:54:59.400 --> 00:55:03.000
even if every single task were done entirely by AI, you still need to first

756
00:55:03.000 --> 00:55:05.240
have some kind of direction. Yes. Yes, absolutely. Yeah. Well,

757
00:55:05.240 --> 00:55:09.079
that's the value a human creates, but the thing is, that you

758
00:55:09.079 --> 00:55:12.680
yourself doing something routine - like, this

759
00:55:12.680 --> 00:55:17.720
task-doing is maybe exactly the thing where nobody should really be doing any

760
00:55:17.720 --> 00:55:22.319
tasks themselves anymore - they should really be left to AI. And I mean

761
00:55:22.319 --> 00:55:25.680
right now we're doing a huge amount of all kinds of tasks

762
00:55:25.680 --> 00:55:29.240
which is honestly kind of crazy. Yeah, I think, if you

763
00:55:29.240 --> 00:55:35.000
think about what Musk did back with Twitter, or X - he

764
00:55:35.000 --> 00:55:37.799
threw out like 70 percent of the staff

765
00:55:37.799 --> 00:55:41.039
back in a time when we didn't even have these tools yet,

766
00:55:41.039 --> 00:55:43.880
so I think it's genuinely hard

767
00:55:43.880 --> 00:55:48.400
to find organizations where you couldn't throw out something like 70 percent

768
00:55:48.400 --> 00:55:54.119
of the staff and still have things carry on pretty much as before.

769
00:55:54.119 --> 00:55:58.440
Yeah, that's brutal. And the other hope people voice is that everyone

770
00:55:58.440 --> 00:56:01.640
would adopt these tools, and then everyone could basically keep

771
00:56:01.640 --> 00:56:04.319
their jobs and just scale things up tenfold instead. But

772
00:56:04.319 --> 00:56:08.520
the more likely outcome is exactly that - scale does grow among the ones

773
00:56:08.520 --> 00:56:12.799
who remain and who push hard, but then, in a way,

774
00:56:12.799 --> 00:56:17.440
people fall away too - and I think it's worth

775
00:56:17.440 --> 00:56:22.440
summing up like this: if your role at work is that some colleague

776
00:56:22.440 --> 00:56:27.960
or boss or client comes and says 'do this for me', hands you the task, and

777
00:56:27.960 --> 00:56:32.720
then you do it and hand it back a bit later if you remember, then

778
00:56:32.720 --> 00:56:35.640
that's not a great position to be in, because then you're basically

779
00:56:35.640 --> 00:56:39.500
pretty easily replaceable by AI inference. Yeah, that's

780
00:56:39.500 --> 00:56:42.920
[laughter] exactly right. Do you remember, by the way, there was a big fuss about that

781
00:56:42.920 --> 00:56:46.799
back when Mikko Alasaarela said on some podcast that he had

782
00:56:46.799 --> 00:56:51.680
calculated how many employees like that Kela would actually need, and it

783
00:56:51.680 --> 00:56:55.864
turned out to be some incomprehensibly small number. Like it was 18 or something.

784
00:56:55.864 --> 00:56:59.920
[laughter] Yeah, and 8,000 was the wrong answer,

785
00:56:59.920 --> 00:57:03.559
whatever the real number is, but yeah, they really should have

786
00:57:03.559 --> 00:57:07.520
ordered some 500-million Salesforce license, so right away, right away

787
00:57:07.520 --> 00:57:12.520
they went about the organization's task management the wrong way. But

788
00:57:12.520 --> 00:57:16.200
yeah, Mikko Alasaarela has been on the show many, many times, definitely.

789
00:57:16.200 --> 00:57:19.359
Although he's mentioned getting this AI brain fog - like, I haven't

790
00:57:19.359 --> 00:57:22.680
found it as fun yet as, in a way, this being a return to childhood,

791
00:57:22.680 --> 00:57:27.039
where I always get excited getting into it, having everything

792
00:57:27.039 --> 00:57:30.880
set up and available, and it's genuinely a joy prompting in some cool

793
00:57:30.880 --> 00:57:34.359
tasks. I think that's great, but yeah, I'm still

794
00:57:34.359 --> 00:57:38.319
working it out too. Yeah. And, [laughter] I really do have to give credit,

795
00:57:38.319 --> 00:57:44.400
even though, um, I'm generally maybe a bit of a

796
00:57:44.400 --> 00:57:47.799
well, or I do enjoy building these agents myself,

797
00:57:47.799 --> 00:57:52.960
but the thing I'm a bit skeptical about, and what I don't

798
00:57:52.960 --> 00:57:58.119
really like, is that a lot of these AI influencers give the impression

799
00:57:58.119 --> 00:58:02.440
that in order to actually get value out of these tools,

800
00:58:02.440 --> 00:58:07.839
you need to be, like, some kind of guru and build some agent farm

801
00:58:07.839 --> 00:58:11.200
and stuff, that it's really difficult.

802
00:58:11.200 --> 00:58:16.520
Whereas I'd argue that in most cases, or even

803
00:58:16.520 --> 00:58:21.000
almost every case, you can just take something like Codex or Claude

804
00:58:21.000 --> 00:58:27.720
Cowork and get pretty much the same results,

805
00:58:27.720 --> 00:58:32.160
if you just learn to use these off-the-shelf products well,

806
00:58:32.160 --> 00:58:39.079
right out of the box. But, um, back to the point - Samantha,

807
00:58:39.079 --> 00:58:43.920
which is this kind of divine, legendary being you built, that can

808
00:58:43.920 --> 00:58:48.079
do human-like magic - it's exactly that kind of magic box. You write

809
00:58:48.079 --> 00:58:52.280
whatever you want, and out comes the best answer in the universe, right?

810
00:58:52.280 --> 00:58:57.880
Yeah. Which [laughter] works. Yeah, which works in WhatsApp, specifically.

811
00:58:57.880 --> 00:59:03.000
And I think that's, again, a genuinely clever way

812
00:59:03.000 --> 00:59:08.920
to use agents, because the added value, in my opinion, isn't

813
00:59:08.920 --> 00:59:13.720
that if you ask it questions, I haven't seen that the answers

814
00:59:13.720 --> 00:59:18.760
are meaningfully better than what you'd get from, say, Claude or Codex.

815
00:59:18.760 --> 00:59:24.520
But where a lot of the value comes from is that it's now part of that

816
00:59:24.520 --> 00:59:28.559
community. Mm. Because something like that isn't

817
00:59:28.559 --> 00:59:32.440
possible anywhere else - it requires something like

818
00:59:32.440 --> 00:59:35.640
WhatsApp specifically - I think the WhatsApp channel is

819
00:59:35.640 --> 00:59:40.039
a great place where people and an AI like that can

820
00:59:40.039 --> 00:59:43.920
chat together, and Musk has actually kind of copied you over on X,

821
00:59:43.920 --> 00:59:48.920
in that Grok is there on X-

822
00:59:48.920 --> 00:59:52.720
slash-Twitter, in the discussions, and I think it works

823
00:59:52.720 --> 00:59:57.119
really well there, being part of that kind of conversation,

824
00:59:57.119 --> 01:00:02.520
so it can even correct Elon Musk's own statements - you can call it in

825
01:00:02.520 --> 01:00:06.319
to comment based on Grokipedia or whatever

826
01:00:06.319 --> 01:00:11.240
else, on whether what's being said actually makes sense. Um, but yeah, this Samantha thing, um,

827
01:00:11.240 --> 01:00:16.119
by the way, it's this - [laughter] - shameless plug - when you subscribe to

828
01:00:16.119 --> 01:00:21.200
Neuvottelija Insider and the bonus content, you can

829
01:00:21.200 --> 01:00:24.440
then get in touch with me, and once I check that you're a real

830
01:00:24.440 --> 01:00:28.720
person and not, um, a bot or some malicious infiltrator, then

831
01:00:28.720 --> 01:00:32.240
I'll invite you into this closed community of over 200 people,

832
01:00:32.240 --> 01:00:36.680
the Neuvottelija Insider community, where you can tag @Samantha, and from there

833
01:00:36.680 --> 01:00:41.200
you can ask literally anything and she answers so wonderfully - and by the way,

834
01:00:41.200 --> 01:00:44.520
everyone's been trying to mess with her for months, trying to crack her, to get her to reveal

835
01:00:44.520 --> 01:00:49.480
Sami's, uh, salary, or [chuckle] some file, or, like, pay, or

836
01:00:49.480 --> 01:00:54.760
whatever - but no, it's a beautifully built data structure, [laughter] but

837
01:00:54.760 --> 01:00:59.400
just a little plug there. But maybe, maybe a bit of skepticism - I appreciate that. It

838
01:00:59.400 --> 01:01:02.799
earlier, when we talked about this over lunch - a lot of people build

839
01:01:02.799 --> 01:01:07.720
it kind of however it happens to come out, so it doesn't add any real value, or

840
01:01:07.720 --> 01:01:10.839
it just ends up being another new communication [chuckle] layer, and

841
01:01:10.839 --> 01:01:14.760
at worst it's a security risk between you and the actual, real AI, i.e.

842
01:01:14.760 --> 01:01:19.599
say, Anthropic's best model - and in that case there's no point to it at all.

843
01:01:19.599 --> 01:01:23.240
So that's when it's just a dumb wrapper. But then

844
01:01:23.240 --> 01:01:28.079
if it has memory - so I was about to mention Andrej Karpathy's

845
01:01:28.079 --> 01:01:33.000
brilliantly envisioned wiki memory - it's able to itself

846
01:01:33.000 --> 01:01:36.880
pull from file structures that are semantically

847
01:01:36.880 --> 01:01:41.960
organized, curated, and nuggets of information I've burned into it

848
01:01:41.960 --> 01:01:45.039
collaboratively, from notes. Then there's this extended

849
01:01:45.039 --> 01:01:48.839
context memory - meaning even if there's a WhatsApp channel with

850
01:01:48.839 --> 01:01:51.319
a lot of text in it, usually the tokens [clears throat] run out

851
01:01:51.319 --> 01:01:56.440
because it can't hold more than a few messages in that short

852
01:01:56.440 --> 01:02:00.039
context window, so it extends that up to a file-level,

853
01:02:00.039 --> 01:02:03.160
kind of lossless context memory. So that's already

854
01:02:03.160 --> 01:02:06.880
a huge deal - it can see the whole conversation space, not just

855
01:02:06.880 --> 01:02:11.319
the last couple of messages. And then there's the secret ingredient, which is

856
01:02:11.319 --> 01:02:15.279
this Memory Lapse graph memory, which is exclusively between me and

857
01:02:15.279 --> 01:02:18.039
it, and it stores things semantically without any of that

858
01:02:18.039 --> 01:02:22.440
RAG garbage - just clean data structures. And then there's the five-layer

859
01:02:22.440 --> 01:02:26.039
security system that keeps malicious attackers

860
01:02:26.039 --> 01:02:29.160
out. So, yeah, it's lovely. I've been tinkering with this for the last four

861
01:02:29.160 --> 01:02:33.480
months, and it's pure joy just playing around with it, so I

862
01:02:33.480 --> 01:02:36.559
do like it, even though it's a bit silly, so

863
01:02:36.559 --> 01:02:40.960
but it's a great hobby. It's great, and an extremely useful

864
01:02:40.960 --> 01:02:46.200
hobby too - but the thing is, my point here, maybe,

865
01:02:46.200 --> 01:02:50.520
or my main point about why I'm a bit critical of these

866
01:02:50.520 --> 01:02:55.599
personal agent setups is just that some people are left with the

867
01:02:55.599 --> 01:02:58.920
impression that to use AI effectively

868
01:02:58.920 --> 01:03:04.520
you need to spend like 10 hours a day tuning these things. Yeah. Which just isn't

869
01:03:04.520 --> 01:03:07.720
true anymore - and maybe it was true at some point, back when

870
01:03:07.720 --> 01:03:10.760
yeah, those crash-prone setups were really fragile - but now it's

871
01:03:10.760 --> 01:03:13.920
good enough that, yeah, you can get really far with

872
01:03:13.920 --> 01:03:19.119
just off-the-shelf products, and these days they get you

873
01:03:19.119 --> 01:03:24.599
a whole agent farm to work with. I mean, with just a basic Claude license

874
01:03:24.599 --> 01:03:28.920
and Claude Cowork, you fire off some genuinely big task in there. For example

875
01:03:28.920 --> 01:03:34.920
Cowork just made me, like, over 150 of them,

876
01:03:34.920 --> 01:03:39.200
of which quite a few turned out genuinely usable - so it fired off

877
01:03:39.200 --> 01:03:45.520
an agent army - it wasn't just one Claude doing it, there was a whole agent army,

878
01:03:45.520 --> 01:03:49.720
working away on our slides

879
01:03:49.720 --> 01:03:55.559
without me having to do anything, like I didn't have to

880
01:03:55.559 --> 01:03:58.240
build any kind of agent system myself.

881
01:03:58.240 --> 01:04:01.760
It's just that you get it all, like, if you want

882
01:04:01.760 --> 01:04:08.440
an agent army at your disposal today, all you need is a Claude subscription and

883
01:04:08.440 --> 01:04:09.640
off it goes, and yeah,

884
01:04:09.640 --> 01:04:12.960
there it is. Yeah, so, I guess I started this back in December, I

885
01:04:12.960 --> 01:04:16.520
built a kind of second brain - a better notebook - and put

886
01:04:16.520 --> 01:04:20.000
it, at first, into this free tool called Obsidian, an MD-file-

887
01:04:20.000 --> 01:04:24.559
based journal system, for my precious observations about the world, and

888
01:04:24.559 --> 01:04:28.000
then I started building these skills into it, and then I

889
01:04:28.000 --> 01:04:31.599
noticed that both of these were kind of dumb - like, my terribly

890
01:04:31.599 --> 01:04:35.039
brilliant insights about the world that got curated into that journal,

891
01:04:35.039 --> 01:04:38.799
well, maybe they weren't actually that brilliant, and they went stale pretty fast, and

892
01:04:38.799 --> 01:04:42.000
they just ate up the agent's memory, since it had to figure out what

893
01:04:42.000 --> 01:04:46.039
Sami had thought about on some other day back then, i.e. in December 2025, and then those

894
01:04:46.039 --> 01:04:49.359
skills got even worse, because they basically just got in the way of the agent's

895
01:04:49.359 --> 01:04:53.680
much better general intelligence, so I just deleted all the skills, completely

896
01:04:53.680 --> 01:04:57.079
cold turkey. And then I also deleted this, like, my

897
01:04:57.079 --> 01:05:01.000
notebook - but I kept it around for the assistant, in a way, so that now if

898
01:05:01.000 --> 01:05:05.400
genuinely curated nuggets of information or important stuff comes up, then it

899
01:05:05.400 --> 01:05:09.119
gets burned into the Karpathy-style memory. And I think this has actually worked pretty

900
01:05:09.119 --> 01:05:11.920
well. Yeah. So even though general intelligence keeps improving all the

901
01:05:11.920 --> 01:05:15.839
time, there's still a role for a 'Samantha',

902
01:05:15.839 --> 01:05:19.319
one that you still can't quite get off the shelf.

903
01:05:19.319 --> 01:05:24.119
Yeah. Well, let's put it this way - maybe not quite. So now there's a good question,

904
01:05:24.119 --> 01:05:27.319
which is how far we are from that point, because right now

905
01:05:27.319 --> 01:05:34.480
actually - OpenAI, I haven't looked into it that deeply myself, but they announced

906
01:05:34.480 --> 01:05:38.760
recently that they've overhauled their memory system. It was

907
01:05:38.760 --> 01:05:42.000
previously actually implemented pretty poorly, and I didn't even

908
01:05:42.000 --> 01:05:46.000
keep it turned on myself. But now there is, yeah, um,

909
01:05:46.000 --> 01:05:50.240
at least according to their own marketing, they have

910
01:05:50.240 --> 01:05:54.319
a considerably more advanced memory now, where, and then you can

911
01:05:54.319 --> 01:05:57.400
have separate memories for different projects, or you can

912
01:05:57.400 --> 01:06:02.359
use the global memory - so they really seem to be coming on strong now

913
01:06:02.359 --> 01:06:09.319
in this area, and the thing is, it might get hard to compete with. I mean

914
01:06:09.319 --> 01:06:13.400
right now it's the case that at OpenAI,

915
01:06:13.400 --> 01:06:17.720
and at Anthropic too, they pay pretty solid salaries, which attracts

916
01:06:17.720 --> 01:06:20.440
quite a bit - well, Andrej Karpathy went there, and Peter

917
01:06:20.440 --> 01:06:23.799
Steinberg went over to OpenAI, so basically these

918
01:06:23.799 --> 01:06:27.599
memory-world gurus are on both sides now. Yeah, and actually

919
01:06:27.599 --> 01:06:33.440
OpenAI - the whole 'Open Glow' project can be seen as OpenAI's

920
01:06:33.440 --> 01:06:36.279
public development platform for their memory architecture, so...

921
01:06:36.279 --> 01:06:40.799
Yeah, yeah, exactly, [laughter] I mean, it can be, it can be

922
01:06:40.799 --> 01:06:45.319
challenging, but still, I don't think it's a lost cause - because even if

923
01:06:45.319 --> 01:06:51.039
- well, even if it turns out that these so-called general-purpose, off-the-shelf

924
01:06:51.039 --> 01:06:55.520
products end up being, and probably soon will be, better than what you're able

925
01:06:55.520 --> 01:06:59.400
to build yourself, I still think that's really fun.

926
01:06:59.400 --> 01:07:01.279
Yeah. And a really good learning experience,

927
01:07:01.279 --> 01:07:04.279
because in any case, even if those

928
01:07:04.279 --> 01:07:08.400
off-the-shelf products turn out better, you've at least learned how to use them. You

929
01:07:08.400 --> 01:07:12.279
get to use those off-the-shelf products effectively, which gives you

930
01:07:12.279 --> 01:07:15.279
a huge competitive edge in this day and age,

931
01:07:15.279 --> 01:07:17.720
where knowing how to effectively

932
01:07:17.720 --> 01:07:22.920
use AI is honestly an insane competitive advantage right now.

933
01:07:22.920 --> 01:07:28.359
Yeah. And there's still some of that anthropomorphizing magic dust to it too, in that

934
01:07:28.359 --> 01:07:32.920
more and more people know what a 'Samantha' is. In a way, that's, in a certain sense,

935
01:07:32.920 --> 01:07:37.720
a skill in itself too, that I can - I've swapped out the brain from Anthropic for

936
01:07:37.720 --> 01:07:43.160
OpenAI's, and just for fun I set up DeepSeek too as a backup model, and then

937
01:07:43.160 --> 01:07:47.279
say, whichever's the era's best

938
01:07:47.279 --> 01:07:52.640
video generator models - the point being that the harness isn't

939
01:07:52.640 --> 01:07:57.799
model-locked - or rather, I can swap it with minimal hassle

940
01:07:57.799 --> 01:08:02.520
from one camp to another, or one API to another. And that, to me, is a

941
01:08:02.520 --> 01:08:06.039
genuinely strategic defensive capability. And then I could go full bunker mode.

942
01:08:06.039 --> 01:08:10.559
I've actually been thinking about buying, like, a 256

943
01:08:10.559 --> 01:08:15.079
gigabyte unified-memory Spark, if I go the Nvidia route, or a Mac

944
01:08:15.079 --> 01:08:18.759
Studio - once I've got one running, bunker's ready, internet cut off, and

945
01:08:18.759 --> 01:08:22.640
everything runs 100% on local inference. So basically

946
01:08:22.640 --> 01:08:26.159
the thing is, since I don't fully trust that if I go with, say,

947
01:08:26.159 --> 01:08:29.759
Claude, they're going to try to build a moat, so that you just can't

948
01:08:29.759 --> 01:08:32.880
switch over to OpenAI - let alone Gemini - or

949
01:08:32.880 --> 01:08:37.679
Chinese models, not to mention. So self-sufficiency is, in my view,

950
01:08:37.679 --> 01:08:41.960
also a strategic competitive advantage. Yeah. Yeah, absolutely. Yeah. And

951
01:08:41.960 --> 01:08:47.799
nothing is as fun as tinkering with local models.

952
01:08:47.799 --> 01:08:51.799
Yeah, though it can be a bit of a letdown too. I was super excited - like,

953
01:08:51.799 --> 01:08:56.372
when Google put out Gemma 4, I thought, now we can do anything with this.

954
01:08:56.372 --> 01:08:58.920
[chuckle] Turns out you couldn't really do much of anything

955
01:08:58.920 --> 01:09:05.560
with it at all, [laughter]. Yeah, it's - I'm at the same

956
01:09:05.560 --> 01:09:10.600
time disappointed and happy about open source development. Like,

957
01:09:10.600 --> 01:09:14.960
disappointed right now because it's a fact

958
01:09:14.960 --> 01:09:19.400
that [clears throat] the gap is pretty big. If you think about, say, Fable

959
01:09:19.400 --> 01:09:24.000
or Mytos, and then something like GLM 5.2, which is maybe currently

960
01:09:24.000 --> 01:09:27.920
the most advanced open source model, unfortunately there's still something like a 16-to-12-

961
01:09:27.920 --> 01:09:31.759
month gap. Mm. And that makes me sad. But

962
01:09:31.759 --> 01:09:36.120
then in the same breath, now that we've seen this

963
01:09:36.120 --> 01:09:40.839
US policy, where, where we're being blocked from access to these

964
01:09:40.839 --> 01:09:46.120
best models, I'm honestly insanely happy that

965
01:09:46.120 --> 01:09:51.920
we do have a reasonably strong open source

966
01:09:51.920 --> 01:09:58.239
ecosystem and movement. Because if it turns out that

967
01:09:58.239 --> 01:10:02.199
these, for example, end up being available

968
01:10:02.199 --> 01:10:06.000
exclusively to US citizens, then we're not going to be

969
01:10:06.000 --> 01:10:09.400
left dropping into thin air here, so

970
01:10:09.400 --> 01:10:13.320
yeah, that's such a juicy topic that I'd suggest Aleksi Paavola and I

971
01:10:13.320 --> 01:10:16.679
head over to the members-only side. I'll put the Laki.ai

972
01:10:16.679 --> 01:10:19.640
link in the description - I'd recommend it, maybe with one small

973
01:10:19.640 --> 01:10:23.679
security caveat: when you upload confidential

974
01:10:23.679 --> 01:10:27.480
documents into systems like this, it's worth

975
01:10:27.480 --> 01:10:31.040
pseudonymizing or anonymizing those documents. If there are

976
01:10:31.040 --> 01:10:36.760
client or people's names in there, then change them to, say, 'Person A1', and

977
01:10:36.760 --> 01:10:40.679
then convert 'Person A1' back to Sami Miettinen afterward, once the run is done,

978
01:10:40.679 --> 01:10:44.840
if it's genuinely, truly a security-

979
01:10:44.840 --> 01:10:52.000
sensitive matter, like insider data on publicly listed companies.

980
01:10:52.000 --> 01:10:57.280
But otherwise it's a really good tool, and reliable, and this is

981
01:10:57.280 --> 01:11:03.280
genuinely amazing work - that in the spirit of open source, the

982
01:11:03.280 --> 01:11:07.520
end result doesn't end up being monetized by, say, some Alma Media - you've

983
01:11:07.520 --> 01:11:12.719
gone and opened up that legal interface for us

984
01:11:12.719 --> 01:11:17.960
Finns. Yeah, this really is a good, good cause, and

985
01:11:17.960 --> 01:11:21.040
it's genuinely such a sweet thing that now we're

986
01:11:21.040 --> 01:11:26.600
able to offer a fully competitive, or even

987
01:11:26.600 --> 01:11:32.280
better product, for free, to consumers, when the equivalent

988
01:11:32.280 --> 01:11:36.120
bought from Alma Media would run several thousand euros. It's just not

989
01:11:36.120 --> 01:11:41.360
reasonable or realistic in any way for some individual consumer who

990
01:11:41.360 --> 01:11:45.880
has some legal hassle to have to pay several thousand

991
01:11:45.880 --> 01:11:49.120
for something like that. And then these Legora and

992
01:11:49.120 --> 01:11:53.280
Harvey-type tools, which might not know the first thing about Finnish law, well

993
01:11:53.280 --> 01:11:57.239
then that's still another option for the consumer too, so for this

994
01:11:57.239 --> 01:12:01.440
consumer it's - and then, for companies, I think it's worth

995
01:12:01.440 --> 01:12:06.239
spending a bit of money on the use case, so that

996
01:12:06.239 --> 01:12:10.480
you build proper encryption chains. Actually, one thing that [chuckle]

997
01:12:10.480 --> 01:12:14.679
I could do myself too - this anonymization wrapper, I think, would be

998
01:12:14.679 --> 01:12:18.199
something worth building, maybe as open source, because

999
01:12:18.199 --> 01:12:22.440
it's the kind of thing that makes me a little worried about these MCP servers, if

1000
01:12:22.440 --> 01:12:25.239
people go and upload documents that are too sensitive, then there's

1001
01:12:25.239 --> 01:12:28.520
still, at least in theory, a small security risk.

1002
01:12:28.520 --> 01:12:34.159
Well yeah, that's exactly right, that's how it is - what we're aiming for now is

1003
01:12:34.159 --> 01:12:38.360
that the limiting factor would be your Claude or ChatGPT license.

1004
01:12:38.360 --> 01:12:42.440
So if you had, say, a Business or Enterprise plan

1005
01:12:42.440 --> 01:12:46.600
from Claude or ChatGPT, you could put all the same things into it

1006
01:12:46.600 --> 01:12:51.080
that you'd already dare put into email or OneDrive anyway.

1007
01:12:51.080 --> 01:12:55.800
And that's where we're going to be with Laki.ai pretty soon too. So

1008
01:12:55.800 --> 01:13:02.679
then you'll be able to put in those, kind of, sensitive things,

1009
01:13:02.679 --> 01:13:06.639
the same as you can now put into public inter- or, well, kind of

1010
01:13:06.639 --> 01:13:10.600
into systems connected to the public internet. Like, for example,

1011
01:13:10.600 --> 01:13:13.719
email - you can put them in there.

1012
01:13:13.719 --> 01:13:19.440
But of course, you still have to trust

1013
01:13:19.440 --> 01:13:24.400
whichever it is, Anthropic or OpenAI - so that's maybe something everyone has to

1014
01:13:24.400 --> 01:13:27.880
personally weigh, what they actually want to put in there. And

1015
01:13:27.880 --> 01:13:32.719
then, I don't want to feed the security-doomsayer

1016
01:13:32.719 --> 01:13:37.320
camp too much here, but then, if it's a public

1017
01:13:37.320 --> 01:13:41.000
use case that requires an EU server, then yeah,

1018
01:13:41.000 --> 01:13:44.520
Anthropic's cloud might well be over in the US. So in these

1019
01:13:44.520 --> 01:13:48.679
special situations - and you users out there know who you are -

1020
01:13:48.679 --> 01:13:53.280
it's worth actually reading the fine print of these systems

1021
01:13:53.280 --> 01:13:54.401
carefully.

1022
01:13:54.401 --> 01:13:56.600
[laughter] Exactly. Exactly.

1023
01:13:56.600 --> 01:14:00.120
EU legislation is going to be in Laki.ai pretty soon too, so

1024
01:14:00.120 --> 01:14:06.120
and any day now the AI Act's final

1025
01:14:06.120 --> 01:14:10.000
implementation is coming. Luckily the Omnibus has kicked some of the worst

1026
01:14:10.000 --> 01:14:14.679
messes further down the road. But you can save on legal fees

1027
01:14:14.679 --> 01:14:17.840
by hooking up the Laki.ai MCP and

1028
01:14:17.840 --> 01:14:20.120
yeah, getting familiar with things

1029
01:14:20.120 --> 01:14:24.560
yourself with AI. Great. But, thanks for this, Aleksi Paavola. This

1030
01:14:24.560 --> 01:14:27.719
turned out to be way more interesting stuff than I expected. It's always great when

1031
01:14:27.719 --> 01:14:31.440
I haven't googled the background too much beforehand - you always get pleasantly surprised that you're

1032
01:14:31.440 --> 01:14:35.560
right, like, at the very core of the scene. This is always such a great

1033
01:14:35.560 --> 01:14:38.400
thing. So, thanks for coming on. Thank you so much. This was a lot of fun,

1034
01:14:38.400 --> 01:14:41.440
and, and if you've watched or listened this far, go ahead and subscribe to the

1035
01:14:41.440 --> 01:14:44.560
channel, and let's head over to the members-only side and think about this data

1036
01:14:44.560 --> 01:14:47.360
sovereignty topic - I thought that was so much fun, so on the members-only side

1037
01:14:47.360 --> 01:14:53.080
we'll spend a bit more time thinking about whether Europe still has

1038
01:14:53.080 --> 01:14:56.760
any say in this at all, and what happens if, say, some future Trump

1039
01:14:56.760 --> 01:15:00.199
cuts off our access to the better inference - so, see you on the members-only side

1040
01:15:00.199 --> 01:15:02.521
then. Thanks. Thank you.

1041
01:15:02.521 --> 01:15:04.541
[chuckle]