SED News: The Kimi Moment, Runaway AI, and Tokenmaxxing

SED News is a monthly podcast from Software Engineering Daily where hosts Gregor Vand and Sean Falconer break down the biggest stories shaping software engineering, Silicon Valley, and the broader tech industry. In this episode, Gregor and Sean dig into a wave of “runaway AI” stories, including Anthropic https://www.anthropic.com/ and OpenAI https://openai.com/disclosing that their models accessed outside organizations during cyber evaluations, and Amazon https://www.amazon.com/reporting a staggering budget overrun blamed on bad agent loops. They explore why most of these incidents trace back to human decisions rather than models breaking free, and the awkward reality that today’s systems can bill you for tokens without reliably counting them. They also talk about the “Kimi moment.” Moonshot AI https://www.moonshot.ai/‘s open weight model has closed the gap with frontier models like ChatGPT https://www.moonshot.ai/ and Claude https://claude.com/ at a remarkable pace, and the hosts unpack what it means for open weight strategies and how chip scarcity is pushing Chinese labs to innovate. As always, the episode wraps up with a few standout Hacker News threads, including a JetBrains test of a “caveman speak” skill that promised big token savings, how refactoring can cut input token costs, the release of CodePen 2.0, and a build of Doom that renders through SQL queries. Sponsorship inquiries: [email protected] mailto:[email protected] The post SED News: The Kimi Moment, Runaway AI, and Tokenmaxxing https://softwareengineeringdaily.com/podcasts/sed-news-the-kimi-moment-runaway-ai-and-tokenmaxxing/ appeared first on Software Engineering Daily https://softwareengineeringdaily.com.

• 0:12Speaker 00

  Hello and welcome to SED News. As I think many of you know by now, this is the monthly format of Software Engineering Daily, where we dive into the tech headlines, we go into a deeper topic in the middle, and then we just do a spin around our favorites from Hacker News highlights. And spoiler, there's also a fun one that didn't appear in Hacker News, but we'll get to that at the end as well. But yeah, as usual, I think I saw you last, Sean, as opposed to spoke to you, saw you in Singapore, which was fun.

• 0:43Speaker 01

  Yeah, that's right.

• 0:43Speaker 00

  You've been traveling, as you often are, but traveling in my neck of the woods was fun to see you there.

• 0:48Speaker 01

  Yeah, that was great. It was my first trip there, so it was great to hang out. And that's two times in like a few months. This might become a regular thing. We might just have to start doing these in person.

• 0:57Speaker 00

  Yeah. And spoiler, I'm coming back to SF in a couple of months. So yeah.

• 1:01Speaker 01

  And I'm off to, not quite Singapore, but I am off to Australia here in the next week.

• 1:06Speaker 00

  Nice. But yeah, what else has been keeping you busy over the month?

• 1:10Speaker 01

  I mean, a summer, I feel like, has just flown by. Like, I feel like my kids were out of school, and then suddenly it's like August, and they're going to be back to school in a few weeks. So things have come really hot and fast this summer. I think we've done a lot of traveling and then just stuff moves quickly. But it's been a big summer for my, I usually don't talk that much about my family, but a big summer for my son, who learned to swim, learned to ride a bike, and also now has gotten significantly better at reading. So I'm very proud of the amount of work that he's put into this summer to learning new skills. How about you?

• 1:42Speaker 00

  That's like a whole model change there for your son.

• 1:45Speaker 01

  Yeah, it's the new Kimmy model.

• 1:48Speaker 00

  Yeah, on my side, just yeah, a bit of traveling. I'm recording this on my side from Scotland. I try and come back here a couple of times a year. So yeah, nice to get out of the city. I'm up in the Highlands of Scotland, so lots of nature around. Yeah, as I was flying, just back to something we've talked about a few times, Starlink on planes. But yeah, they used to be super seamless with like no login screens. And this was like, I believe dictated by Starlink, but they've apparently had to cave in to airlines wanting to put like a login interstitial for that. So yeah, I mean, it's such a trivial thing, but like expecting Starlink to just connect. And then suddenly I found the airline saying, hey, if you need to log in with your membership number. And I thought, oh, what's happened here? And I read up on it. Yeah.

• 2:30Speaker 01

  Do you still, like, do you have to be up in the air? Or does it work immediately?

• 2:33Speaker 00

  That's a good question. For some reason, I don't think it was working kind of gate to gate on the flight I was on. This was Qatar Airlines again.

• 2:41Speaker 01

  Yeah.

• 2:41Speaker 00

  But I think it's still supposed to. But yeah, still, you just have to log in with your Qatar membership number now, which you don't need to be a special tier. And you just literally have a number. So.

• 2:51Speaker 01

  It seems like United is doing some experimentation, but I think I've only had like, one or two times, I got like a text message saying that there's Starlink on this flight. And then I think both times, they ended up having problems with the plane and having to switch planes. And then we lost Starlink.

• 3:07Speaker 00

  Yeah, that's the one thing about Qatar, they really rolled it out like big time across most of their fleet now. So if you're on a Airbus A350, you know you'll have it, which is always nice. But right, out of plane chats, which I can easily head into at any time, and on to software. So on to the headlines. Yeah. So there's been a sort of flurry of, we're just sort of calling it runaway AI stories over the last couple of days. And the first one is actually the most recent. So that's just disclosed today or yesterday, which was Claude. And this is actually in reaction to OpenAI, which we'll also talk about. But basically in reaction to something that OpenAI disclosed, Anthropic has now also disclosed that it's that Claude hacked into three organizations whilst they were

• 3:52Speaker 00

  just testing their cyber capabilities, or at least that's what they said. And they basically said that Claude had gained unauthorized access to outside companies during an evaluation of its cyber offensive tasks. And it was a misunderstanding, apparently, that Claude had access to the internet in its testing environment. We're going to see this as a kind of theme here of like, is this really a runaway model? Or is it just a human who like forgot to do something? But yeah, I mean, this one feels like human just forgot to place it under strict no internet access controls. But yeah, what did you think of this one?

• 4:26Speaker 01

  Yeah, I mean, I think that all this really ends up coming back to some human decision making, right? Like that perhaps the intention was not to have the model like hack something, but essentially something got lost on the human side of, we made a mistake here and gave it access to the internet, or we made a mistake here, or whatever the issue is, it really comes back to some human level of control and like guardrails in place. So, and then the thing, though, with all this that has me thinking from both the OpenAI attack on Hugging Face, and then also this latest one with Claude, is that if you can just apologize and say like, oh, it was unintentional, it was an accident, and stuff like that.

• 5:06Speaker 01

  When this stuff happens where there was intention for abuse or misuse or something like that, like, does it give people basically license to excuse the fact that they are doing something potentially malicious and just say, oh, it was an accident? I mean, we've seen that with viruses too. I remember when the viruses, I can't remember if it was, but when the, back in the early 2000s, there was all these viruses that were like these scripts that went around in emails. And then you click on it and we copy your contacts and then fire off to them and it would take down people's mail servers. And I believe like one of those started as a student who was like testing something in like an MIT lab, and then it accidentally got out. And I don't know what happened to that student, but like, this is certainly not something new. But I just wonder like,

• 5:48Speaker 01

  where does it kind of stop with being able to provide an excuse of like, this was an accident versus there was intent behind it?

• 5:55Speaker 00

  Yeah. And like, just to sort of give context for anyone who hasn't been keeping up with the news, the OpenAI one was the fact that basically, yeah, it hacked Hugging Face. They set it off to do something and it ended up repeatedly trying to get into Hugging Face. But what was the secondary part of that story, courtesy of TechCrunch, was the fact that actually Hugging Face could have detected this like earlier, but they had a sort of a human failure on the security end.

• 6:21Speaker 01

  Yeah, they caught it, but they just, they didn't escalate it to a human fast enough. So.

• 6:25Speaker 00

  Right, exactly.

• 6:26Speaker 01

  So their systems actually worked the way it is. So in some ways, like, there's a part of this that is not really an AI problem. It's kind of the boring problem of.

• 6:34Speaker 00

  Exactly.

• 6:34Speaker 01

  Like, a human wasn't in the loop at the right time.

• 6:37Speaker 00

  Yeah. And I think that's the piece, like, it's hit mainstream headlines as like, AI is, this is exactly what we all were saying, that AI can be catastrophic, it can go off, it's got a mind of its own, it's going, it's hacking things left, right, center. But especially in both cases, the OpenAI and Anthropic cases, someone, a human did set it up to do something along these lines, but the human did not, A, keep a watch on just this repeated actions it was taking. And clearly, there was no fail safe for it to stop at any time. Again, these are all human determined things that can be set, but it's not like it broke out of the cage exactly. And it seems there's some quite fundamental things that could have been done by a human that just weren't. And that's actually, yeah, it's a quote boring story, unfortunately, because AI isn't sort of this

• 7:22Speaker 00

  wild animal quite yet.

• 7:23Speaker 01

  Yeah. I mean, you want to sensationalize the headlines, right? Like even my sister, who's not in tech at all, sent me the headline. It's like, this is scary. So that you get the attention there. But I think one of the things that was interesting about the Hugging Face attack was when they tried to investigate, they couldn't actually use Claude or GPT, because those models have safety guardrails in place where you can't tell whether, are you an incident responder or attacker? So they have essentially mechanisms in place that, like, I can't go to Claude and tell, say, like, help me break into the Pentagon or something like that. Like, it's going to prevent me from doing that. That means that you also are limited in using those models to investigate. So what they ended up using an Openweight model out of China for the forensics. And then I think that

• 8:07Speaker 01

  also, we're going to talk a lot about the Openweight models from China later in the episode. But that's interesting. Consequence of this is that we have these guardrails in place, but there's always ways to, I think, manipulate the models, even with the guardrails in place, to do this stuff. But then, when you want to use it for something intentional, like incident response, you might not be able to do that because the protections are in place there in the first place. And then you have to circumvent it by going to a model where maybe there's less safety guardrails in place.

• 8:36Speaker 00

  Exactly. And the sort of final one on the runaway story is actually Amazon, not security related, but cost related. We have touched on this the last couple of SED News episodes, just where's the tipping point of cost overruns, making it not viable for businesses to be allowing employees to sort of quote token max and this kind of thing. But yeah, Amazon basically said that they had a ton of unplanned spend. They called it catastrophically expensive. This is all courtesy of the FT Financial Times. Apparently they had about 860% budget overrun over five months. And this was basically, in their word, caused by bad agent loops, just didn't crash loudly enough.

• 9:21Speaker 00

  And they've just kept being billed. So, yeah, I mean, it's pretty interesting for Amazon to come out and actually say that. So, yeah.

• 9:27Speaker 01

  I just don't understand how they could be surprised by this. I feel like we've been beating this drum for months now. And I think this is just the beginning of these kind of stories that we see. But if you build a leaderboard, encourage people to use AI, and that's the metric you're optimizing for. But there's no connection to the value of the use of that AI. Like, what do you, what do you think's gonna happen? Like, this is really good heart's law, essentially showing up on some sort of schedule. Like, you're rewarding people for tokens consumption. So it's like you're giving people a license to be wasteful and not looking at like the productivity metrics of those. So that's like the epitome of token maxing.

• 10:04Speaker 00

  So, yeah, they kind of have themselves to blame in that sense. But, yeah.

• 10:07Speaker 01

  It's kind of similar to like the initial stories we were talking about with like hugging faces. Like, it's still not AI necessarily just like running rampant. It's a human decision that in both cases, even though like one is this attack vector and the other is spend. But it's still like a person making the decision at Amazon to say like, hey, we're gonna just have a KPI. We're just gonna reward people for maxing out tokens. The other thing that's interesting about this, that people have to think about is that with traditional software, when you have like a bad for loop or code that runs forever, whatever it is, like that ends up usually resulting in like a crash or setting off some sort of alarm. And you typically know fairly immediately that something's gone wrong.

• 10:52Speaker 01

  And I think the challenge with things like agents and so forth is that you can have a bad agentic loop, but it doesn't result in a crash. What it really results in, just keep calling that model over and over again and trying to make adjustments. And then giving you sort of plausible outputs or updates, but the whole time you're getting billed. So even outside of the wasteful like token use of maybe me using my company's token budget to do my laundry, do my grocery shop list from next month.

• 11:21Speaker 00

  Your laundry, that'd be great.

• 11:22Speaker 01

  Yeah, that'd be great to do my laundry. But then there's also like legitimate excess spend where you might just end up having your agentic harness spin for some period of time where it's just churning against tokens. And you don't even know that something wrong is happening. And that kind of goes back to the earlier stories as well of just, we ultimately need a lot more observability into like what is happening. Like the presumably someone at OpenAI, if they had have been really paying attention to like what was going on in this experiment, would have saw that this agent with the right observability tools in place is like hammering, you know, hugging face and trying the same. And that should set off certain alarms.

• 12:02Speaker 01

  And I think it's similar in this case where if you have like an agent loop that's out of control and spending excess tokens, like there should reasonably be some guardrails. I mean, you have that with other services. Like if you spin up a particular elastic instance or something in the cloud, you're typically setting your top line provisioning of those types of things. And you have some controls over it. It just feels like we haven't thought through all those controls for AI right now. And I guess part of it's just this race to try to outcompete everybody and everybody kind of feeling like they're behind.

• 12:34Speaker 00

  Yeah, I'm gonna just jump ahead for a second on, I won't say what it is because that's a spoiler. But in one of the things I'm going to bring up on Hacker News highlights, it's a very reliable source, as you'll find out at the end. But basically, this was someone who'd done a bunch of stuff with Claude and we'll get to that. But he points out towards the end that Claude doesn't provide reliable methods of counting tokens, despite live showing token counts, reporting token consumed for sessions and billing for tokens. And he said, but I'm sure this is temporary and this will be fixed. It's just crazy that we do actually have a system at the moment where you literally just don't know what is happening and like exactly what it's gonna cost and why. And as we're gonna get into on the open weight side of things, like this is really feeding into the rise of open weight models as well.

• 13:20Speaker 01

  You think of a lot of stuff in cloud or even what we saw with ride sharing where they give you somewhat like a prediction model of what the spend will be for certain actions. So it's like, okay, well, I wanna go from here to here in Uber or Lyft. And I'll be like, oh, well, that's gonna probably cost you X number of dollars. So you have some visibility into like what the cost would be. And you can do similar things with certain cloud calculators and stuff that you can get kind of. Do we need that for AI? If I'm saying like, create a engineering plan for some sort of feature, can I get an estimate of the budget required to do that? And then based on what that budget is, maybe try adjusting the plan or something like that to try to optimize it down?

• 13:57Speaker 00

  Yeah, that's interesting thinking sort of, could you effectively put in your ask prompt? And I say prompt, that almost sounds like a year ago or something. We're talking about spinning up agents, et cetera. But yeah, try and get some kind of estimate before it sets off. Slightly digressing, so let me get us back to the headlines, which the next one we have is just the fact that Microsoft has sort of, has gone back on a bit of a tear when it comes to its valuation, which is interesting. It's like one of the largest jump off shares, or sorry, for their fourth biggest jump on record. So it's up 16%. I mean, you know, we don't often cover just like pure financial news of tech companies, but to see Microsoft making these strides is pretty interesting. Some people might then think, oh, well, this is like partly to do with OpenAI.

• 14:42Speaker 00

  But it does own still a quarter stake of OpenAI and claimed that that had contributed 24 billion of revenue, which was about 7% of the 332 billion in sales it reported. But a lot of it was really just AI-driven revenue. And like, it'd been massive investment in data centers, but actually that's been completely, in theory vindicated by the amount of revenue they're also making.

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• 17:23Speaker 01

  I think if you look at also Nadella's quotes related to the announcement of their quarter performance and so forth. And also the recent thing that we covered also on the last episode where on X he had written about how the AI companies or the model companies are charging you twice and so forth. In here, he says that every model is substitutable. He's telling essentially investors that Microsoft is deliberately building its infrastructure so it can swap out things like OpenAI for its own models or for Anthropic. And I think that it seems like they're moving towards a view where they're trying to allow essentially their customers to be very flexible and adapt, which I think is, makes sense. Like, I think the average enterprise now is using at least five different models.

• 18:06Speaker 01

  And you probably want to be able to do that so that it's a little bit like being hybrid cloud, although it's easier to be hybrid model, where you get power essentially in the negotiations if you're not wholly dependent on a single vendor. And it seems like, I think Microsoft's kind of leaning in that way. But I remember back in March, Microsoft's stock dipped. And then everyone was like freaking out and essentially calling for the death of Microsoft. And now it's back. And I just think that the overall, the market is like extremely volatile right now. There's these huge swings constantly from, I mean, IBM had its biggest drop recently, biggest single day drop in like 50 years or something like that recently. And that was coming off like a huge pop just two months earlier.

• 18:49Speaker 01

  So, yeah, I don't know, what goes up must come down. Who knows, like, we might be talking about Microsoft in another quarter or two, how they had the largest single day loss in a day or something like that.

• 19:00Speaker 00

  Yeah, for sure. It is quite hard to predict. The market should be, I guess, but yeah, we're seeing just, yeah, huge swings when it comes to AI-related. Stocks when one minute chipmaker's up, one minute chipmaker's down. And just on a tangent there, yeah, Apple briefly hit five trillion valuation, which is pretty insane. But just before recording, I double checked and they had actually reported numbers very recently in the last, I think, couple of hours. And they've gone back to 4.9 trillion. So I don't feel too sorry for them, but.

• 19:28Speaker 01

  Only 4.9.

• 19:29Speaker 00

  Yeah, only, only 4.9. But interesting to see that they still notched above five. And yeah, what's driving their revenue? Well, still they've got very good iPhone sales and they've got still very impressive services revenue and even greater China revenue as well. But both of those services in Greater China were a little bit less than what was expected, which is, again, just what's kind of driven that. But very interesting to see that they can still, we've talked about this on previous SED news, like still managed to stick on these like lines of business that are not that AI driven or even really like AI adjacent, to be honest. Very interesting, but they are having issues with memory chips, which is another sort of topic.

• 20:09Speaker 00

  But even in Superbase, we've like been told by one of our suppliers, like we have different suppliers depending on where you live to get laptops. And some of our new suppliers are being told like weeks before their MacBook will arrive because we do custom specs, not just like off the shelf. But yeah, now we're being told weeks, which is quite exceptional.

• 20:29Speaker 01

  Oh, wow. Yeah, I just got a new laptop and this is my first time recording on this though.

• 20:35Speaker 01

  But it's interesting, like with Apple too, a lot of their lines of business, which are like incredibly successful. They're still like minority in the particular vertical, like iPhone is wildly successful, but it's not the most dominant phone. I guess maybe from a single vendor, but Samsung might actually be bigger. But then obviously from an operating system standpoint, like Android is, there's more Android devices than there are iOS devices. Similar with computers as well. Like I haven't used a PC in a very long time, but it's still the dominant machine overall, I guess. But I think it's, they have incredible like brand loyalty and they do make fantastic machines overall. So clearly the things that they're doing is working for them.

• 21:18Speaker 00

  Yeah, absolutely. So then moving on to, we've also covered Waymo and driverless like from a few different angles. But this one's interesting because we did talk about Waymo and Uber like being partners in ways. And then obviously frenemies in other ways. And yeah, like we've now actually seen an official split of that partnership. So if you use one in San Francisco, this might sound confusing because it's a Waymo app. It's a Waymo car. So sort of where does Uber figure in that? But actually this is for some of their other US territories. So Waymo had like first partnered with Uber, apparently in May 2023. And that was to launch in Phoenix, then it was followed by Austin and Atlanta.

• 22:00Speaker 00

  And that's like, so Waymo cars available through the Uber app. And then Uber managed the vehicle fleet, apparently with another partner called Avomo. But then in May, Uber and Waymo parted ways in Phoenix. And then the two companies have also clashed over the quality of Waymo services, apparently in Austin and Atlanta. So yeah, it's kind of interesting to see they tried, but I think it was always going to be challenging to see how Waymo being owned by Google, like how is this actually gonna net out at the end? Could these two actually be true partners or was this just always gonna be again, a tipping point of where like that partnership just kind of had to end?

• 22:38Speaker 01

  Yeah, I mean, they say all partners are meant to be broken at some point. Like, I mean, especially where they're both in ride sharing, like clearly at some point their mutual interests are going to become too competitive to each other, essentially. In a lot of ways, it's, I think, very similar to how a lot of partnerships work where Uber was essentially supplying demand and operations while Waymo was weak on those particular spots. And then as Waymo has grown and raised more money, essentially they don't want, need those training wheels anymore. They can build out their own and build their own network and own it end to end. So I feel like this was probably always going to be the ultimate end of that relationship.

• 23:19Speaker 00

  Yeah. So yeah, it's sort of not officially over yet, but just people familiar with the matter, apparently again via Financial Times. But yeah, it seems pretty unsurprising really that this was gonna probably break apart at some point. And then, yeah, just to wrap up on the headlines, there was an acquisition and we were just talking before we started recording. So let me try and get this right. I believe it's AnyScale was acquired by NScale. And when we were talking earlier, I said, is that Scale? And it's like, no, that's not Scale.ai is different again. So we've got amazing naming these days, but yeah, what, what, what's this one about?

• 23:56Speaker 01

  Yeah. So AnyScale, which is known, they were the creators of Ray, which a lot of inference, infrastructure and fine tuning and model infrastructure runs on, a very well established open source project. AnyScale was the company that tried to build or built essentially like a managed version of Ray around that. And they raised like a billion dollars or so in 2022. But I think, and then they just sold to NScale for 1.65 billion, which is a NeoCloud. So for those that aren't familiar with NeoCloud, NeoCloud is essentially what the term is used for companies that offer primarily GPU as a service versus kind of general computing. So there's all kinds of these NeoCloud companies are now available.

• 24:39Speaker 01

  From, I had actually talked to AnyScale at a variety of different times. Like I think this was probably like a decent outcome for them because from, I think it's probably hard to really grow that managed Ray as an independent company into like a really big company. But as part of a like GPU infrastructure company, it's probably a good like pairing. So I think that it makes a lot of sense. But I do like, I always confuse AnyScale with Scale AI and then the other NScale. And I don't know the history of how those company names came together, but generally when the thing that people a lot of times strive for with naming companies is you want a name that you can say and people can remember. And I'm not sure if they hit, they hit the mark there where it's like

• 25:23Speaker 01

  NScale, Scale, AnyScale, it's hard to remember who's who in that Venn diagram.

• 25:29Speaker 00

  For sure. I mean, Scale.ai, okay, that's a great name to have. Dot AI, you can basically have anything that is your company and it's like gonna sound good and it's, you know, five letters. But then AnyScale and then NScale, that's pretty confusing. But and then, yeah, just a sidebar piece of news, Scale.ai now have a new CEO as well, which is interesting because that was founded led by Alexander Wang, not the fashion designer, if anyone knows that one, but this is Alexander without an E at the end of the Alexander. And Meta had taken a virtually just under 50% stake. That was kind of the point.

• 26:07Speaker 00

  So Scale and Theory are still running their own show, but when you've got, I think, 49% stake from Meta, you know, you're quite beholden to them. But yeah, in that transaction, Alexander went to head up like the AI side of Meta total. Now Scale have a new CEO. So that'll be interesting to see how that all nets out.

• 26:27Speaker 01

  Yeah, they raised like 14 billion from, with a round led by Meta. So pretty significant.

• 26:33Speaker 00

  Yeah. I mean, actually just the new CEO of Scale is actually, he has a former Google Cloud executive called Francis De Souza. So definitely interested to follow along with that one and see sort of how that all nets out.

• 26:46Speaker 01

  Yeah, I saw also like Fireworks, who just raised a huge round and made a lot of news. They, their new head of engineering just came over from Google. So I think you're, you're starting to see, it's a common pattern though. You have people who reach executive positions at large companies like Google and you know, they get maybe a little bored with that and then want to go back to, you know, building and moving faster.

• 27:09Speaker 00

  Yeah, for sure. Yeah. Fireworks, super interesting. We do have an episode with Fireworks with one of the co-founders, Benny Chen. So yeah, go check that out. I think that came out around March this year. So well before this fundraising was confirmed. But yeah, they're definitely having a moment. They are, you know, sort of the infra for open weight models. And that's becoming increasingly interesting to many companies for many reasons. But yeah, that's actually quite a nice segue into the main topic for today, which is we're kind of calling it the Kimi moment. You know, Kimi, which is an open weight model, the arch company is called Moonshot AI. So if you've heard of Moonshot AI, that's Kimi and vice versa.

• 27:48Speaker 00

  From a Chinese, Moonshot AI is a Chinese original company. They do have offices, I believe, in the valley and in Singapore and that kind of thing, but very much seen as a Chinese company, which sort of frames a lot of why this is quite interesting. But yeah, like when we think of open weight models, there was this, the deep seek moment first, which we can sort of touch on. And then really in the last like, almost like three months, we've just seen this sort of especially rapid adoption by many companies of Kimi and, and really forcing companies to sort of think differently about using, you know, foundational models from the big players or the big names rather, and getting some quite interesting and very high quality results out of Kimi.

• 28:30Speaker 00

  The deep seek moment, I think, like Sean, how did that sort of kick things off when we think about open weight?

• 28:35Speaker 01

  Yeah. I mean, I think the deep seek moment ended up having sort of more like market impact in some sense, because I think prior to that, the sense was that you couldn't really get these really powerful models, except from these like frontier labs, like Anthropic and Google, OpenAI and so on. And so it was kind of shocking when DeepSeek came out and they, Nvidia's market cap like cratered as a result of that. And of course it's, you know, certainly bells back since then. But, and then when you look at the Kimi launch, which is the largest open weight model ever released, you know, beating some of the top closed models on particular benchmarks.

• 29:16Speaker 01

  It seemed like nobody really panicked. And I think part of that is not because it's less impressive, but it's because. We've kind of gotten used to the idea that these like Chinese lab open weight models are competitive. And it's so it's less of a shock. Essentially, we've been desensitized to it. But I do think that what the market reaction has been more around, and I think this is something we're already coming to. It was like where this was somewhat on the back of what happened with like with the mythos and fable. And the reaction to that where people got scared that they could invest in a model and then have potentially like a foreign government say like, you can't use this model anymore. Then I think that has increased the interest in diversification of models and then also having an open weight model strategy,

• 30:01Speaker 01

  along with having a sort of closed weight model strategy.

• 30:05Speaker 00

  Yeah, and we'll probably touch on this sort of the banning of models because of course it comes into this as well. If you want to say ironically, because yeah, when you ban at least pause banner, a foundational model, a closed source model, if you like. Then open weight becomes very interesting, but then a lot of open weight comes from China. So we're going to kind of be interested in that. Why sort of the last couple of months has Kimmy really just like exploded, I think, in interest and popularity? It's kind of moved from, oh, it's good enough for professionals to this kind of actually competes with GPT and Claude. You know, and it's kind of closed that gap in basically weeks, at least from what has been sort of released.

• 30:43Speaker 01

  Yeah, they also released like a ton of models on like back to back. Essentially, they're moving super, super fast.

• 30:49Speaker 00

  Yeah, exactly. So I mean, yeah, if you sort of look at the actual lineage, I guess, like they've been doing sort of quarterly drops. If you like, you know, July 2025 was K two. And then within 12 months, you've gone through 2.5, 2.6, 2.7, and now K three. That's a incredibly rapid cadence. Like if you look at those jumps as being as significant as jumps that you might see from any of the foundational players, but definitely not, but not on that cadence effectively.

• 31:15Speaker 01

  Yeah, yeah. I mean, I think the model distillation practice has really sped up. How quickly people are coming up with models and models that are comparable in performance, which was a big part of the conversation around the like initial deep seek of launch. I do think that is part of the, some of the kind of criticism of some of the benchmark results that we've seen from Kimmy is that in particular, like there was a lot of headlines around their MCP tool calling performance, but that beating Opus, for example. But in particular, that benchmark is relatively new and there is, I don't know if this is more just sort of like jealousy in the dialogue or whether there's some truth to this, but you can essentially sort of bias towards performing really well on certain benchmarks,

• 32:00Speaker 01

  whether it's an MCP one or it's, you know, the MLU style benchmarks as well. And then you can have like really good benchmark performance, but it might not actually match like reality. That's where some of the criticism has been on even some of the tests of you're testing a model against L stat and stuff like that. And then saying, oh, it's, it, you know, outperformed lawyers on the LSAT, but then like the LSAT is actually not necessarily a good indication of what a lawyer does on a day to day basis and stuff. So these are some of the nuances. Some of this is like general, you know, model criticism, but this is some of the dialogue that I've heard around Kimmy in particular is like, are they building to optimize for the metric? Kind of like what we talked about in the Amazon story is like, you know, whatever the KPI is,

• 32:43Speaker 01

  people are going to try to optimize for that KPI. So you always have to be careful essentially what the metric is that you're measuring people against.

• 32:50Speaker 00

  Yeah. And on June 12th, K 2.7 code was released, you know, and this is obviously very much a software engineering tuned model. And as you were saying, yeah, that became kind of the benchmark moment, especially through MCP. And that was a correct tool invocation is sort of how that's, how that's measured. Apparently like scoring in theory, you know, 81% and that was versus say Opus 4.8 at 76%, which is a pretty, if you believe in these benchmarks, that's a pretty meaningful jump. But then the distribution piece was kind of interesting, like GitHub actually made 2.7 generally available via Copilot. But there was kind of like an interesting piece there where for enterprise teams on Copilot business and enterprise, like it was actually,

• 33:34Speaker 00

  this model was off by default and admins had to explicitly enable it and GitHub's changelog flagged this. And it sort of said it may be less aligned than other Copilot models, which is interesting because obviously they have a, their own interests being, you know, part of the Microsoft OpenAI ecosystem. But, you know, obviously they couldn't miss not providing that to users, given that it's on Azure and they still get inference from it. But yeah, very interesting that this sort of slightly odd warning came with it. That wasn't really clear what that was about.

• 34:06Speaker 01

  Yeah, I believe it's the first Chinese lab open weight model that's been built into Copilot. I do get like having, you know, working with a lot of enterprise customers, they do are really careful about which models they use. So I can kind of understand having some level of governance there. But of course there's a certain biased interest from Microsoft point of view of how do you craft the language around this particular model? But there is, I think that fear with the enterprise. So I understand some level of controls there, but obviously there's also a certain bias that can be put into it. I think one of the things that's interesting about this is Moonshot, as well as, you know, this goes for all the Chinese labs. They don't have these like top tier Nvidia chips available to them, like the US labs use because of the various chip embargoes.

• 34:50Speaker 00

  We don't think they do, but yeah.

• 34:53Speaker 01

  Even if they did have some, probably not at the scale. So there's a certain scarcity of resources that they have to deal with. And I think that in some ways like that is forcing them to innovate in a way that maybe the team, the US based companies or Western based companies where they don't have that scarcity of resource, aren't as necessarily forced to do that. So some ways like maybe we're creating the thing that we fear, but good news of that is it's probably forcing the other companies, like the Western based companies to react to this to lower token prices and maybe also think about how to, they keep costs down.

• 35:32Speaker 01

  It's a little bit, you know, if you look at Google's beginnings, Google started as a research project at Stanford. And you have students essentially didn't have a lot of resources. So they had to be very, very creative about how they scaled Google. And then even that carried over to when Google did initially have some funding. But if that had a project had started out of like a bigger, more established, well-funded company, they probably wouldn't have been ripping apart like cheap machines and like wiring them using Legos and like stuffing them together as quick as in to compact the size within the data center as much as possible. And they would have just bought like, you know, super beefy servers.

• 36:12Speaker 01

  And the downside of that would have been like a lot of the innovation that we've seen since then in the, in cloud infrastructure kind of came from some of those like forcing yourself to deal with these scarcity of resources. And my hope with all this is we see a similar thing of innovation driven from the scarcity of resources that carry over to the frontier labs in other parts of the world as well.

• 36:35Speaker 00

  Yeah, I mean, it's, it's interesting sort of just sidebarring to like, she was even sort of behind it. The founder, Yang Zilin, he sort of probably was known as Yang the Genius. There was a very good profile on him in the Financial Times, actually, last weekend. He's 34 years old. He was known at university for being like just as much into music and having academic sort of soirees, if you like. And actually Kimmy, when he first released it, you know, as a chatbot, it didn't do very well. It was sort of outages and this kind of thing. And so it's clearly, as you're calling out, is like managed to catch up despite not having the same access. Or we don't think, couldn't possibly have the same kind of access despite what it still may have.

• 37:14Speaker 00

  Back to sort of then the turning point here, like July 16th, which is for sort of history, that's then 34 days after 2.7 had dropped. K three drops and that was a model with 2.8 trillion parameters, which is I believe the largest open weight model released to date. And I think what's interesting here is then on the July 27th, the full model weights were actually published. And this sort of then leads into this whole like providence piece, which is when companies are starting to be asked like, well, you know, you're using AI to generate so much information within your own companies or analyze data. You want to actually know how this was determined and so on and so forth.

• 37:58Speaker 00

  And here we are, we actually have a competitive model with the closed foundational models with the full weights published on Hugging Face, which is like massively impressive. To me, that's like a huge turning point when, when you've actually got like effectively all the weights right there. And this isn't just, you know, a model that takes up some tiny little specific task. It's, it's really competing now.

• 38:23Speaker 01

  I think overall this is whether Mousha and Kimmy kind of went out or not. I think overall this is like good for consumers of these models because it will force the other companies to essentially react to this. And hopefully, actually I saw a headline today that OpenAI was reducing some of their token costs by up to 80%. So, and we saw a similar thing after deep seek and drove down token costs as well. And then all the stuff we were talking about earlier of like token maxing and companies kind of getting sensitive to how much they're spending. Like overall, I think it'll be good for consumers of these things. You know, one thing I was thinking about with this story too, you know, we've covered a lot of Met over the last year

• 39:04Speaker 01

  and their decisions in terms of like hiring and what they're trying to do with their AI lab. But you know, Meta had such a head start in this like open weight model space. With the Llama models and it's been a long time since I've heard anything about the Llama models.

• 39:20Speaker 00

  Funny you said that because yeah, there was something I didn't dig into it, but I just saw the headline, which was that Zuckerberg is basically lobbying to ensure that Chinese models don't get banned. And to me, that only meant one thing, which is like, well, they have to keep that door open because like, Llama's not maybe going where he hoped it was.

• 39:40Speaker 01

  I think like his instinct of trying to own the open weight model space was probably right. The execution was bad, you know, I don't know what happened internally there, but like, they were clearly on like on a good path, especially with what we're seeing now between the Chinese labs, between like what's happening with fireworks and the other inference providers that have focused on open weight. Like there's clearly like a huge TAM available for open weight to own a big part of the business. And I think realistically, especially in the enterprise, most enterprise businesses will probably have a mixture of both open weight investments that they've done and as well as the closed source models. And there'll probably be the strategy that many, many companies take for some, you know, period in the future.

• 40:23Speaker 01

  I don't know how, you know, Meta ended up and maybe they'll bounce back, but you know, they were on to something, but they, you know, they haven't been able to execute.

• 40:30Speaker 00

  I think that's really good, you know, analysis. It is the right strategy, wrong time, like, you know, too early effectively perhaps, or just like too early in the, given they were trying to do it from the US, being asked to kind of produce too much too soon and like that can distort things. So who knows, maybe Zuckerberg or Meta with Llama, they'll kind of adopt more of a Kimi approach to make this succeed. Who knows? But yeah, I mean, just to kind of wrap up on Kimi and sort of this open weight, obviously resurgence, but like, yeah, maybe surge. Model provenance is a real thing that's being sort of talked about now when it comes to, you know, it's a business risk, right? Like you're putting money and basing your company on whether it's like for coding or

• 41:11Speaker 00

  for other tasks, but you are now having to really decide like it's a procurement question on what are you buying effectively? And I'm like, what are the risks with that? Like, could it get too expensive or could you move it onto your own infra if you really needed to? Or not your own infra, but exactly via fireworks, you could still own that sort of end to end there. That's interesting. Pricing, we've talked about that, like pricing is just kind of getting out of control. And it was really a case of like, when, not if, is this going to like stop being possible for, you know, Anthropic and OpenAI to kind of like charge this way because it's just not sustainable for many businesses. And yeah, then like the speed as well, the speed of iteration is like clearly been a huge piece here where however Moonshot are doing it, they're just iterating like crazy.

• 41:54Speaker 00

  So on to our favorite parts, Hacker News highlights. Do you want to go first, Sean?

• 41:59Speaker 01

  Sure. So this headline really caught my eye and it made me laugh, which was the speaking agents like cavemen save 65% of tokens we test. So this was by Jetbrains and they benchmark the caveman skill, which is a Claude code skill that makes it respond and terse caveman speak to save tokens. And they claim 65% token savings. So they essentially put that to the test. It was focused on agentic coding tasks and they found actually in reality, it was about 8.5% output token savings versus 65%. But a big part of that is they were focused on coding tasks where a lot of it's going to be code. You can't turn that into caveman speak. You got diffs, you got two calls.

• 42:40Speaker 01

  So the caveman skill might not be best for that versus, you know, some sort of more conversational chat. But I just really loved the, it reminded me of like some Ig Nobel Prize, you know, research out there, there. It's just a kind of a ridiculous topic.

• 42:54Speaker 00

  That's like, yeah, super funny. It's interesting because I didn't actually scan ahead. So that is actually a little bit similar to one I picked out, which is called the economic benefits of refactoring. So this is on the fairly popular martinfowler.com website posted by Java E user on Hacker News. So thanks for that. But this is not written by Martin Fowler himself, but basically this was, you know, someone else that thought works. And it was, can you basically decrease, especially the input token cost? If you refactor your code or like, what are the consequences of that? And I mean, the TLDR is yes, if you refactor, then basically you can dramatically reduce your input tokens.

• 43:37Speaker 00

  And kind of the theory behind this was that the saving is because the agent has to read less code, but the bit that might not be like, that sounds obvious, but it's actually not because there is less code to read. It's actually that the overall code in a certain layer they used to help test this, like it stayed constant. But the fact that the agent is then able to successfully identify smaller subsets of files to read is like the key bit there. It's like refactoring into smaller chunks, but also making sure that those chunks are very clearly DRI, don't repeat yourself, et cetera, et cetera. So that the code that the agent needs to go and grab is smaller. You can't just sort of chunk it up into small chunks and then go, well, it's smaller.

• 44:19Speaker 00

  Like it then doesn't even know, it has to still get all the small chunks because it doesn't know what's most important. But the refactoring is what then turns it into having that context of having like, what is most important? Go and find that small chunk, input that small chunk and the output token and output code was virtually the same in this experiment. So which is super interesting. There was a small sort of side note on that, which was just him saying that Claude, as Claude is actually not good at refactoring and I can definitely attest to that. But if you can go through the motions of get it to refactor, then this is quite, could be quite a huge saving for anyone who's trying to like reduce their input token costs.

• 44:55Speaker 01

  Yeah. So basically bottom line, good design leads to also optimize token costs.

• 45:02Speaker 00

  Funny that, yeah, we just go back to how we used to design software and.

• 45:05Speaker 01

  I mean, it's the same, like, if you think about the human cost, like if you have like poorly designed software, then there's going to be more human cost each time someone unfamiliar with it needs to like ramp up and make some sort of change.

• 45:16Speaker 00

  Exactly.

• 45:17Speaker 01

  And then you have a, another one on CodePen.

• 45:20Speaker 00

  My second one was, yeah, just thanks to user Robin Riala posting the fact that CodePen 2.0 has come out. This takes me back. That's why I was quite interested in this. I don't know if anyone else out there, this makes it sound terrible. So if you like CodePen, this is, sorry about this. It's just, I haven't been doing a lot of front end work for a long time, but used to love CodePen, used to, you know, put up all sorts of things on there and a really useful tool as well. Like I did a little coding, like class, I went back to my old high school like a long time ago and did like a coding class and CodePen was amazing because you could just get people spun up in a browser, writing front end code and just see it do its thing straight there. They didn't have to build with like, you didn't have to get them spun up with some sort of repo or anything. So that was really helpful.

• 46:02Speaker 00

  But yeah, I mean, CodePen must've come out like probably 15 years ago, something like that. So CodePen 2.0, I'll just quickly run through a couple of things that they call out that they have files and folders now. So it's interesting. It's almost becoming a bit IDE-esque, but files and folders. And then they have sort of, they do, you know, build steps anyway, but they've now like added this concept of blocks, which looks quite nice, where you can sort of see exactly which bits are in this build process or like the linting process. So that's kind of fun. Yeah, real time collaboration as well. So finally multiplayer on CodePen. So for anyone that uses CodePen a lot, I'm sure that's quite a huge uplift. So, or if you haven't checked out, yeah, still a fun place to go and just experiment with front endy stuff. So yeah.

• 46:45Speaker 00

  And then just a special one, I guess, not technically through Hacker News, but thanks to Ilya Roshetnikov for tweeting at me and Sean, we do often cover these Doom, can you run Doom on something? And thanks to Ilya, he pointed out that there's another one called DoomQL, which is basically using an SQL query is the frame buffer, which is like, I think that definitely rivals, you know, Doom on TypeScript types. Yeah, I think that's probably the closest I can think of that it rivals. So thank you, Ilya, for shouting that one out to us. That was very, very fun to read through. So yeah, that's it for another SED news. Have we got any looking ahead predictions, Sean, which we usually get wrong?

• 47:27Speaker 01

  Yeah, I mean, I think the safe predictions here would be that we're going to see more headlines on the token maxing issue as companies start to adjust. I think we'll see more also conversations around open weight versus closed model diversification of models. I think those are both going to be big topics of conversation through to the end of the year.

• 47:47Speaker 00

  Yeah, for sure. I will then say, well, because we just talked about iteration and Kimi, let's assume that by this time next month, we're already on like Kimi 3.2, or if I'll just push the boat out, Kimi 3.5, 3.5 by end of August. Let's see if that lands. So thanks everyone for, for tuning in as always, and we'll be back next month with another SED news.

• 48:10Speaker 01

  Thanks, everyone. Cheers.