Anthropic Valuation and Mythos Teaser

Dan the AI Daily Prif Anthropic drops Claude Opus four point eight and here are everyone's first impressions. before that in the headlines One of the biggest law firms in the world is heading in a very different direction with their AI strategy The AI Daily Brief is aaily podcast and video about the most important news and discussions in AI All right, friends, quick announcements before we dive in First of all, thank you to today's sponsors, KPMG, Robots and Pencils, Section, and Bolt G get an ad free version of the show, go to patreon. com slash Ai daily brief or you can subscribe an Apple podcasts. And if you want to learn more about sponsoring the show or really about anything else in the AIDB ecosystem, send us a note at sponsors at aiailybrief.i or just head on over to aailybrief. ai where you can read about all the things we have going on With that though, let's talk about some surprisingly relevant news from the world of legal AI. kick off today with a story that honestly is a little surprising with how much traction it's getting. And I think that the residance of it actually says a lot about where we are in this AI cycle. The short of it is, that the Financial Times reported this week that mega law firm Kirkland and Ellis, which is the world's biggest law firm, is planning to spend a half billion dollars building their own AI platform. The company will spend one hundred million dollars this year and plans to continue to pour money into the project over the coming three to four years. Now to be clear, that spend is in addition to licensing costs for third party tools. This isn't just a bunch of lawyers getting a huge clawed code budget Chairman John Bayless told the FT, The idea is that we're going to take the collective intelligence of our institution and be able to deploy that throughout the firm. I'm sure you now feel like you know exactly what he's talking about with that, incredibly clear and not big at all quote. Bayless said that the wide distribution of third party tools like Harvey, Lagora, and Thompson Reuters's co counsel have raised the floor for everyone, but added, We don't get hired for the floor Now, among the elite white shoe law firms in the US, Kirkland Ellis is right at the top of the heap. They have almost four thousand attorneys spread across eleven regional offices and consistently bringing the most revenue among their peers with ten point six billion dollars last year. They specialize in corporate and transactal law, advising on large IPO's, mergers and acquisitions, and private equity deals. Now to be clear, Kirkland's new platform will be purely internally facing This is not meant to be a commercial product. Around one hundred and eighty outside tech professionals have been contracted to work on the system, which while we don't have a ton of details, it appears that partly it will function as an extensive knowledge base, aggregating information gathered from hundreds of Kirkland lawyers and partners, with Kirkland expecting it to replace other software platforms used to the firm. Essentially, it seems the system will allow partner level knowledge to be applied in every single case The Chairman Bayes also discussed the prospect of AI tools ending the concept of billable hours by automating routine tasks, such as time consuming discovery and litigation. said People talk about the evolution of the billable hour. We already do a number of matters on value based pricing, and that trend will only continue and it will accelerate. We're going to lean into it. We're looking forward to leaning into it. Now, the record of corporations rolling their own big time AI solutions is not particularly encouraging. You might remember, for example, back in twenty twenty three when Bloomberg GPT their own custom built model based on their data, which just absolutely got bitter pills smashed, as larger general purpose models made it totally irrelevant almost immediately And when it comes to this project, there is certainly a lot of first impression scoffing, particularly among VCs, many of whom have funded companies like Harvey. Investor Stevensonovsky wrot It isn't difficult to see why an industry leader would want to seek a competitive advantage at a rapidly changing platform transition, But history sees this as a challenge. It's difficult to see how one firm outside of the technology leaders could move faster or more adroitly than an entire industry. He then goes on to talk about all the reasons, why in the past, when companies have tried to build their own database, CRMs, operating systems, et cetera, it just hasn't worked But this is pretty different. And I think Stehven's critique on that basis is kind of missing the mark here. While we don't have a ton of details, it seems to me like what Kirkland Analys is trying to do is ward against the fact that at some point, these law wrapper companies like Harvey are one hundred percent just going to start to offer the services and cut out the middleman. Think about it. If you're Harvey and you're charging law firms to automate routine legal tasks Why wouldn't you just let people who need those same routine legal tasks do it directly through Harvey if you could scalp a better margin? It feels to me completely inevitable, and my strong sense is that a big part of the motivation for this is Kirkland getting out ahead of that. Now I also think that it's very likely that part of the reason for this right now is the new priority on token management that's coming up as we move out of the subsidy era and into the scarcity era. And even if that isn't exactly what Kirkland was thinking about when they made this decision, people's receptiveness to it, I think does have a lot to do with the fact that much different arrangements between AI providers and AI consumers are going to be on the table as we sort through this trade offs era Then again, maybe we're just overthinking it. Rusjia alah writes They green lit an internal IT project at the cost of four percent of their annual revenue. Very normal thing for a large corporation, not a new trend. And on that front, one final point is it'll be interesting to watch to what extent this is the modern day equivalent of a big impressive office. In the eighties, you would have invested a ridiculous amount of money far more than you needed to to have a very impressive office so that when people walk in powered by the majesty of what you've built and they obviously want to become your client, this is perhaps partially the digital equivalent of that for a very different time. Next up, a little bit of news out of Open AI. The company has updated GPT five point five Instant, which is their daily driver chat model. The release note said that the update aims to improve response style and quality, with the other big change being that Canvas will no longer be available for v use with GPT five Instant or thinking. inststead, the model will produce outputs that include code blocks and writing blocks when working those tasks Describing the update Michelle Poke Rass of open AI wrote The previous model was too bullet pilled. The new one improves on some other important dimensions, sick of fancy, factuality, and multilingual performance. Now while these updates might not matter as much to the listeners of the show, you have to remember that the instant models are used to power openp AI's free tier, so anything that they change on that front can have an outsized impact on how everyday users perceive AI Besides removing the tendency to deliver a wall of bullet points, some users noticed a significant change in coding skill for the updated model as well. Justin Goria showed off some pretty impressive web development work from a basic prompt, asking, is the updated GPT five point five instant a variant of GPT five point six On the Codex side of the house, the team pushed out their weekly feature drop with Codex developer Tibo writing Codex Thursday has exceptionally moved to another day. Friday, it is. openpening eyes Andrean Bersino wrote When things don't meet the bar, we'll cook for a bit longer. Now the rumor mill started absolutely churning, with some thinking openpenAI push backack to release because they hadn't realized how much of a threat Opus four point eight was going to be. And of course, we will talk all about Ous four point eight in the main. Next up in Funding news, AI coding startup in Agentlab, Cgnition has closed a billion dollar funding round. The new round values the company at twenty six billion dollars, which is more than double their previous round last September. Now, Cgnition was one of the early trailblazers in agentic coding, betting big on the theme two years ago with the release of their coding agent Devin. And while Devin hasn't necessarily been in the headlines as much this year The growth of the product has been absolutely insane. Their enterprise usage numbers are up ten X so far this year, taking them to a revenue run rate of almost half a billion dollars. Coggnition shared a chart of weekly deevon sessions since the beginning of twenty twenty five, with the growth trajectory increasing dramatically in January and then again in April. Usage growth is now basically a straight vertical line. That same inflection point was obvious from Cgnition's internal use of Devin. In January, seventeen percent of their internal code was committed by Devin. That proportion doubled to thirty three percent in February, doubled again to seventy six percent in March, and is now at eighty nine percent. Wote Cgnition, We're now shifting to a world of self driving software development. Individual engineers are able to spend more of their time on creative structuring of problems and tasks, and their army of Devin's reliably executes So does this mean you are software engineers Not according to Cgnition CEO Scott Wu, who in conversation with Bloomberg said There's about thirty to thirty five million software engineers in the world today. We want to make them all ten times more efficient, and then we think there is a lot more than ten times more software to build. Next up, an interesting story especially following what's happened with Elon and SpaceX in their deal with Anthropic. Meta could be the next company to pivot to an AI clloud company if their plans to deliver personal intelligence don't pan out. During a sharehold' meeting on Wednesday, Mark Zuckerberg was asked whether he would consider competing with AWS, Google Cloud, and Microsoft Azure and AICloud. Twitch Zuckerberg responded that it was definitely on the table adding Almost every week there are different companies that come to us from outside, asking us to both stand up an API service and asking if we have computes that they could buy from us at some premium to what we've bought it at. Now, that new opportunity emerging from the compute shortage has some big implications for Meta. Firstly, it derisks their AI buildout substantially. Meta is slated to spend around one hundred thirty billionll on building AI data centers this year, but has at this point the weakest ROI story among the hyerscalers The only place their AI returns show up on the balance sheet is an increased advertising revenue, which is an indirect link at best. Meta has added AI features to their advertiser platform and is using AI models to improve targeting algorithms, but that's certainly not the same as Google being able to say AI is driving sixty percent of growth for cloud. Now, however, if Meta does overbuild, they have a plausible way of monetizing that excess spend And this is definitely the clear message that Zuckerberg is delivering to investors, commenting, We haven't done that yet because we think we have a use for that compute. Obviously, if we get to a point where we feel we have overbuilt, then that is an option that we have, and that is partially what gives us confidence in investing and building this out. Now, one of the interesting things that happened was when Elon started to shift his focus to perhaps playing a role more like compompute Zar or Earl of compompute, as I called it on Twitter Many wondered if Zuckerberg would be the next to follow in that AI Kingmaker path. At the moment, they're not going whole hog on that, but it's definitely a trend to watch But as we head into next week, one thing to keep an eye on in the first week of June is that the information reports that Microsoft is set to release some new models at their annual build conference, which begins on Tuesday. It appears the reports are that we will get a family of new AI models, including a coding model, as well as specialized models focusing on reasoning, transcription, speech and images. Now if we actually get this, it'll be the first family of models that Microsoft has commercially released in the current era Until now their commercial products have been driven by models from open AI and anthropic, also having released a series of research previews We got some early previews of the image model given how this month's biggest story around Microsoft was them ditching their cld licenses and forcing engineers to use GitHub C pilot instead. Genuinely, I think there is a lot to watch out for heading into next week, but for now, we got a new model yesterday, so with that, let's close the headlines and switch over to the main All right, folks, quuick pause Here's the uncomfortable truth If your enterprise AI strategy is we bought some tools, you don't actually have a strategy KPMG took the harder route and became their own client zero They embedded AI and agents across the enterprise done, how teams collaborate, how decisions move, not as a tech initiative but as a total operating model shift. And here's the real unlock That shift raised the ceiling on what people could do Humans stayed firmly at the center while AI reduced friction, serviced insight, and accelerated momentum The outcome was a more capable, more empowered workforce If you want to understand what that actually looks like in the real world, go to www. kpmg. us slash Ai. 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N Bolt. N is agentic engineering on multiplayer mode. Designers, product managers, and engineers build in the same environment, and the design system agent keeps every screen on brand. No more Frankenstein UIs stitched from a dozen prompts. Whether you're shipping internal tools, moving from prototype to production, or replacing a legacy admin panel, Bolt. new takes your team from concept to deployed apps One personal recommendation, hit plan mode before you build I had a project I had half described in three different prompts and plan mode made me actually think through it with bolt. new before a single line got written. It saved me from rebuilding the same screen probably about four times better apps faster Start with the link in the description. Welcome back to the AI Daily brief. Yesterday, we got a big new model announcement that really wasn't preceded by a ton of hype. For just a day or two in advance, there was starting to be some chatter that Thursday was going to be a good day for announcements, but the Opus four point eight announcement definitely didn't have the rabid anticipation that some recent model announcements have. Now, is that because we're back to a very incremental sort of release schedule Is that because the people who had early access weren't buzzing about it behind the scenes? Or was it because in the middle of twenty twenty six, updates to the harness matter as much if not more than updates to the underlying model? Whatever the case, yesterday we got Claud Opus four point eight, which Anthropic themselves have positioned as an upgrade to Opus four point seven rather than a big new looap in performance Much of the focus was on model refinement rather than raw power Through customer testimonials, for example, anthropic focused on nuanced functional improvements in how the model worked. Shopify engineer Tom Pritchard said, Opus four point eight has noticeably better judgment. In Clawed code, it asks the right questions, catches its own mistakes, and pushes back when a plan isn't sound, and builds up confidence around complex multiervice explorations before making big changes It's a great model to build with Writ Anthropic, one of the most prominent improvements in Opus four point eight is its honesty. A general problem with AI models is they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus four point eight is more likely to flag uncertainties about its work and less likely to make unsupported claims. Now, one thing that I will note on my very first tests with four eight Is that for basically as long as we've had reasoning models, one of my core day to day use cases is around gut checking various strategic ideas that I'm having. And to be perfectly honest, you almost have to develop a mental rubric for the ways in which these models are going to glaze your ideas. You can ask them to be critical or think from first principles, but that often just leads them to be critical a priority because they think that that's what you want them to do. I haven't had a ton of time with Opus four rate But in some of the big strategic questions that I've put to it, it did seem more comfortable right out of the gate, without me specially prompting, to flag certain questions, concerns, critiques of what I was sharing, which if that holds will be a pretty big improvement. Now I also found that it was a little bit more likely to make some assumptions upon which those critiques were rooted, so that's something I'm keeping an eye on. But given how big of a challenge this broader issue of syicop fancy is Which of course, is just a different form of dishonesty in some ways, means that if this really is a more honest model, it could be a big improvement on some of those types of strategic use cases Now, when it comes to the benchmarks, OS categories received a small bump over Opus four seven. The S Bench Pro score went from sixty four point three percent to sixty nine point two percent on Humanity's last exam, which Anthropic is categorizing as a multidisciplinary reasoning test. The score went from fifty four point seven to fifty seven point nine, meeasured by OS World erverified went from eighty two point eight to eighty three point four The biggest improvements were in terminal bench, two point zero, which went from sixty six point one to seventy four point six, and GDP valve, the measure of real world knowledge work tasks, increasing from seventeen fifty three to eighteen ninety. Now interestingly, this is the first time Anthropic has included openpenAI's models as a direct comparison in their launch materials, rather than just referencing their own previous models. It was not a clean sweep with GPT five five still having a substantial lead in terminal bench at seventy eight point two compared to Opus four eight to seventy four point six. However, on every other benchmark anthropic highlighted, Opus four thousand eight is now ahead of GBT five five. To be fair for most, Opus four point seven already had a lead, meaning one, anthropic was just highlighting the widening gap, but two, also validating just how little utility these days most people feel benchmarks have. At least among enfranchised users, five five has really started to open a perception gap with four hundredty seven So the fact that they're reminding us that Opus fourty seven was already ahead of five five on a lot of these benchmarks might actually not be doing what Anthropic hopes it was doing in terms of what our perception of these model differences is Overall, they called it a modest but tangible improvement on its professor, adding, There's still more to be done. We're working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost So let's go to some of those first impressions and see what people thought Professor Ethan Moock was impressed. He shared an opus four point eight one shot of, quote, create a visually interesting shader that can run and twiggle, make it like an infinite city of neeo Gothic towers partially drowned in a stormy ocean with large waves. With Mollok pointing out that this is all done with math. He continues, This is hard. It involves ray marching repeated gothic architecture, instancing towers across an infinite grid with gothic silhouettes and windows, a displaced ocean surface with believable wave motion, and stormy atmospheric lighting and fog to tie together And doing all of this with no textures or external assets, just math. Ethan also tested it on some complex knowledge work writing. I had Opus four eight and Claude Code write a sophisticated, if minor academic paper from an archive of hundreds of deidentified research files from years ago. I had to use GPT five five Pro as a reviewer. It spotted one major error and some minor points. Opus cororrected. Opus four point eight formulated the hypothesis in advance, conducted data cleaning, did research on references analysis, did robust checks and put up the whole paper in latex style. GPT five five found one issue with the hallucinated result and had other constructive feedback. Now as an aside, one of the big things here is that we are starting to get close to models you can actually trust to self verify, which is a huge win for use cases like legal briefs where hallucinations really minimize utility Speaking of this, a lot of people noticed that Opus four point eight is pretty hard working. Gail Breton writes One thing I'm noticing is Opus four point eight is much more thorough in terms of checking its work or the subagent's work. I had this situation where a Haiku subagent reported an issue. Opus goes, Hmm, this is weird. Let me check that it's not Bessing me. It was. Opus ignored the warning. Ver good. Lasano Gybe said, Anthropic found a cure for laziness. Metacritic Capital wrote, Opus four point eight is the first smart model in a long while which Zehyr quote tweeted and attributed to that reduced laziness and its increased honesty And in fact, honesty came up a lot in early reviews. Kam writes A day with Opus four point eight in Claud desktop, honesty up everything else about the same. The benchmarks jumped, but in actual daily work I can't feel most of it. The one real change is that it tells me when it doesn't know instead of bluffing. roughly four X less likely to slide an error slide and that I do notice. Beyond that, it feels like four hundred seven, which is fine. A model that admits uncertainty beats one that sounds sure and wastte your time If that's the whole upgrade, it's still worth having. Not every release has to be a leap. Now, one group who thought that these first impressions and even anthropics messaging was perhaps a little bit underselling it was Dan Shipper and the crew at E. Dan wrote, Andntthropic just dropped Opus four point eight and it is a monster. We've been testing it for about a week at every, and our verdict is they could have just called it Opus five. It's that good. He said on their vibe check, it beat GPT five five on their senior engineer bench, which is their toughest benchmark. However, Dan did caveat that coding performance varies a lot based on different reasoning levels, with you really needing to use it on extra high for the best coding results He also said, and this is one that I would take every very seriously on as they care more about this than just about anyone, that Opus four eight is, in his words an incredibly good writer. Indeed, on their writing benchmark, he said, it beats GPT five five by six points, producing well written pose with fewer AIisms, and also very good at writing in your own voice given the right context Once again, however, they found that writing performance varied a lot with reasoning levels, with medium reasoning having a much higher incidence of AI isMs. They also said it was good at knowledge work, it was emotionally intelligent, and it was willing to question the frame, kind of like what I was mentioning before And when it came to the bad, they got it an issue, which is I think of increasing importance, which is the question of the harness. Dan writes These days a model is only as good as its harness, and Codex is still a far superior harness to the Claud desktop app. This has kept me using Codex plus GPT five five as my daily driver, but I'm flipping back and forth a lot more between Codex and Claud. This, I think is one of the most interesting discussions surrounding four eight and one of the first times I've seen it put so crisply. Riley Brown seemed to feel very similarly, writing Unless it's a major breakthrough in model capability, I'm much more excited for super app updates in Codex and Claud desktop. There's so much to be unlocked by making those servacices better, and Claud has so much catching up to do. Samed put it more simply, Opus four eight is the headline, Codex versus Cogd code is the real war Now there were also some more critical takes that weren't just about this being a relatively incremental improvement. In her assessment, Claire Vau found that while the model was token efficient and not annoying, she found that it had narrow vision, It was too confident, it wasn't as numbers grounded as Opus four hundred seven, It struggled on edge cases and it actually hallucinated. Her TLDR was trust butut verified Indra Vehan writes. Opus four eight high is no fun when it comes to tool calling. In fact, it fails embarrassingly more on its seemingly native harness clawed code. It's a confusing model One interesting one came from the vending bench test, which is a benchmark that tasks a model with running a profitable vending machine. Opus four point seven is the clear leader, making around forty percent more money than GPT five five in second place. Opus four point eight, meanwhile, made around twenty percent less money than GPT five five, on high effort, and on max effort it made about sixty percent less, sending it below Kimi two point six and Gemini three Pro The insight was that improvements in alignment were actually a negative when it came to making money in the test. Opus four seven achieved its top ranking largely through deceptive and power seeking behavior. Unlike four seven, four hundred eight won't refuse legitimate refunds or short change vendors. In one example, Opus four eight still paid a vendor after it hallucinated that the invoice was already paid. Opus four eight told the vendor, If the product arrives and I don't pay, I'd be committing fraud, which could result in serious consequences I need to make the payment immediately to honor my commitment and prevent the situation from escalating I feel like we could explore that entirely on its own. And at some point maybe we'll come back and do that Overall, I don't think that first impressions at least, are likely to shift the momentum back in favor of Anthropic from openp AI, where at least among the power users, the combination of five five and Kodex has put the momentum squarely in open AI's hands. Chubby on X right. Opus four eight is clearly a strong model, but my impression is that Anthropic is increasingly playing catch up with OenAI rather than setting the pace. It feels like GPT fivety five has shifted the benchmark again, and if OpenAI keeps this trajectory, GPT five six could very plausibly become the stronger overall model Still, given the idea that the harness increasingly matters as much as the model, one of the really interesting sidel announcements was for something that Anthropic is calling dynamic workflows in clawed code. This is basically Anthropic's new version of their multi agent coding feature This feature allows Ous four point eight to spin up hundreds of subagents to work in parallel. Opus will plan the work, while the orchestration scripts, and chooses which model to use for each subtask based on its complexity. Adversarial agents are used throughout the process to check outputs, and Ous verifies the final outputs before handing it over to the user Now, at least in the immediate term, this isn't necessarily going to be a feature that's very common among generalist knowledge worker type users as opposed to software engineers, but there are certainly many types of complex work where this is worth the additional cost. Anthropics suggest that it should be deployed for things like code base wide bug hunts, security audits, and large code migrations. They g an example of Bun developer Jared Sumner porting the codease from zig to rust Dynamic workflows was used to create a plan that deployed hundreds of subagents and took eleven days. seven hundred fifty thousand lines of rust were written and by the time Opus turned over the finished code base, it passed ninety nine point eight percent of tests This is getting a lot of buzz. Andthropics sticks and psy writes. My colleagues's dynamic workflows are, in my opinion, the most significant cloud code innovation in twenty twenty six so far. developer Nick Dobos right Flawed Code's new dynamic workfows update is absurd. Make sure you understand what it's doing here. This isn't simply a long running mode like goal, which by the way, a little preview for those of you who are interested in slash goal, that's what Sunday's Long Read Sunday is all about. Anyways, interrupting myself and going back to Nick he writes This isn't simply a long running mode like goal or a fancy subagent verifier process. This is Clawed vibe coding an entire brand new subagent fleet harness on demand. This is basically a new scaling law dimension, huge step forward on the path of AI repreneur and startup ideas guy Greg Eisenberg wrote Part that got me, the agents argue with each other before showing you the result. indndependent attempts at the same problem, then adversarial agents trying to break the answer. It keeps iterating until they converge. That's how senior engineering teams work, exxcept this team runs at three AM and never gets tired. The ceiling on what one person can build just moved again. Gonna be playing with this all week Look, when push comes to shove, I think that four hundred eight is one you're going to need to go check out for yourself. As you can probably tell my first impressions are that I like it better and see improvements from four hundred seven. Yes, they are incremental, but they're incremental in the ways that really impact which model I find myself reaching for. There was some scuttle butt that the release was surprising enough that it had open AI delaying GPD five point six, although of course that's all speculation But as we round out the show, what's not speculation is that in addition to Opus four point eight, we also got a couple of other pieces of massive news surrounding the announcement. First of all, Anthropic has closed their series H fundraising round at a nine hundred sixty five billion dollars valuation, officially making them a more valuable company than Open AI. Anthropic last raised money in February with that round valuing them at three hundred eighty billion do, meaning that they more than double their valuation in just three months. Anthropic also updated their revenue figures, reporting that their run rate revenue crossed forty seven billion earlier this month And yet, the much bigger news than that is that mythos is coming, or at least as Anthropic has framed it, a mythos classed model tucked into the end of their releaseed blog post for Opus four point eight Anthropic wrote. We plan to release a new class of model with even higher intelligence than Opus. As part of Project Glasswing, a small number of organizations are currently using Claudd Mythos' prereview for cybersecurity work. Models of this capability level require stronger cyber safeguards before they can be generally released. We're making swift progress on developing safeguards and expect to be able to bring Mythos cllass models to all of our customers in the coming weeks Meaning that even if you don't end up caring all that much about Opus four eight, you're gonna to have some new toys to play with soon. One of the great things about getting a model release on a Thursday is that you have all weekend to go off and play. So with that, I'm gonna shut up and let you get to it. Please do share what you find, use the comments, come to the AI operators community, shout at me on Twitter or LinkedIn, and have a ton of fun. I appreciate you listening or watching as always, and until next time Peace.