Subscribe
Share
Share
Embed
TBPN is a live tech talk show hosted by John Coogan and Jordi Hays, streaming weekdays from 11–2 PT on X and YouTube, with full episodes posted to Spotify immediately after airing.
Described by The New York Times as “Silicon Valley’s newest obsession,” TBPN has interviewed Mark Zuckerberg, Sam Altman, Mark Cuban, and Satya Nadella. Diet TBPN delivers the best moments from each episode in under 30 minutes.
IBM is absolutely nuking. The stock is down 25%. Boom. IBM well, that is a crazy chart. What is that?
Speaker 1:The that's the one week chart? It looks better on the five year because the stock is actually way up in the AI era since the launch of ChatGPT. IBM has done really, really well. The stock has basically doubled since the introduction of ChatGPT during the AI era. You know, are you gonna be a winner or a loser?
Speaker 1:Are you gonna get steamrolled, slopped, something like that? But it's been doing well up until today when the company reset the narrative around their server business specifically. So the high level reason that IBM is not well positioned in the token path to use the Brad Gerstner and Gavin Baker parlance is that AI spending is currently flowing into GPUs, memory, networking, hyperscale cloud computing, and frontier model inference. IBM is not a major winner in those categories. So, just to refresh on IBM, because interesting business with a great name, International Business Machines.
Speaker 1:The first business machines they made were punch card systems. They made clocks. Like, it's like you're running a business. You need a machine.
Speaker 2:Need a great
Speaker 1:clock. You're gonna need a clock. No. You really
Speaker 2:Not just any clock.
Speaker 1:Like a clock that works really well.
Speaker 2:That's right.
Speaker 1:Professional clock. Clock Pro Max. You
Speaker 2:on time.
Speaker 1:Exactly. Clock Pro Max.
Speaker 2:It's lighter. It's the lightest, thinnest, best looking, fastest.
Speaker 1:You don't want a fast clock. Tabulating machines, basically a bunch of different ways to process information mechanically. That foundational insight was pretty simple. It was businesses will continually pay forever to automate record keeping. And at a high level, that's sort of been working forever and they're continuously You
Speaker 2:know if they ever tried to sell a clock as a SaaS product, time as a service?
Speaker 1:If you really, really squint Red Hat Kubernetes, it's keeping time between distributed systems, maybe there's something there. But when you're running a database across a bunch of different servers, there's some timekeeping aspect that's important. But no. I don't think they ever did. The IBM that people know, the mainframe business, that started in 1964.
Speaker 1:System three sixty, it was a compatible family of devices, which is interesting. It's not just one people think one mainframe, but it was actually a whole bunch of different systems that you can upgrade piecemeal without redesigning the entire workflow. So you need a little bit more storage, you upgrade that. You need a little bit more compute, you upgrade that. And this turned IBM into the dominant supplier of corporate computing.
Speaker 1:Banks, insurers, airlines, manufacturers, governments, they all used IBM as the central system for their hardware and software. This was the mainframe era. The whole reason that IBM in particular became dominant in mainframes was they focused on high reliability, long customer relationships, expensive switching costs. It's very difficult once you're in the IBM ecosystem to weed your way out. Proprietary software tied to the hardware, certain software, would only run on IBM hardware so you couldn't replatform.
Speaker 1:You had to rip everything out, which is very difficult for a large bank or a large airline in the sixties and seventies.
Speaker 2:Good for business.
Speaker 1:And they also had huge huge support and consulting contracts associated with all the software and the hardware that they were delivering, sort of a precursor to the Ford deployed engineer if you squint a little bit. But the PC era was the real turning point. So the IBM PC launched in 1981. This legitimized the personal computing market and set up two new companies, Intel and Microsoft, to capture immense value during the next computing boom. So the IBM PC ran Windows and used an Intel chip.
Speaker 1:At the time, IBM was doing $30,000,000,000 in revenue. Intel was doing less than $1,000,000,000 and Microsoft was only doing $17,000,000 in sales. And so I think Microsoft had like a 120 employees. And all of a sudden, two companies became ultimately way, way, way bigger, like 10 times as big. So the market eventually fractured and proposals to break IBM into separate companies started to pop up.
Speaker 1:The market fractured because once you had, you know, an Intel chipset and Windows operating system, you could run Windows on a different chipset and you could have a different chipset with different operating system. The value capture piece, there were just other PC manufacturers that came in and then obviously Apple with their, you know, anti IBM like challenge the man campaign. So the market was fracturing and there was a bunch of proposals during the eighties and nineties to break up the company into separate units. Lou Gerstner, who became CEO in 1993, rejected that idea and he said, We do not necessarily need to manufacture every piece of technology. We need to be the company that makes all of it work together.
Speaker 1:So we have to work together. We're going to be the integrator, the systems integrator. His strategy ultimately produced three things: IBM Global Services, large outsourcing contracts, a vast consulting organization, and that's a lot of what we know about IBM today. So services businesses do have limitations though: lower margins, higher headcount, slower organic growth, price competition, etcetera. In 2019, IBM acquired Red Hat for $34,000,000,000 and spun off its traditional managed infrastructure outsourcing business in 2021.
Speaker 1:Today, you can think of IBM as sort of three key businesses. They have software, which is 44% of the business. That's at 80% gross margins. Great business. 31% of their business is consulting.
Speaker 1:That's under 30% gross margins, though. And then 23% of the business is infrastructure, which is just shy of 60% gross margin. And so for the last three years, the stock's been doing really well, up 77% before dividends, and the Red Hat acquisition started paying off. And the z seventeen mainframe cycle was surprisingly solid, but the problem is is that they just called out a shift away from mainframe spending with customers shifting capital spending towards the physical AI build out. Demand for AI and associated hardware is strong, but IBM is losing share of their customers' technology budget.
Speaker 1:IBM still does have a strong asset for the AI era. Red Hat OpenShift, which is their enterprise Kubernetes platform for orchestrating workloads across multiple computers. But there are so many other companies offering AI cap capabilities up and down the stack that they're getting a little hammered today with the the biggest share drop in its one hundred and fifteen year history. Rough day for IBM, but an interesting story nonetheless. Demis Calls Watchdog from DeepMind, the DeepMind chief, he has called for a US led body to test frontier AI models.
Speaker 1:He says society has a precious window to prepare for technology advancing at historic speed. He's a Nobel laureate. The Financial Times has the story, there's an article that he posted on X that will click through and give you the takeaways. From the Financial Times, Google DeepMind chief executive Demis Calls Watchdog Pauses has called for the creation of a US led standards body to test new frontier class AI models for national security threats, arguing that urgent action from international regulators is needed to address the risks posed by rapidly advancing technology. I'm surprised has he never proposed this before?
Speaker 1:This feels like something that has been proposed many, many times, but maybe I'm just misremembering the AI twenty twenty seven people and the AI 2,040 people and the OpenAI white paper and what Anthropics said. Like, it feels like we've seen this before, like we need to have a regulatory body of some sort. All the way going back to when the All In podcast was talking about an FDA for AI back like two or three years ago. But it's now here and it's coming from a DeepMind executive which hits a little harder. The warning from Hassabis, a Nobel laureate who leads Google's AI efforts, follows the White House's abrupt export ban on Anthropic's most advanced models last month alongside a fresh wave of warnings about the potential for AI to disrupt the global economy and financial system.
Speaker 1:We talked a little bit about The Economist. They got to see they got together with a much more moderate proposal, think, because it wasn't actually it wasn't actually calling for any sort of change to the development of AI whatsoever or the rollout. Just saying
Speaker 2:it it could get AI could get better in the next ten years. Yes. Which is a very sort of
Speaker 1:Could get a lot better. That's what they said. Didn't just say that. A lot better. But May.
Speaker 1:But the actual pitch from from the the economists was we need to have economists and government officials think about responses. If there is job displacement from AI, what is the impact? What will the reaction be? So to sort of like prep legislation so you can be more ready when things start to happen, whether that's retraining or stimulus or jobs programs or all sorts of different things. So this intervention from Demis is the most detailed proposal yet for AI regulation from Google, which is vying for AI leadership with Anthropic and OpenAI.
Speaker 1:Quote, we've already seen the challenges frontier models pose for cybersecurity, good point, and other threats, including nuclear and bio risks, may soon emerge as capabilities continue to advance. The rapid progress we're seeing in AI requires a new approach to testing frontier AI model capabilities that is dynamic, adaptable, and rigorous. The US is well positioned, given its economic and technical standing, to take the first step in developing such a framework. My big question is it seems pretty easy to go to the leading labs and say, hey. We you have to go through this process.
Speaker 1:But do we have a good framework in The United States for reviewing code that China just sort of throws over here open sourced? Because I mean, as we've seen with the Kimi k two and GLM, like, if you tie someone up in an FDA like review for even six months, let alone a year, let alone what the FDA timelines are for drug development, five years, ten years sometimes, you are going to have open source models that are way, way more advanced. So Here's the name.
Speaker 2:Yeah. New frontier model
Speaker 1:Yeah.
Speaker 2:Is going through a a six month review Yeah. Let's say. Maybe it's really like a one month review and then there needs to be
Speaker 1:With AI, it could be a two minute review.
Speaker 3:Yeah.
Speaker 1:Who I would hope.
Speaker 2:But let's say it's like a a three month
Speaker 1:Yeah.
Speaker 2:A three month delay or six month delay. Then when it eventually does get released Mhmm. It it gets distilled likely Mhmm. Within even less time than that
Speaker 1:that
Speaker 2:is publicly available.
Speaker 1:Yeah.
Speaker 2:I would like to see So we keep seeing these like letters and proposals. Yeah. And they always come, one, with a request for urgent action.
Speaker 1:Mhmm.
Speaker 2:But they rarely come with super concrete scenarios near term scenarios. I want here's what's gonna happen in six months. Here's what's gonna happen in twelve months.
Speaker 1:Or even just a a like a trigger. Like, it would be if somebody if the unemployment rate goes above 10%, I would recommend a stimulus check of $1,000 be sent to everyone and means tested so it only goes to the middle class and lower class. Like, that is a very reasonable thing. That's basically what happened during COVID. Right?
Speaker 1:Like, the unemployment rate went to 15% and then boom, there were checks in the mail. And that's a very concrete proposal that you could say Yeah. If this happens, then this happens.
Speaker 2:Yeah. I want I want someone like Demis. Yeah. Basically, the the the world of less wrong and AI 2027 and 2040 Yep. They're willing to lay out super Sure.
Speaker 2:Sure. Scenarios. And they they can, at times, come across as Yeah. Very sci fi.
Speaker 1:Yeah.
Speaker 2:Yeah. But there's always, at least so far, been some element of reality in them.
Speaker 1:I hear what you're saying.
Speaker 2:So I I want somebody who's like Yeah. Generally more like kind of moderate Mhmm. To come in and just say like, here's here's a few potential scenarios and this is what I think. Because I don't believe it's, you know, Demis could suggest what he thinks that the government should do. Mhmm.
Speaker 2:The US government, in this case Mhmm. He's, you know, encouraging like Sure. The US watchdog.
Speaker 1:Yeah.
Speaker 2:He's in London though. So I think it's gonna be on our lawmakers and our government to understand in these different scenarios, at least start thinking through in these different scenarios, how would we approach them.
Speaker 1:Mhmm. Mhmm. Yeah. I just I always have a problem with the timelines and predictions because those can be get so nitpicked and they're so hard. I'd be more interested in less of, like No.
Speaker 2:But don't you think that'd be helpful if
Speaker 1:if I don't think it's helpful. No. No. No. I actually don't.
Speaker 1:I think it's much more helpful to say if the unemployment rate goes to 10%, create a new government body that hires people to do something. Like, create the next TSA or I just think or send out similar
Speaker 2:checks or lower interest rates. Right? Retail Washington DC AI models are very good at hacking computer systems. Mhmm. And they're gonna get better at hacking computer systems.
Speaker 2:There's not really much for them to do with that because hacking computer systems are already it's already illegal. Yeah. And the solution there is for companies to beef up their own cyber security, make sure they're using the most advanced models. Yeah. And so if you play out more concrete scenarios where like, here's a timeline for the trucking industry and potential job displacement Mhmm.
Speaker 2:Within trucking or any of these other categories. I just think it allows people in Washington, actual lawmakers, to start thinking about
Speaker 1:I just think that's always wrong. Like, they're always wrong about those predictions. Yeah. But it's so much better to just say, look, if the trucking industry goes through mass job displacement, then here's what I actually propose. Here's the solution.
Speaker 1:As opposed to just saying like, there might be a problem and I think that there's a problem coming down the pipe. I don't know. Like, it's like, what are you actually advocating for other than just being like, the sky might fall. I have a p doom of this number and it's your job to go figure it out. It's like, you're smart.
Speaker 1:What do you suggest? UBI?
Speaker 2:Higher taxes? I just don't think predictions are always wrong. There's been so much so many examples over the last decade where people have gotten predictions like dead on.
Speaker 1:Yeah.
Speaker 2:Situational awareness.
Speaker 1:Tyler, what do think about this?
Speaker 3:Yeah. I mean, I I'm probably in the camp of, like, productive regulation. Like, usually, it, like, has some bad consequences if it doesn't really work out. But also, I was just gonna say, like, what he's describing is basically just Casey, the Center for AIS and Innovation, which is under the commerce department. Yep.
Speaker 3:And it's like a slightly beefed up version because right now Casey is like very much you opt in. Yeah. But it seems like he should just have said, like, you should specifically beef up Casey Yeah. Add add these policies, do these, like, specific things. Yeah.
Speaker 3:And I think that would be much more palatable or or well received or, something like like, what do we actually do with this letter? It's kind of very, you know like, I'm not sure what we actually do with this.
Speaker 1:Yeah. It feels like if I mean, to go back to cybersecurity, it's like if it's a national issue, like the NSA works on this stuff, increase their budget, maybe, raise taxes to increase their budget, issue more debt to to increase their budget. If it can be solved by the private market, it's like go support CrowdStrike or start a new company that can help with cybersecurity. I don't know. The actual concrete recommendation boiled down from what Demis wrote is something along these lines.
Speaker 1:Create a US frontier AI standards body that's like Casey, but probably more beefed up. He's also advocating that it's overseen federally, but funded by AI companies. Define and regularly update benchmarks to determine which models and labs qualify as Frontier. So that's something that doesn't exist yet. Require Frontier Labs to submit models for testing up to thirty days before release.
Speaker 1:That's sort of nice because that would allow someone who's just going and building a recommender system on Netflix that's actually it is using AI technically, but it's not a frontier model because it doesn't qualify for that. So then they can just go and ship the latest recommendation algorithm on Netflix. No big deal. Test models for cyber, biological, nuclear, deception, autonomy, and guardrail passing capabilities require strong cybersecurity personal vetting, model cards, watermarking, and substantial safety research. Use national labs, federal agencies, and independent third party auditors to conduct evaluations.
Speaker 1:Develop independent, confidential tests so labs cannot train specifically against the benchmarks, require labs to fix serious vulnerabilities discovered after release, apply the rules to all frontier models deployed in The United States, including foreign and open source models while exempting smaller models. Okay. So he wants to apply it to foreign open source models. That feels very tricky, but I guess you could get, like, sort of DMCA notices to Hugging Face and GitHub so that it doesn't proliferate across the web.
Speaker 3:But Yeah. I mean, there's probably some power lot to, like, where people are actually downloading and Yeah. Inferencing the model.
Speaker 1:And I guess if you go to all the neo clouds and all the open source folks and you're like, okay. This model is actually a bad model. You gotta go. You gotta stick to GLM 5.2. Yeah.
Speaker 2:It's it's much easier to regulate the compute.
Speaker 1:This is gonna be very controversial to the open source fans.
Speaker 3:Yeah. This is kind of like the George Hoss.
Speaker 1:George Hoss nightmare take.
Speaker 3:Yeah. Attacking every single GPU.
Speaker 1:For sure. For sure. Coordinate a slowdown among frontier labs if testing reveals sufficiently serious risks and turn The US framework into an international system of shared frontier AI standards. Well, I like I like the general direction. I like the idea that he's just he's just sharing his viewpoint more broadly.
Speaker 1:I think all of that is good. I'm not sure that there's there's enough to dig into here exactly how this would like, where the rubber meets the road, how this would be implemented, or what effect this would actually have on the industry. Like, this could be really good for open source because it could just slow down the frontier, closed source labs. It could also be really bad for open source if it's much more cumbersome because an open source project might not have a regulatory budget to actually massage a model through the approval process. Like, there's a reason why small biotech companies get acquired by big pharma before they launch their drugs.
Speaker 1:It's because the big pharma companies have offices in Washington, D. C. And can walk the legislators through So the whole in general, I'm sympathetic to the view where people say, oh, regulation benefits the biggest companies in the world because historically that's how it's played out. Maybe that's different this time. Who knows?
Speaker 1:It could just slow down the frontier. But, you know, he does work for a leading lab. So
Speaker 2:What's going on in New York? I will
Speaker 1:tell you what's going on in New York. Today, New York governor Kathy Hoechel signed an executive order placing a one year pause on new AI data centers in the state. This is the for everyone except
Speaker 2:Americans. People that are
Speaker 1:Probably. Order establishes a moratorium while New York develops a regulatory framework and conducts environmental impact assessments examining data centers, energy demand, water use, water quality, air quality, and effects on the electric grid. You would think there's a decent amount of oversight around those things generally already, like air quality, like whether you start a new barbecue restaurant or a coal plant, you would imagine that there's just a general rule about not polluting the atmosphere that would apply to data centers by default. But it seems like there's a little bit of a special case here and so they're working on this in particular. The move immediately drew criticism from the tech industry, which argues that restricting data center construction will cost local communities jobs and weaken America's position in the global AI race.
Speaker 1:Earlier this year, Maine considered a similar moratorium, but Democratic governor Janet Mills vetoed the proposal after concerns it would block a major data center planned for a town still struggling after the closure of a local paper mill. Hochul's Republican challenger, Bruce Blakeman, also opposes the moratorium, arguing that local governments, not the state, should decide whether to approve projects that promise significant economic benefits. If it stands, the order would make New York the first state to impose a broad moratorium on large scale AI data centers. There hasn't been that much of a data center boom in New York State that I'm aware It'll be interesting to see how they how they define AI data center. Will they do it on energy or what type of GPUs you're racking or what's going But on more to dig in.
Speaker 2:Ken Griffin was on Goldman Sachs' podcast Yeah. The exchange
Speaker 1:Mhmm.
Speaker 2:Exchanges podcast. And it was circulating this week even though it was, I think, recorded last month. And he was talking about how yeah. In his view, what what an error this would be to the data centers are gonna get built. And if they're not built here, that means hundreds of billions of dollars of revenue basically flowing flowing through other countries.
Speaker 1:Right? And No. Other countries. I mean, it'd probably go to other states first. Right?
Speaker 2:Well, he was talking about you know, if if New York does it, there's gonna be a lot of other states.
Speaker 1:Yeah. The meme is like China wins in this scenario. US senator John Yeah.
Speaker 2:This with nuclear. We did this with manufacturing.
Speaker 1:Yeah.
Speaker 2:And I'll agree. We're we're mistakes. Mhmm.
Speaker 1:There's also an article in the Wall Street Journal. Can a prettier data center curb the community backlash? People have been batting this idea around for a while. But let's pull up this image and you tell me, would you be okay with this going into Malibu? The Malibu Compute Company.
Speaker 1:Would you be cool with this If it was puking out diesel fumes twenty four seven? If it was clean and it didn't drive up energy, didn't use any water, it was all closed loop and it looked like this, tolerable.
Speaker 2:Right? I wouldn't just be okay with it being in my town. Oh, demand in my backyard.
Speaker 1:Yes. True Yimbi over here. That's right. In an effort to soothe local opposition, architects plan data centers that resemble tech campuses or art museums rather than bland boxes. You have to imagine that the money that they're spending on the data center for a facade like this has got to be very, very cheap by comparison.
Speaker 1:It looks like Half a
Speaker 2:a percent.
Speaker 1:Less. Less. And all of a sudden, just every time it's screenshotted, like, there was that hot Google presentation where they were in front of those crazy tanks and they put the logo on there and it made it look like they were taking like a like a brewing facility and turning it into a data center. But it was just for the press release. Like, data center was actually somewhere else, but it was just sort of like an odd image.
Speaker 1:Americans are up in arms over data centers. Of course, we know this. They worry how much water these buildings use and fume at the amount of electricity they consume. People hate the way they look too, says The Wall Street Journal. Now a small number of builders are on a mission to ensure that new data centers don't have to be eyesores.
Speaker 1:Gensler, one of the world's largest architecture firms, is leading the charge. It's drawing up plans for data centers that look more like Silicon Valley tech campuses or art museums rather than windowless rectangles that neighbors often grouse about resembling prisons. It's no different than any other building and it doesn't deserve to look any worse than any other building, said Jeffrey Diamond, a design director at Gensler.
Speaker 2:Yeah. See, this is just very rough. Yeah. Not good. That is objectively in the back half
Speaker 1:of that community. Yeah. Yeah. You get you people will push it to the limit unless there's some pushback. But in other aesthetically pleasing AI development news, Clanker Media shared that researchers built a soft floating robot for indoor interaction.
Speaker 1:And for so many of the AI robots, the humanoid robots that we see on the show are Lovecraftian and and horrific. This is so cute. I want one. You want one just floating around answering your questions?
Speaker 2:These have the this has the potential to be a, like, a massive hit consumer product.
Speaker 1:It uses helium and flapping fins instead of propellers. Extremely cute. The result is quiet, lightweight, and safe to touch. It can follow people, give reminders, and act as a study buddy. So you can be studying and this whale can come up next to you and answer your questions about your math homework.
Speaker 2:See, I don't even need it to be smart. No. I just want it to fly around.
Speaker 1:Load it up with GPT two. It's good enough.
Speaker 2:No. Before we jump, I gotta talk about my dear friend Brandon Mhmm. Jacoby who I saw in the chat earlier launched his new studio, a multidisciplinary design practice for those who challenge the boundaries of technology.
Speaker 1:Mhmm.
Speaker 2:He combined a Star Wars intro style Oh, scroll. Video with a barrel, a wave. A barrel. Oh, cool.
Speaker 1:I'm visualizing that.
Speaker 2:I I like think he made this for us.
Speaker 1:Okay. You Do you wanna pull it up?
Speaker 2:Look at this. Wait.
Speaker 1:Motion design. Oh, interesting. Yeah. This is both of us.
Speaker 2:Our interests.
Speaker 1:Yeah. This is perfect.
Speaker 2:He made the launch video for an audience of two.
Speaker 1:For some reason, was I was imagining the the text curling up like a wave and it being sort of hard to read, but this is much better. I love it. Anyway, a good statement. This is a mission statement. This is an essay.
Speaker 2:Worked together for for a few years. Yeah. And he was doing this
Speaker 1:He was one the first personnel news we did on the show.
Speaker 2:We Yeah.
Speaker 1:We we tracked his move to X, the everything else.
Speaker 2:But anyways, he's been doing this kind of work forever. Yeah. He was a design lead at at X as well as Cash App as well as My Last Company. And he's incredibly talented. So he's open open for business.
Speaker 1:Fantastic. Leave us five stars on Apple Podcast and Spotify. Sign up for our newsletter at tbpn.com and we will see you tomorrow. Goodbye.