← Sharp Tech with Ben Thompson
Sharp Tech with Ben Thompson
(Preview) SaaSmageddon and the Future, Microsoft After a Market Correction, Anthropic’s Super Bowl Lies
00:27:38
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- [00:00]
A
Hello, and welcome to a free preview of Sharp Tech. Hello, and welcome back to another episode of Sharp Tech. I'm Andrew Sharp, and on the other line, Ben Thompson. Ben, how you doing?
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B
Irritated. Andrew.
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A
Oh, boy. Why?
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B
Well, you know, I was struggling a little last week with a sore throat. Got pretty sick over the weekend. Had to take a sick day, which I never do. There's so much happening this week. I know. I feel. I feel like there's so much stuff I did cover, and I actually. It's really irritates me. I'm like, I could have really used an extra day of. Of publishing this week and then. But I was feeling better yesterday, doing well, and then today I was coughing again. Just.
- [00:45]
A
Well, here's the thing. You don't sound as bad as you did at the end of last week. Last week, you really did sound like Tone Loke on all your podcasts, and now you just sound a little bit under the weather. So I feel like we're making progress.
- [00:58]
B
Despite back here, this is like when. When. Like a warm stretch in, like, February and then you get, like, below zero in, like. Like March. Totally. It's. It's the return. Even if you know you're always over. Yes. Demoralizing. That's the word. I. Demoralize. That's the word.
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A
Okay, well, that's.
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B
It could be worse.
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A
Okay.
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B
I'm not as demoralized as Sash shareholders, so.
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A
Indeed, there you go. That's where we're going to be for most of the show today. We're going all male on this episode. A lot of different beats to hit. And we're starting with two questions on a company that lost $350 billion in market value last week. You wrote about them on Monday, and Rav says Andrew and Ben took a sick day. Excuse me?
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B
Tuesday.
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A
You wrote about them on Tuesday. Rav says Andrew and Ben. Microsoft got destroyed because the company sucks at AI. Azure is an AI winner, but the rest of the business is a loser. They have had access to all of OpenAI's IP for three years, and yet their own products are atrocious. ChatGPT beats Copilot in enterprise settings. GitHub Copilot was usurped by cursor, wind surf and Now Claude code. GitHub Copilot is stuck in 2024. Dragon Copilot already lost to Game of Thrones.
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B
Now look at this guy.
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A
Yeah, here we go. Now Anthropic is coming for the office suite by hijacking it. This encapsulates the issue facing the entire software complex Today, the leading labs control the neurons, allowing them to dictate the entire AI future product suite through mid training and post training rl. Moreover, their AI products will always be superior to the ones produced by software companies leveraging their APIs. Thus, every software company faces the threat of anthropic or OpenAI hijacking its distribution as they convert the promise of AI into material value. Furthermore, the labs have a structural pricing advantage. Because LLM API calls are the most expensive part of any AI application, you cannot beat a competitor with a 70% lower marginal cost. Microsoft's failure to deliver a single good AI product is the red herring for all software companies. Even if you were handed the underlying LLM, you still wouldn't create a competitive product. If Microsoft can't execute, no one else will. Now, Ben, I read that I have two notes. I think the final line Microsoft is the red herring for all software companies. I think the phrase he wanted there is canary in the coal mine, not red herring. So different animal idiom, but we're good
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B
on the idioms, not so good on the pronunciation. Don't test us on idioms beyond that one.
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A
I found myself nodding my head at basically every other point that was made there. So what do you think? What do you think of where Microsoft sits amidst all this?
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B
Well, I mean, where to start? Do we start with Microsoft? Do we start with the broader sort of ecosystem? I think the there's one interesting line in here he talked about about how the AI companies are always going to be superior and superior on what value vector. This is maybe sort of the single biggest question which sort of gets at what? Why do all these SaaS companies exist? What job do they do in sort of the jobs to be done sense. And if the job to be done is to deliver a compelling AI experience, that's right, they are not going to be as good sort of definitionally than the labs themselves. But if anyone has used enterprise software, the sort of user experience and quality is not necessarily the selling point. And this has been an eternal sort of issue in the enterprise broadly, where everyone is confused at why do all these applications suck? Like, how do these companies persist? What's the bit here? And the answer is often they are solving problems that you don't see, that no one sees that. It's that lack of visibility into the problem that should give you a hint as to why they exist. So maybe there's a particular issue and there's 47 layers of regulation and compliance or whatever it might be. Let's take the most extreme example Healthcare. In healthcare, you have all these. You have things like the HIPAA laws, you have all these sort of like regulations or just let's get into like you're prescribing drugs which have all these interactions with other drugs and all these sorts of things. Xyz and not only hanging over that are massive liability concerns where you need to know for sure who is at fault if something goes wrong. And you have to have all these sort of backstops. And it is a extremely messy, difficult business that you can't afford. Number one, it's very hard to solve. So solving that problem actually takes a lot of work. And you end up with. Because you're having to handle all these variables and all these edge cases, it's like definitionally impossible to have a good user interface to manage this. You end up with like an EPIC installation and doctors having legitimate complaints about filling in 50 gazillion boxes in forms. But every one of those boxes in forms is downstream of someone not being liable or there being some sort of rule or some sort of regulation or some sort of legitimate concern about some interaction effect and all these rules about what could go together and what can't. That sort of has to be managed. And this is a problem in society broadly. People are frustrated at how difficult so many things are. And why do we have all these like layers of stuff when it's like, why can't the doctor just say what's wrong with me? And prescribe something and be done with it? It's like, well, yes, ideally that's what would happen in 98% of cases. The problem is those 2% where stuff can go wrong if something's not caught. Number one, someone could die. And number two, the liability is gonna be off the charts for everyone in that stack that screwed it up. What you're paying for is someone to accept that responsibility to actually go through and get all the this stuff in order.
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A
Solve for the edge cases. Sure.
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B
Right. And the frustrating thing is this probably ends up net worse, right? Having every doctor spend most of their day going through a horrible UI and drowning in paperwork and notes and all these pieces instead of what they got into the business to do, which is to help patients. Don't even get me started. The whole insurance sort of issue, right? Well, actually the whole insurance area. Lego Epic is a great example. They're downstream from Obamacare. Like, they're the ones that like solved all these new issues that were brought on by government regulation and now there's like entrenched in the marketplace and everyone hates Them because they're hard to work with, but they have this dominant sort of position. But the like that is, that is the actual thing that they are superior on.
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A
Right.
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B
And it's frustrating to think about and talk about because they are superior on a vector that everyone hates, but it's the vector that actually drives the bottom line.
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A
They're not solving the top line problem, they're solving problems beneath the surface that people aren't even really concerned about. But they're the only problems, many of
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B
which, many of these problems are driven by an avoidance of risk and all these sorts of issues. So now again, that's sort of an extreme example, but there's all sorts of things that are like this where there's all this sort of muck that, that is just solved by a company understanding the space deeply, going through it, dealing with it. They talk to a CTO or a CIO or a CEO and they're like, look, you can have your employees sort of doing this using other software or Excel or whatever it might be, or you could have this of all these layers of controls and you can feel confident that the muck will be you're not gonna sink the company. Right. Or whatever it might be. Right. Or. And so. And again, this isn't a great outcome. The outcome is you have all these crappy sort of experiences and interfaces and you have the problem that we've talked a lot about on this podcast of what is all the good stuff that doesn't happen when you're dealing with all this crap. Yeah, but the problem is when you're managing a company your most concerned, the crap is visible or the downsides are all visible, the upsides are not and so well.
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A
And dealing with the downsides, dealing with the muck. I misspoke earlier. Epic and companies like Epic, they are solving the top line problem, they're just not doing it particularly efficiently. And everybody who uses them are frustrated. But they're also addressing the mug.
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B
But not doing it efficiently in what way? Right. It all depends on what you like. So everyone in tech. And again, I took an extreme example with healthcare, but there's so many processes. I was talking about this with Benedict Evans, I thought he had a very good framing of this in why do companies have like 400 SaaS, apps or whatever it might be? It's pretty nuts. And the reality is if you have some sort of functionality where it's being done regularly. Here's a good example. I was just having a discussion with Dumond about interview scheduling and in the interview scheduling I interview people in different time zones.
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A
And so how do you put that in the calendar?
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B
How do you put that in the calendar? Exactly. And he was taking an approach that I didn't agree with. And the reason I didn't agree with is because, number one, it's his job to think about that. That's why I'm outsourcing it. But number two, I might change the calendar event sometimes. And by definition, that's like, I have a lot of stuff on my plate, a lot of stuff I'm thinking about. And I want to make sure we have a system that minimizes the possibility that I miss schedule something because it's in the wrong time zone. So in this case, sorry, Dubro, I'm gonna throw you under the bus here. He was setting the time zone based on the guest's location. And I'm like, this is stupid. They are not my podcast. No, no. The point is, is that, like, there's a. Makes a certain sense to it, right? Because then. Because whenever I communicate with people, I'm always communicating in their time zone. Like when I'm texting with them, figuring out a time. I've learned a long time ago, especially when I was in Taiwan. I'm the weirdo here, okay? I'm used to managing time zones. I can deal with it. It was always sort of.
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A
I always appreciated it while you were in Taipei, you know, it was always
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B
a very hairy few situations whenever the time zone changed because Taiwan did not have daylight savings. Diamond does not have daylight savings times, and they do here. And the likelihood of a mistake happening in those few weeks, just very fraught. Like, I'd get anxious about it, but the is.
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A
It's your calendar. So put the time zone that you're using. Is that right?
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B
That was. That was what I said. Just because. And the reason is, is I know this is an issue, so I definitely should. And I usually do pay close attention to the time zone that the event is in, but the highest likelihood of a mistake being made is me. Is like, I. Like, I'm out. I'm at my son's basketball game. Someone says, can we change a thing? I look down, I change the calendar. I don't notice the time zone. Now we're scheduled and it's a big disaster, right? Like, that would happen. Like. So we need to have a process that assumes I am going to screw up and minimizes that issue. Okay? There is eight gazillion processes like this in every enterprise where it's actually pretty clear it's not that hard to do. But what you're concerned about is the one fat finger or the one not paying attention, and it's done. And this was sort of the point Benedict was making is a lot of these buttons or forms in your crappy enterprise application. What they are is capturing. That's like institutionalized process of. This is something that's done repeatedly. So we're going to encode it in code to make sure that the chances of it getting screwed up are very low.
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A
Yeah.
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B
And that is offloaded. The complex, the dealing with that crappy whatever it is is offloaded to the employees who complain about it or whatever it might be. Or you can look at it from the outside, say this, like, why are you paying $50 a seat for this application? You could just do that. It takes three minutes to do it. Right. And it's like, yes, either one's what One screw up costs you a bunch of money.
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A
Yeah.
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B
And so just broadly speaking, this is why I wanted to zoom in on this point in this email, which I think was a good email. But this superior on every vector is superior on every vector 98% of the time, because the LLM just does it better. And look at you have a natural language interface. I could say, oh, change my interview to xyz. And then it's changed. The problem is, if you gain a small amount or save a small amount of money over time, that can be completely undone by one screw, by the
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A
2% which companies are already paying to address with the SaaS that they're already paying for. That. That all makes sense to me. Sure.
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B
So. So this is like. So this is sort of. I wasn't actually sure where we'd go this conversation, but I guess we're starting with a defense of software to a certain extent, particularly in the face of lms, which are amazing and by their very nature. There's a thing I wanted to put in this rundown. I don't know if you added it later.
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A
I did add it.
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B
Circle back to this.
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A
I'm a little skeptical of it. We'll get there in an hour or so.
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B
We'll get there in an hour. Yes. This is professional podcasting. We'll give you a tease for later on in the episode. Make mistakes, they hallucinate. They are probabilistic entities that. The whole point is they're right the vast majority of the time. And because they are sort of loose with it in this probabilistic way, they create this quote unquote, superior experience. But embedded in that superior experience is the possibility of Error. And a huge amount of software is about eliminating the possibility of error.
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A
Fair enough. Well, before we go further down that road with some of the other SaaS companies who are implicated by everything we've seen this week, I do want to focus on Microsoft specifically, because one thing I appreciated about your article earlier in the week was the callback to where we were two and a half years ago with Microsoft looking like a clear winner from the AI era. You had the integrity to cite your own optimism rather than cite one of like a thousand other people who are saying the same thing two and a half years ago.
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B
Look, I don't get to quote myself endlessly when I'm right. If I don't get to quote myself when it's not looking so good, you
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A
have to take the L's sometimes as well. And to me it was just a testament of how quickly all this has moved. Because all those conversations about Microsoft and the boundless optimism feel like they happened five years ago at this point. But in general, I mean, this is the biggest software company on the planet. What should Microsoft be trying to do? Like where should they want to be in 10 years? Are they going to be a platform, something else? What's the roadmap for that company?
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B
Well, so get to Microsoft specifically. I think the other thing I disagree in this email with, in this email is I would have framed it as why do we think Microsoft was going to be good at this when they've sucked at products forever for a long time? Right. It's like, it's like he's expressing surprise
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A
that Microsoft has not been product for 30 years. Let's be very clear about that.
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B
That's exactly right. Which is paradoxically the reason to still be optimistic about Microsoft and sort of assume they're just the next big tech company to go through this cycle. We went through it with Google, went through it with Apple, went through with Meta. Amazon sort of been a low level bit of concern. Although I think there's some aspects of this discussion that are actually good for Amazon, which we can sort of get to in a little bit. And now it's sort of Microsoft's turn to be facing skepticism. And the thing is, what is the implication of software becoming dramatically cheaper to produce? I think for all the defense that I just laid out, which a lot of people have laid out, you don't get to stop there. You don't get to say like nothing's going to change because software is really important. When a fundamental input completely changes, things are going to change. So this is where I went back to content and sort of the Internet, you know, the 90s Internet, you start out, and the Internet seems like it's great for everyone. It's great for the New York Times, it's great for the Washington Post, another entity in the news this week. The Washington Post doesn't just publish now for people in the. What do you guys call it? The DMV.
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A
The DMV, that's right. Yeah.
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B
Hilarious. Hilarious that the Washington, D.C. area calls itself the acronym of what people associate with a horrific experience.
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A
The worst example of bureaucratic excess and largesse.
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B
Absolutely. It cracks me up endlessly. Like, what is it? Dc, Maryland, Virginia? It's not even a good acronym. That's the thing.
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A
I. I'm. It's a sensitive time for residents of the DMV because our team just traded for Trey Young and Anthony Davis and is apparently aiming squarely for the middle over the next three or four years after allegedly tanking to build a championship team. So I can't even mount a spirited defense of the DMV label here, but the Georgetown dmv, the actual dmv, is better than it has been in my entire life. So I'm at least grateful for that. As a decent.
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B
I heard it's great. I heard things like snow removal are excellent, great city services.
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A
So we're down bad right now. It is what it is.
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B
So, yeah, it sounds like your snow removal is being run by the dmv. That's sort of what I understand is going on. Yep. So anyhow, the. The dmv. I forgot what I was doing with this.
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A
The Washington Post.
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B
Yeah, the Washington Post. So your initial take on the Internet is like, wow, the Washington Post doesn't just get to serve the dmv, they can serve the entire country. It's amazing. Our total addressable market just went from a few million. However many people are in the dmv. I've just say the DMV as many times as I can in this segment, but they can actually. Their addressable market is astronomical. It's the whole world. This is great.
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A
Everyone in the world will subscribe.
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B
The problem is that expansion in market applies to every single publication that applies. And most pertinently, it applies to the New York Times. And if you want the story of what happened to the Washington Post, it's that everyone subscribes to the New York Times, like so. You're the second.
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A
Well, in this new expanse, you got
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B
to set a laws.
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A
Yeah, power laws predominate and so people aren't going to subscribe to multiple papers that are not particularly differentiated from one another and pay $20 a month for each one. You're going to choose one subscription. And most of the people who are in that space have chosen the New York Times, which frankly has a more distinct point of view, which probably helps them retain customers.
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B
Well, they've been the big winner of this space generally. We're going to come back to New York Times. I think it's actually a very interesting analogy here. Okay, so, but the point being is what was the input that changed? The input that changed was the cost of distribution. Newspapers actually were light manufacturing companies with the trucking business. They printed newspapers and they delivered them and put them on your doorstep and in a newspaper box, in a stall and all these sorts of things. That was actually their business. That whole thing that they felt constrained them, I can't serve the whole country, was actually what protected them. They had local geography, local monopolies in their geography. Once that went away, once that input went to zero, the cost of distribution you're online, that was actually in the long run completely value destructive. Because now you're competing with everyone. And over time you're not just competing with all other publications. The cost of entry went way down. So suddenly you're competing with me. Like the Washington Post is literally competing with me. Because people can only read one thing at one time. And by the way, if they're subscribing.
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A
Exactly, yeah. I mean they're competing with you on subscription prices as well.
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B
Right, well, but from the eyeball perspective, you're also competing with Facebook and with TikTok and all these sorts of things. And I actually think this point, this is the one point I put in there that's sort of like refuted my thesis, which is I at the beginning was like, look, AI is not replacing software, okay? For all these reasons we sort of talked about. But you go to the content one, it's like actually the content people mostly consume today is all user generated content. It's not professionally produced content. And it. But the thing is it took 30 years. So maybe my whole defensive software will be moot in X number of years because the AI actually will get good enough to do all that sort of stuff. Right? That's very plausible. I think it's going to take longer than people think. But then again, you could say that about lots of stuff that moves very quickly. Anyhow, that aside, well, wait, before we
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A
move on, can I actually read an email that's further down in the rundown here, but is related to this particular point on content. Marshall says one of the best parts of Ben's analysis is its roots in the history of technology. It provides valuable context and helps to ground the discussion in long term durable dynamics as opposed to what ends up being ephemeral. For example, when some bulls were claiming we were all going to buy three peloton bikes during the peak of COVID given this, I'd love to.
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B
I was probably a little too optimistic about peloton. So look, I'm just pushing on my sore spots here, but there you go,
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A
you know, owning the L's along the way. Given this, I'd love for Ben to provide some context on what I perceive as a sea change in the way he seems to be talking slash. Writing about two areas of technology, semiconductors and software. Has AI fundamentally changed characteristics of these industries in a permanent and sustainable way relative to the past few decades? My working hypothesis is that cyclicality will eventually return.
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B
Oh, great job. I saw that word coming. I'm like, man, I'm not, I'm not sure. Cyclicality, there you go, a lot of season there.
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A
It will eventually return to the semiconductor business. Cyclicality and the software business.
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B
Look at you just showing off.
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A
Yeah, there you go. Though diminished from its halcyon days.
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B
Oh, look at that.
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A
Another one will regain its luster at least somewhat. In your recent piece on Microsoft, you compare the impact of Gen AI on software to the impact of the Internet on newspapers. And though the analogy is apt to a degree, I think it may underappreciate the stacked and multifaceted nature of many software moats. While code writing becomes much easier due to Gen AI and I imagine some switching costs get reduced, there are a variety of other moats enjoyed by leading software businesses that the newspapers did not have once their distribution monopolies went away. Is that fair, do you think?
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B
Yeah, no, totally. I was a little hesitant, like I was thinking about the content analogy, like all weekend when I was thinking about thinking about this piece. And I was a little hesitant to go there for this exact reason, because the defensibility of newspapers actually ended up being quite shallow. It really was just geography. And to his point, there's a lot more that goes into software. And so I think that's a valid point. The issue I wanted to push on though, well, number one, I just threw in the user generated content bit. No, I don't think anyone in 1993 fully thought that actually 99% of most people's media consumption, okay, maybe setting aside TV to a certain extent, was going to be user generated content. Like, most people don't read newspapers at all. They don't read books, they don't read magazines. They are watching Instagram. If they're literary, they're reading Twitter and reading stories.
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A
Well, they're watching YouTube. The YouTube views that were reported this week, it was like 200 billion or something like that. Yeah, it's absolutely insane.
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B
No, I keep talking about YouTube's the biggest threat to Netflix, and I think I asked Greg Peters about this. Netflix is being modest. They're only talking about YouTube on TVs. If you talk about YouTube on phones and computers, it's a gazillion times higher. And I'm like. I was actually, like, questioning. I'm like, your situation is much worse. Maybe it's time to, like, be talking about this. Like, the reality of this. So, yes, so anyhow, but. So the implication of this is you might be underestimating AI, its ability to. Why couldn't AI actually discern all those rules and all those issues on the fly?
- [27:03]
A
All right, and that is the end of the free preview. If you'd like to hear more from Ben and I, there are links to subscribe in the show Notes, or you can also go to SharpTech FM. Either option will get you access to a personalized feed that has all the shows we do every week, plus lots more great content from strikeri and the strikerite bundle. Check it out and if you've got feedback, please email us at. Email sharptech fm.