Jerod Santo: Yeah, absolutely. So the background story is that we had a newsletter for many years called Changelog Weekly, which you would subscribe to on Changelog.com/weekly. We also have a nightly newsletter, which is still going, which is an automated scraper of the most starred repos on GitHub, which is increasingly becoming simply AI, crypto, and malware. So it’s getting to be less useful than it used to be… But that one goes out weekly, it went out weekly, and that’s why he named it Weekly. Right, Adam? We had a hard time coming up with a better name for that, because well, we already had Nightly, and Weekly, and… And it was what it was. And then we started the Changelog News podcast as a flavor of the Changelog… Which was quite a success, a hit. People liked it, we like to do it, so we kept doing it, and we decided to streamline the newsletter, and the podcast, send them out at the exact same time… They were already complimentary; let’s just make them more efficiently complimentary. And so Changelog Weekly became Changelog News, which was just both a podcast and a newsletter. People who are listening to this pretty much already know that.

The technical side of that was that the podcast became its own podcast in our system, and had a podcast page just like all the other ones, just like Ship It’s page, just like JS Party’s page, etc. But it had this newsletter component that wasn’t very well featured, basically.

So when you’d go to changelog.com/news, you would find the Changelog News podcast, and there’d be a subscribe button with newslettery things… But we weren’t featuring the newsletter at all. And so this redesign was an effort to have a different page that lives on changelog.com/news, that serves both the podcast and the newsletter better, in terms of people understanding what we’re doing there, being able to quickly read the most recent issue… You know, give it a better splash page. Because lots of people land on that page, and we want them to know what it is. And it’s more than just a podcast. So that’s the backstory.

A couple of shout-outs. Of course, Cody Peterson, who worked with me on the design and the frontend implementation of the new splash page. Shout-out to me for coding it up and opening up an epic PR, which I don’t normally do… And to a handful of folks who decided to send us kind words to use on the page. We have a lot of people who enjoy the podcast, enjoy the newsletter, and they tell us this privately via email and stuff, and so I reached back out to everybody who reads it and said “Hey, if you would give us a quote, we’d like to put some of those on the homepage.” And so shout-outs to Mary, to Justin, to Chris, to Maros, and to [unintelligible 00:17:57.09] whose quotes were awesome. And we got more than those, so thank you to everybody who sent one in. Those were the five that we selected to feature on the homepage. And that’s pretty cool.

[00:18:15.23] Being able to hear from and see some of the people who take value in the newsletter I think is powerful when you’re trying to decide – like, two things you want to know. A, do people like this? And then B, what does it look like? Show me a recent issue. And I think with this redesign that we’ve accomplished that. So that’s that.

Kevin Ball: I saw somebody trying to spin that, like “Oh, generative AI and the crypto wave are made for each other, because of this, that and the other.” No. No, they’re not. No. This is a useful technology that we’re figuring out the use for. That is a technology that people have been trying to figure out a use for for the last 15 years, and consistently failed.

Shawn Wang: That is a short-term business model, yeah. I bet they have much more up their sleeves… I don’t actually know. But they did just hire their first developer advocate, which is interesting, because I think you’ll start to hear a lot more from them.

[58:12] Well, there’s two things I will offer for you. One, it’s a very common view or perception that AI is a centralizing force, right? Which is, Adam, what you’re talking about, which is, “Does this just mean that the big always get bigger?” Because the big have the scale and size and data advantage. And one of the more interesting blog posts - sorry, I can’t remember who I read this from - was that actually one of the lessons from this year is that it’s not necessarily true, because AI might be a more decentralized force, because it’s more amenable to open source… And crypto, instead of being decentralized, turned out to be more centralized than people thought.

So the two directions of centralized versus decentralized - the common perception is that AI is very centralized, and crypto very decentralized. The reality was that it’s actually the opposite, which is fascinating to me as a thesis. Like, is that the end game, that AI eventually gets more decentralized, because people want this so badly that there are enough researchers who go to NeurIPS to present their research papers and tweet out all this stuff, that diffuses these techniques all over the place? And we’re seeing that happen, helped in large probably by Stability AI. The proof that Stability as an independent, outsider company, like not a ton of connections in the AI field, did this humongous achievement I think is just a remarkable encouragement that anyone could do it… And that’s a really encouraging thing for those people who are not FAANG and trying to make some extra headroom in this world. So that’s one way to think about the future.

The second way to think about who monetizes and who makes the billion dollars on this… There’s a very influential post that I was introduced to recently from Union Square Ventures, called “The myth of the infrastructure phase”, which is directly tackling this concept that everyone says “When you have a gold rush, sell picks and shovels”, right? And it’s a very common thing, and presumably AI being the gold rush right now, you should sell picks and shovels, which is you should build AI infrastructure companies. But really, there are tons of AI infrastructure companies right now, they’re a dime a dozen; really, they’re all looking for use cases, and basically, the argument, the myth of the infrastructure phase is that technology swings back and forth between app constraint and infra constraint. And right now, we’re not infrastructure-constrained, we’re app-constrained. And really, it’s the builders of AI-enabled products like TikTok that know what to do with the AI infrastructure that can win.

So I’ll introduce those concepts to you… I don’t know what specifically that means. I’m looking out for opportunities like that myself. But I think it’s apps; I think it may not be infra, because it’s going to trend very, very hard towards commodity infrastructure provisioning, and I don’t really care where I get my A100s from… A100 being the predominant NVIDIA chip that is being used to train all these models… Which, by the way, costs $200,000 per chip, which is insane. Imagine a GPU costing that much.

Jacob DePriest: Well, you know, it turns out that threat actors also have figured that out. And when they hear things like free compute, they go right after it for – pick your abuse vector. We see campaigns that will try and escalate the number of stars that a particular repo has to increase its popularity. We see people trying to mine crypto using free compute. I mean, we see a lot. Hosting files on github.com that we’ll just say don’t have anything to do with software development. And so it’s a challenge. And it’s something that we have a fantastic team working on, we have been employing machine learning and AI for a while in this space, we’ll continue to do that… But it’s a really complex, challenging problem. And the balance we have to strike here is because we serve so much of the world’s software development ecosystem, we can’t turn the dials too strict, because then we start locking out hundreds, thousands of users that have legitimate use cases [unintelligible 01:08:03.10] And certainly, those things happen. You can’t fine-tune every filter to be perfect. But we really try and strike that balance of how we do that.

[01:08:12.15] So this is where we’re also working with our product counterparts to understand what are things we can make changes to in the actual product, or maybe a signup flow or things like that, that will decrease the likelihood of abuse, or impose costs a bit higher to actors who want to do that… And so that’s certainly something – there’s new campaigns every day that come out in this space that our teams are firefighting, and they’re doing a great job at it, and it’s something that’s just top of mind, because it’s something we don’t see getting less of, we’re definitely seeing an increase.

Sameer Ajmani: So I’ll ground this in just what are the opportunities for the existing house, versus the other opportunities. Let’s look at cloud, for example. Go is a fantastic language for building cloud applications, multi-threaded servers, doing a lot of communication. All the estimates I hear of point at the majority of workloads that could be on the cloud still aren’t. So there’s a huge still future for building scalable software and infrastructure that needs to be reliable and production ready and efficient, and all the things that Go is. So I think our existing sort of base for Go is still really important, and it still requires a lot of investment to do really well.

At the same time, as Cameron pointed out, software engineering and software development is transforming. And we need to pay attention to how that is changing. So I don’t think we know – I remember seeing in your notes, you were asking like “Well, what about ML? What about frontends?” We even looked at Go mobile years ago… We try not to chase next new opportunities just based on what’s hot. We try to be selective. For example, when it was all the crypto and mining stuff, some of the crypto people adopted Go, and people at Google asked “Oh, we see [unintelligible 00:54:56.19] and we basically said “No, we’re good. We don’t need to chase that ball.” But with supply chain security we saw the opportunity to do something that was truly differentiated and was well-aligned with what our existing users would want.

And so with AI - again, we’re not trying to chase the data scientists and the Python users who are iterating on model development. What we’re looking at are people who build in production at scale - they’re going to have to deal with AI, and deal with incorporating AI into their existing systems, and they’re going to deal with models and security and observability. So how do we serve them? So there’s a way to take our existing user base, with their existing concerns, and also think “Well, how are their needs expanding? How is the Go user’s job going to change?” And so it’s both building systems that use models, and also using AI themselves to generate Go code, or understand their Go programs. So I guess that’s the way I think about how our jobs change, is if you are an average Go user, how is your world changing, and how can we, the Go team, facilitate that change?

Chris Benson: [04:30] Yeah… Although, before we kind of move fully over to the AI side from the crypto side, I happen to be staring at Sam Bankman-Fried’s Wikipedia page, and I’m looking at his hair… And as you mentioned the barber and stuff, there’s gotta be a joke there. That’s all I’m saying.

Kevin Ball: I do wonder, coming back to the generative AI thing, if part of the challenge right now - and this is coming in from the chat, but the challenge right now is sort of this assumption pushed by people who have a lot of investment in AI, that AI can actually replace people coding. And they’re pushing – you have all these studies, “Oh, it’s improving your productivity, it’s doing all this”, and so everybody thinks “Oh, I’ve got to do this or I’m falling behind.” And as we were highlighting, for some problems it’s a big help, and for other problems, it’s crap. And it’s not helping you get more done, and at the same time, it’s inhibiting your ability to learn.

So I think some of this is pushed by people like the Sam Altmans of the world, who - they have an agenda. And it’s very clear that they have an agenda, but they’re kind of taking this… It’s like the NFT boom. “This is gonna replace all these things. This is gonna be doing these things.” Well, there’s more value in AI than I think there was in crypto, but there’s also a whole lot of junk.

Victor Dibia: [03:57] My background is a mix of computer science - software engineering - and a bit of human-computer interaction, and more recently applied artificial intelligence. I have a masters degree from Carnegie Mellon University that’s focused on software engineering, some management courses… And as part of that I did this thesis in building a web-based public key crypto system that was introduced by my advisor. That was a nice experience, it’s just that there was a lot of time spent implementing cryptographic functions in Java. After that I felt it would be nice to move away from just the technical aspects of computer science, and also look at some of the human aspects of computer science.

Right after that, I made an interesting choice… I moved to Africa, Lagos, Nigeria to be specific, and I started a company focused on making software, focused on the African market. I did that for about a year, and as a part of that I also taught at the university in Lagos, Nigeria, through some project that was co-sponsored by MIT and Google.

At that time I figured out I’m really interesting in human aspects of computer science, and I had all this experience building software tools within the framework of a startup, and also teaching at a university… And then I decided to do a Ph.D. My Ph.D. was in information systems. I did it at the City University of Hong Kong. The main focus was quantitative user behavior studies.

At some point during my Ph.D. I had the opportunity to do an internship at IBM Research, and that’s where I got into AI. So I interned with a group called the cognitive environmental lab at IBM. Most of what that group did was trying to figure out good user experiences for applied machine learning. So we spent time taking APIs built by other research groups - APIs around speech-to-text, text-to-speech and computer vision, and our goal was to use these tools and build them into interactive, in some cases [unintelligible 00:06:20.13] experiences.

That’s where all of my interests with AI started. I started out applying models, and after a while I spent time implementing some of these models in TensorFlow and Keras, and essentially made the transition to start applying some of these custom-built models in new problem spaces.

I was at IMB Research as a post-doc, and then a research scientist, and earlier this year I joined Cloudera’s Fast Forward Labs as a research scientist. It’s a bit of a journey, but this is kind of how it all went down.

Adam Stacoviak: These projects are not many businesses, what you’re saying. You don’t think that these open source projects should transcend from communities to many businesses that aren’t really businesses. Not necessarily disagreeing with that either, by the way. So the trend line, I think, for capitalism right now is it’s becoming more and more pervasive. Fractal. And there are better tools and representations of value - one of which is crypto, by the way - that will allow people to participate in capitalism more easily, in a more fractional way in the future. You can come up with some buzzword for all that. I don’t know what that would be. I do not think philanthropy, donations, chipping something into a hat is sustainable. Nope. That is not sustainable, because it is not a system that creates more inputs relative to the outputs. It’s not.

[01:42:17.21] So your example - the way I would describe your example is this person is respected by a community, they raised their hand, and the community responded and said “Look, you asked for this. We’re going to chip in. There’s what you asked for.” Great. That is not sustainable, though. It’s like a one-off activity. How many more times could that individual make such a request? I mean, I guess it would depend on the value of what they’ve done before… Things are going to get, I think, pretty weird in the future as these AI systems can produce very valuable technologies… And so how do you actually sustain the notion of value, and then connect a currency to that? I think that we’re careening - not gradually, we are careening towards a world of completely repricing and reshaping the way value is represented, both in terms of basic standard goods and services in the economy, like bananas and bread, to digital products, and also the price of advertising, which is like the predominant business model of the internet still today.

So I try to remain humble about – like, I have no freaking clue how these things are going to get solved… But I deeply believe capitalism is a very sustainable thing. And you want to create a continuous stream of revenue and income, and have your cost structure and your spend be less than that. To me, that’s called sustainability. There are many other – I’m just like a business person, sort of, so I kind of use the business example… There’s many other examples that you can use… Like in physics, and systems that have inputs and outputs; the same principle applies. To me, that’s how I think about sustainability.

V Körbes: Yeah, I mean, all the AI stuff, like the GPU stuff… Do we have that out of the box in Go? You’re gonna have to go hunt down your favorite library and waste a week trying to figure out which one’s the best. If we’re talking about, for example – you need some distributed stuff, like maybe an application running on a billion different places at once, then you’re going to need all the access control, you’re going to need all the encryption stuff… Let’s say you’re in the government, you’re going to need your crypto libraries to be compliant with for example FIPS, and a bunch of regulations like that… You don’t have none of that stuff out of the box. So I mean, I think honestly Go is just a really bad language in general, but especially for prototyping.

Ken Kantzer: Yeah. No, in some ways, hashing is like a great use case for AI, because you can generate inputs, right? A lot of times, coming up with effective machine learning requires you to have a very large sample size, and you can’t like just make it up. But in crypto, it turns out you can. It’s called the oracle, right? I ask the MD5 oracle, and I get a response. I can do that a million times, and then hand the output to a machine learning algorithm, and say “Hey, are you able to correlate one to the other?”