Episode Summary
Episode Video
Episode Show Notes & Transcript
Show Highlights:
- (0:00) Intro to episode
- (0:41) Backblaze sponsor read
- (1:08) The origins of Nikhil’s viral article
- (4:20) The disconnect between buzzwords and work
- (5:26) Throwing money at AI
- (7:17) AI vs. craftsmanship
- (13:36) The rush to get AI tools out the door
- (16:12) The telltale signs of bad AI content
- (18:50) AI, crypto, and GPU grifts
- (20:33) The fallout of Nikhil’s blog post
- (22:34) Firefly sponsor read
- (23:10) The practicality of GenAI
- (26:24) GenAI presentations vs. reality
- (29:07) Predatory hiring practices and tech’s current barrier for entry
- (32:03) Sturgeon’s Law in the industry
- (35:22) Consequences of the hype cycle
- (38:48) The fantasy land of “conferenceware”
- (42:01) Where you can find Nikhil
- Backblaze: https://www.backblaze.com/
- Firefly: https://www.firefly.ai/
Transcript
Nikhil Suresh: [00:00:00] You know, every week there's some new thing about how all writers are gonna lose their jobs, all artists are gonna lose their jobs, you're gonna be out the streets unless you learn how to program. They tried to terrorize programmers, but it didn't work because we knew what we were talking about.
Corey Quinn: Welcome to Screaming in the Cloud. I'm Corey Quinn, and I am thrilled to be able to have the chance to talk today with someone who took a, what I found to be a very fair and reasoned approach to the ongoing zeitgeist fixation on AI. Nick Suresh is a director at Hermit Tech. Nick, thank you for joining.
Nikhil Suresh: Very happy to be here. Thanks for having me on.
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And you are the author of the very even handedly titled blog post that came out a couple of weeks before this recording, titled, I Will F ing Pile Drive You If You Mention AI Again. That is just a chef's kiss, beautiful title, well done, even if this goes no further, thank you for that title, it absolutely made my week.
Nikhil Suresh: Thank you, thank you, I was so calm while writing it, um, I had no pulse, my heart was not beating.
Corey Quinn: What I love every time someone writes an incendiary topic like that, where there's profanity in it, and I put it in the newsletter that I send out to 32, 000 people every Monday, and I love the bounces I get where you're, the chiding ones of like, the mail filter rejected your email because you used unkind language, yada, yada, yada.
It's like, great, this is a terrific list of companies that I never would want to work at, because, you know, let's treat people like adults. Ignoring [00:02:00] the stylistic aspect of it for a second, can you describe the basic, where did this blog post come from? What inspired you to put digital quill to ink, or digital ink, ah, digital quill to parchment, and pen this amazingly well drafted screed?
Nikhil Suresh: Well, first, thanks for that. And yeah, so I only entered the tech market around 2019, coming out of a data science degree from a big university here. And, uh, back then, GPT didn't exist. But at that point, you still couldn't really do any AI work, right? It's just hundreds of managers who had no idea what they were talking about, barely knew how to operate a computer.
We'd just go on and on about it. They'd hire people, you'd join the company, and there'd be no work for you to do. They had no clue what they were talking about.
Corey Quinn: It's almost like quantum computing, where the Hello World tutorial is go and get a PhD from Berkeley or equivalent, and then come back, and we'll go on to step two.
Nikhil Suresh: That's exactly right. They also talk about quantum. I went to a conference last year in Queensland, something digital. Half the talks were on quantum, just quantum, they just say quantum, whatever that means. And there's [00:03:00] no way the audience had the credentials to know what that means, because I don't, and I was more qualified than them.
Corey Quinn: It feels like it's an episode of Star Trek Technobabble come to life when a lot of these people give conference talks about this, like, okay, great, the only people that can really say yay or nay basically fit around a diner table at Denny's, and that's great, but you're giving this talk to 10, 000 people, we're just all going to smile, nod, and wait for it to get back to something relevant to our experience.
Nikhil Suresh: Yeah, it was, it was fascinating. And you know, so that was 2019 though, when I mentioned, uh, you can really do any serious AI work. And then I just left the field. I was like, there's, I'm not going to have a job in two years. And around the time GPT 3 was coming out, the jobs that actually were disappearing here in Melbourne.
And now they've just exploded again. People have no idea, like how they're going to get value out of this technology in any way. They talk about it obsessively. I got a call from one or two execs here in Australia who somehow read that post. And didn't realize I'm their natural predator. And they were like, come on my podcast, come on my podcast.
And when I asked them, you know, what they do at work, [00:04:00] they always described their technology stack as Gen AI and other stuff. They can never talk about the part that needs them to understand any math. And yeah, finally, someone sent me that a scale survey, which appears in the thing, which says, uh, 8 percent of companies have not seen positive gains from Gen AI.
And something in my brain broke. I just, I just started writing 10 seconds after getting that.
Corey Quinn: It's a wild statistic because it's, it also, it doesn't necessarily measure truth. It measures who is willing to go on record saying that, oh, this thing that everyone is convinced is a savior, we're going to tell you that we're not seeing business improvement from it.
It's similar to surveys that show that overwhelming percentages of CISOs rank security as their top priority, but Where companies spend put the lie to that, because no one is going to answer a survey, even if it's anonymous, with, Nah, we don't actually give a crap about this until a regulator makes us care about it.
It feels like it's the right answer, but there's a definite divergence between what people say they're getting value from and what they're actually doing. [00:05:00]
Nikhil Suresh: Yeah, I wrote a blog post that went pretty viral last year, which was about this, right? Companies say they have values they don't hold, and you actually look at their behavior.
It's a huge cause of workplace burnout. People come in and they're like, Hey, you know, we said we care about this quality. So I'm going to put that quality in, you know, and that's like a real human cost to companies just lying to employees and assuming that the employee knows it's all some sort of fiction.
Like it, it is not obvious to everyone.
Corey Quinn: There's been an attention gold rush towards Gen AI. People are hurling money at it across the board. But what I found in my own experiments with it is that it's very good at a topical surface level of bullshit. And when you start digging into it on any area, you know, well, It immediately falls apart because it's not actually reasoning despite how it appears, but a lot of the world does, in fact, run on bullshit.
I found it incredibly useful, for example, to take a very terse, uh, email of please get the thing done, and then turn that into something that people will receive and [00:06:00] not be convinced that I'm a raging asshole. When they, when they read that, like, oh, there's a period at the end of the one sentence, oh, he must hate me.
No, it's put this into business context for me. It's useful for things like that. Don't get me wrong. But the unspoken message in so much of the Gen AI boosterism is soon you'll be able to fire half those useless bastards hanging out in your company's customer service team and replace them with a chatbot.
Uh, press X to doubt.
Nikhil Suresh: I appreciate the press X to doubt reference. And yeah, like, it's so interesting because when I, I listened to a podcast from an exec who reached out and they seemed nice enough, I was listening to it. And they work with an engineering team. So they go, Oh, of course, you'll never replace the programmers.
You know, of course, you can't replace, uh, programmers, chat, GPT. They were just signaling how human aligned they were. And then one sentence later, they come out with. But you're going to replace all your cheap customer support people, right? You know, those guys, they're expensive. Get them out of here. Bring ChatGPT in.
And it was just so interesting to see how [00:07:00] quickly this person was flipping somewhat ignorantly between like signaling human alignment and then this compute, a complete kind of HR, all humans are fungible cogs, get them out of here. And the funniest thing is, like, it's not going to work for either of those, so I don't know why they published that episode.
Corey Quinn: Well, there's some aspect of it, too, where, well, it just gets rid of, like, the junior level work, like, you still need senior engineers to do things, like, that's great, where do you think senior engineers come from? Do they spring fully formed from the forehead of some god? Generally not. We learn by doing a lot of those junior tasks.
Now, great, AI is in fact better and faster than we are at copying and pasting code without context out of Stack Overflow. It's very good at this, but as soon as you start digging into why are you doing it this way or building an app this way with a bunch of different snippets that turn into wild spaghetti code because nothing has a context window to understand the entire app, it's pretty clear that no, no, this is just a bunch of stuff being glued together and maybe it works for an [00:08:00] MVP.
But this is not artistry, this is not, this is not a well crafted solution, this is brute force mixed with enthusiasm, which are my two favorite programming languages, but it doesn't, it doesn't have a soul to it, if that makes sense.
Nikhil Suresh: No, that does make sense, right? There's no element of craftsmanship to it.
And, you know, I don't want to talk about just craftsmanship because you can automate a bunch of things, right? Like this chair I'm on was mass produced in Ikea. There was no hand craftsmanship. But there's no development of judgment in deploying LLMs on these topics. And that's the thing that makes you a professional.
So if someone's relying on ChatGPD to do stuff, for one thing, it can't. I tried to get it to Hello World in Elixir a while ago, and it's fine at Python, but it can't. It, like, it doesn't know anything about Elixir. It's just so bad at writing. This thing wouldn't run. Uh, which, you know, kind of shows you the man behind the curtain.
It's not as smart as it might initially appear. But also You know, when junior friends I have start trying to use it, they're like, I'll get into programming. They very quickly [00:09:00] find that they're not getting smarter while they're using it. It might accelerate, you know, one or two kind of small bits that didn't matter that much anyway.
And then, you know, it constrains their brain in weird ways. Because it's very hard to learn how to write good tests in programming. It writes your tests for you. It doesn't write them very well, but then you stop thinking about it. It's very dangerous to use as a junior, I think.
Corey Quinn: You have to know the rules before you understand when it's okay to break them, as I think is part of the approach on this.
But, but I want to be clear on this, that I have been accused in the past, when I have said, when I have pushed back on AI, that, oh, I'm basically being defensive because I think that it's coming for my job, and that's going to basically erode everything that makes me me. I find that line of questioning to be more than a little offensive, just from the perspective of, my value is not the sheer number of words I can bang out in a short period of time, it's the insight and thought that goes behind it.
That's the reason people, presumably, listen to me. Either that or they don't know how to click the unfollow button on Twitter. Six of one, half a [00:10:00] dozen of the other. It's, it's not about getting words out quickly that sound vaguely good, it's about building a story. It's about understanding who the audience is and what they need to hear.
And I worry that a lot of this slop is going to just flood the zone with basically dangerous stuff, if you let it.
Nikhil Suresh: It absolutely is, and it's funny because I've heard that line of, you know, people go, Oh, you're worried about your job, that's why you're saying this. I'm not worried about my job. I am worried about the jobs of people who come out and say things like, Oh, it's so much better at writing than me.
You know, like, it's such an incredible cell phone. Anyone with a modicum of talent in almost any field looks at it and goes like, This isn't as good as getting a professional, right? If I was writing a book, I would not use AI to make the cover art. I wouldn't use AI for any serious writing. I wouldn't use it for programming.
And all these execs coming out like it's so good. I'm just like, come on, you're just, you're embarrassing yourself in public.
Corey Quinn: It's been memory hole, but one of the big consultancies came out with a statement that said they were going to be [00:11:00] using generative AI to crash, to craft their business strategy for the coming three years.
And it was, what? You're basically having a sarcastic parrot do this to you? Do you mean stochastic parrot? No, I absolutely do not. If you prompt it correctly, it's a very sarcastic parrot, and that's kind of the point of it. Like, it's just empty words that act, that go well together on a predictive algorithm when you do vector math on it.
That's not, that is not the stuff from which good strategy springs. And if it is Maybe your job is nonsense.
Nikhil Suresh: Yeah, and you know, what it writes is so predictable almost all the time. And it does have like very weird use cases that humans are quite good at. Something it's great for is you describe a problem, it's very good at describing technology you should go Google.
Because it turns out Googling tech is kind of weird. Companies have names like Stripe. How would you know that's what you need to look up to get to what you're looking for? But outside of that, I, you know, like, how is it going to help you with strategy? If someone at, you know, we have, we have a flat structure.
None of us at any point have been like, we'll [00:12:00] use chat GPT to do our strategy. Not because we're opposed to it. We like winning. And we, like, that's a path to lose it. It didn't even come up in discussion.
Corey Quinn: I do see value for it for things that are, honestly, bullshit type jobs, whereas, great, we need a 400 slide PowerPoint deck that no human will ever go through, but we need that artifact to sit there and check a box somewhere.
Okay, great. Use it for stuff like that. Personally, I love using it for in ways that I don't think that they quite expect me to use it in because it turns out that you can bring creativity to prompting. Uh, I just received an email that I think is kind of inane. Great. Respond to this email with either overwhelming enthusiasm or withering sarcasm, but is impossible to determine which it is.
And sometimes it is just spectacularly on with prompts like that because I'm not going to bother to write a five paragraph email thanking you for your invitation to some gen AI nonsense. Let the robot do it.
Nikhil Suresh: It's difficult, you know, I'm looking for a [00:13:00] point here that's maybe less obvious to the audience, because I suspect people who listen to this already have the same view here.
So maybe the interesting thing to comment on that is not that it's obviously bullshit, but we need to address the fact that a large number of people running the industry have not developed personal judgment. You know, they can't make that determination. It is, it does not look like bullshit to them. It writes those sentences.
They've somehow become CEOs and that's how they think, right? So they look at that and they don't think. What they should be thinking is, Wow, I've been spouting bullshit for 20 years, and that's why this looks good. But they haven't connected those dots.
Corey Quinn: They also put two and two together. I mean, Amazon recently launched its Rufus, uh, AI assistant in the iOS app.
So when I encountered that, Okay, I, don't try to out stupid me. I'll play those games. And they, of course, do their best that they can to defang the thing so it doesn't make them look bad. But, you know, if you have enough creativity to bang two neurons together and make a spark, it's not hard. Write a limerick about this product.
Easy enough to do. Great. And it [00:14:00] did, and it spat out a limerick where the last line didn't rhyme, because why wouldn't it? What really is a limerick talking about how much it enjoyed riding a dildo? Because that's right, Amazon is also the world's largest dildo emporium. People forget that. I call them the underpants store, but that's really out of respect, because I couldn't call them the many, many things they would have deeper problems with.
But you can't, on the one hand, sell things like that, and then act shocked when your AI robot on your website spits out commentary about that thing. But companies are rushing to stuff these things directly in line with customers and having them say things that are never reviewed by a human being before they're out there representing your company.
And I don't understand that. If a human were to say even half of these things, they'd be fired on the spot. And yet here we are.
Nikhil Suresh: Yeah. And the rush is, I have to assume it's not related to gen AI in particular. It has some interesting characteristics that are good for grifters, right?. A comment I made, um, on Better Offline was that if you look at [00:15:00] rolling out a crypto app or something, I don't like that space, but you actually do need to know how to code to like do something in that space.
If you look at serious engineering companies in Melbourne, crypto companies are overrepresented because you can scam a lot of people, but you need to engineer to do the scam. If you look at the AI space, I think a lot of people don't realise, like especially non technical executives. They just have this class of person that is rolling out, like, really basic Django web apps.
And the AI component is just someone typing in import open AI, and then whatever, you know, string you possibly
Corey Quinn: It's a very thin shell script wrapped around a call to their API, but sure, that's enough if you tell a good story to raise 4 million.
Nikhil Suresh: Yeah, I'm not even thinking about, like, VC money. I'm thinking, like, you know, big institutions here in Melbourne.
Um, that they're not even making money off of this. They're not getting good valuations or anything. But they do this anyway, and it's not even because of this grand institutional plan. It's because, like, this individual grifter class that is just, you know, infiltrates every big organization. Everyone's [00:16:00] seen it.
They just convince non technicians that they're as good as open AI. Because it's not obvious. It looks like you've built the thing that the specialist team in the US built. But you've just got two lines of code in there.
Corey Quinn: It feels like it hits differently in different arenas. Whenever you have the chatbot, or you just generate a blog post or whatnot, it always feels like it's making the fundamental attribution error here.
That you don't care enough to write it, but somehow magically, through the power of internet and AI, People will give enough of a shit to read. Anything that you shove out from this thing. I, I think that is, that is a mistake. I think that people are going to learn to tune it out extraordinarily quickly.
And when I'm gathering new, when I'm gathering articles that I'm debate, I am considering, do I put this into the newsletter on Monday? I, I don't know if it's that I'm good at spotting AI writing or. If it's just that I have a very low tolerance for bad writing, but either way there's so much stuff that I see that I don't know if a human [00:17:00] wrote it or not, but either way it's crap, so we're not going to be including it.
And I can pick that up extraordinarily quickly as I read it when, you know, there's three logic errors and two misspellings in the first sentence.
Nikhil Suresh: It's, it's fascinating from a, uh, when you're looking at spotting AI writing, I try to be sensitive to the fact that, like, I don't know how many have slipped past me, but I'll, I'll just say that.
At this point, I would have expected someone to have pointed it out, right? Like generate something. Buy AI, save the proof somewhere that you did it by AI and see if you can slip it past humans. And then when it gets past them, just point it out, right? Like, that seems like a pretty easy column to run to get clicks and no one's done it yet.
Corey Quinn: And there are valid use cases for this. For example, I've just written a blog post in English, which is not my first language. Can you edit and improve this? Great, that's a great use of it. But you're a fool if you don't read the thing that it spits out before slapping it into Medium and hitting the Publish button.
Nikhil Suresh: Yeah, and that it's actually a problem with kind of older, less, it was actually still [00:18:00] super hyped at the time, but less hype AI stuff, more classical statistics, which was, it was really easy to hit accuracy levels that felt pretty good, but weren't suitable for the business, right? It's why I can't just take Gen AI or something older and start automating away lawyers, I might even get a pretty good hit rate on normal boilerplate stuff.
If it's like that, contact them constantly, but you could never, ever put it out without having a lawyer look at it. So you haven't actually saved very much labor, possibly none because reviewing stuff is kind of, you get really paranoid. You're like, was it, did I have enough coffee before I read it? Do I need to go through it again?
Like, is this going to craft fraud? It's, it's just the same issue as before, which is like a bunch of almost working demos. It's now easier to get to a working demo. And then the pathway to revenue, I just don't see what it is for 99 percent of these applications.
Corey Quinn: No, it feels like it's hype chasing. It, it, you, we, you talked earlier about, uh, cryptocurrency being a terrific way to scam people.
It feels like some of those exact same people have pivoted to Gen AI and it's, [00:19:00] what is the affinity between these two things? And then it occurred to me, these people are clearly Nvidia's street team. They don't care what you're using GPUs for just so long as you're buying more of them so that they can get their commission or see the stock bump or whatnot.
I'm only half kidding when I say that, because it does feel like there's an awful lot of folks Who have this insane urge to push whatever it is, hard math that demands giant farms of GPUs.
Nikhil Suresh: There's something interesting with them because it's hard to tell which ones are grifters because there are some grifters and there are also some people who have just become so credulous and excitable over their career.
They've been elevated because that brings them a certain amount of energy in social settings. And when you look at someone individually, it's so hard to tell who is like, they actually doubt the tech and they're just here for the money. And how many other people have just been swept away. I know a lot of salespeople like this who, when crypto really started taking off, they used to sell other stuff.
They all sell cryptocurrency in my home country, Malaysia now. And I think [00:20:00] they have, they have made themselves true believers because they want it to be true that there's this thing they don't need to study for or learn anything in, and they can just print money and still be a good person. Um, obviously they can't, that's, that's not how the world works.
Corey Quinn: Hope clouds observation.
Nikhil Suresh: Oh, I'm, I'm, I'm unaware of that, but that's, uh.
Corey Quinn: No, it's a hard problem.
Nikhil Suresh: Oh, sorry, sorry, I, I thought you were talking about someone, uh, called Hope Clouds, uh.
Corey Quinn: Oh, no, no, no, no, no, the, just the idea of you want it to be true so that clears your ability to be objective, and it's, it's a, it's a complicated problem, and I do feel for a lot of these people.
I am curious, I know that whenever I write a blog post that has a certain virality level to it, it. breaks containment and goes outside of the people I generally hear from about these things, and I start getting some responses from folks I would never have expected to hear from. Uh, which is a polite way in some cases of saying complete wackadoos, but okay, great.
This is not the typical, the typical demographic I envision writing for, the typical audience I wind up seeing. [00:21:00] by writing targeted for. I have to imagine you got some element of that, just given the clear overwhelming popularity of, for about 24 hours, you could not go on the internet without encountering your post.
Nikhil Suresh: That did happen. I was very surprised to see it broke out of the IT circle. There's a lot of programmer specific jokes in there. You know, very early on, I make a joke about Postgres. Non technicians don't know what Postgres is. You know, this was, I did not write it to maximize morality. But basically what I tapped into and I was quite upset to find was we had a lot of non technicians writing it.
So writers, artists, uh, people who are just like near grifters and, and didn't really have the credentials that they knew was kind of bullshit, but they didn't have the ability to definitively call it out because they don't program themselves. And, um, it really made me aware of this massive human cost, you know, psychologically for the past one or two years, you have had, uh, these kind of complicit rogues running companies who have been Just terrorizing people.
You know, every week there's some new [00:22:00] thing about how all writers are going to lose their jobs. All artists are going to lose their jobs. You're going to be out in the streets unless you learn how to program. They tried to terrorize programmers, but it didn't work because we knew what we were talking about.
But it's just, you know, it's been horrific. And there is going to be not just this psychological cause. There's going to be a real one when people who are simply not particularly talented at business are going to preemptively lay off their writers and artists. Because they think that Gen AI is going to do it.
And, and then they're going to have to hire them back, but that's going to be like a rough one to two years while people go through this, this cycle.
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I think it's going to be interesting to see how it unfolds. Just because I In the circles that I travel in, I don't see people losing their jobs for Gen AI. There's a, there's a sense it'll happen real soon, once it's just a little bit better, but I don't see it yet.
I see excuses for layoffs coming all the time because, uh, we, we're bad at planning, so we're going to lay off a bunch of people, doesn't play as well as we have optimized their roles with Gen AI. I feel like there's a lot more of that latter case than there are the former in the circles that I tread, but I see it myself and instead of the conference talks I give lately, instead of doing a lot of purchasing of stock photography, I will just have one of these things generated because there is no stock photograph That I will get without a commission of hiring photographers to specifically do this.
But I [00:24:00] needed a picture of a data center aisle. Great! Now put a giraffe in. There is no zookeeper who is wandering there, uh, is taking a giraffe and wandering that thing through a digital realty trust data center or Equinox somewhere. That just isn't going to happen. So bad Photoshop or Just wind up having the bot spit that out for quick and dirty things like jokes on Twitter or throwing it onto a conference slide.
That seems to be acceptable, and I think that that's where you're going to start to see some erosion from the bottom up. And I don't honestly know what to do about that.
Nikhil Suresh: Yeah, well, I guess there's two things, and one is if you couldn't do that, would you have hired someone to bring a giraffe in the data center, or would you have just not made the joke?
Corey Quinn: Exactly. I would have done some bad Photoshop and Microsoft Paint, and that would have been the end of it.
Nikhil Suresh: Yeah, it'd save you, you know, a little bit of time, and then, I don't know what you build per hour, but how often would you have to get to like 600 billion market track?
Corey Quinn: That's an awful lot of giraffes and a lot of data centers.
I feel like at some point that's gonna be hard to do because as we all know, giraffes aren't in fact real. It's a terrific scam, [00:25:00] but I've seen giraffes. They're clearly fake. I mean, there's no way that thing can exist.
Nikhil Suresh: They're just long horses.
Corey Quinn: Exactly. But, oh no, remember, unicorns aren't real. Because, you know, a horse with a horn on his head, oh yeah, that can't possibly exist.
But this thing with a 20 foot long neck, yeah, that's real. How gullible do I look?
Nikhil Suresh: It is just, it is fascinating to me that so many people are uncritically just talking about how these LLMs are going to revolutionize everything and to some degree I wonder if it's, you know, almost like a cult signaling.
When you're very deep into a cult or something you make displays of faith not by necessarily, it's not just believing, it's by saying unbelievable things very very sincerely and the more unbelievable it is the more you are showing to people around you like you're fully committed. And I think there's like two management classes, one with people who kind of know what they're doing and they're nice to talk to, and another who, uh, I always say they just had like Forbes magazine flashing in their brain.
And I think that's like, it's more than 50%, like it's [00:26:00] most people in the corporate world, you know, which is a pretty horrifying number, like most people. And, um, Yeah, they just might be doing all of this to signal to people around them like I'm in on the grift. When the Gen AI thing, you know, disappears, I'm going to say the next crazy thing I need to about crypto or quantum.
And that's how you know you can bring me on board and I'll help you trick someone into giving you funding.
Corey Quinn: It's wild to me that I will go to cloud keynotes and they will have their CEOs on stage talking about how different reference customers are using Gen AI to completely revolutionize their product.
Okay, great. Some of those companies are in fact my clients. So I talked to them, and I'm like, Oh great, I somehow must have missed that on our latest engagement call. Uh, what's going on? Like, oh yeah, we tried it for a bit, didn't really work super well, didn't see much value, so we cancelled the project.
It's like, huh, that's not the story that was being told on stage. So you start to wonder, on some level, these executives and these managers, what's going on? Do they genuinely believe the things that they're saying because someone [00:27:00] else told them that? Do they know that they're not telling the truth? Or is it a game of telephone where someone's like, Yeah, we tried it, like, Yes, this company is using it.
Oh, this company is using a lot of it. Oh, this company is transforming themselves with it. And by the time it gets to them, it sounds like the greatest thing since sliced cheese.
Nikhil Suresh: I've met a couple and, uh, I at least try to be charismatic enough that they open up occasionally. So I've met one who admitted that they I don't think any of it works, but they feel like they have to say these things.
And they've always got some reason. They're like, we'll use this to get funding and do something good for the employees or whatever. But you know, I'm like, it's the line. You got a job. You can't just come out there and sling bullshit all day and be a good person. You get others who are true believers. I think the most concerning one is when you meet a non technician who has kind of ended up in management because they did a lot of like large scale enterprise corporate work.
And, you know, that's almost entirely political, which is kind of promising people things. Like politics is not that hard. And with those people, [00:28:00] sometimes they're actually kind of clever, but you can almost see that they have been groomed by people around them for 30 years. Like they've just had their bullshit detectors turned off.
And they usually make, you know, they'll go like, they'll ask one or two self critical questions. But the thought process stops there. They can't sustain that line of reasoning for long enough. And I kind of understand why, right? If you've had your direct reports lying to you for 30 years, Like, you're just like this crazy gaslight echo chamber.
If someone did that to me, I would think I'm a genius and stop questioning myself.
Corey Quinn: Yeah, when you, when you remove people who are telling you how it is from your orbit and surround yourself with yes men, yeah, it gets very hard to identify the truth from all of the nonsense filtering through.
Nikhil Suresh: Yeah. And you know, grifters, people have good social skills.
They play this complex metagame where they like deliver just enough bad news that it seems like they're being sincere. But it's carefully calibrated so, you know, it's just short of like you need to fire them. They just occasionally deliver something that sounds pretty [00:29:00] bad. Like you can't stand up to like 200 people doing that for 30 years.
You're just going to come out a changed person.
Corey Quinn: At the moment, when someone self describes a data scientist or an AI expert, one of the ways that I've, uh, filtered for are they a grifter or not is to pull up LinkedIn or equivalent and see what were they doing in 2019. Because if it involved data science or machine learning, great, they probably have some idea of what they're talking about.
If they've only been doing this for six months, they're probably a grifter. Problem is, I feel terribly for people who are graduating in the field legitimately now, Because they get buried in the nonsense noise. Like, how do you, how do you guard against that?
Nikhil Suresh: I've been advising a lot of students to reach out after that post.
Because a lot of them said, uh, for some reason, a lot in Brazil, specifically, were like, we're, uh, we're all graduating from universities, and we're really scared jobs aren't going to exist, or we can't stand out. Um, the thing I told them is actually to stay really clear of the Gen AI space. Go find a small to medium business doing like more [00:30:00] classical statistics or you can demonstrate you've got actual mathematical ability and work on your operational knowledge.
Like just become a good software engineer, uh, with some experience in this type of algorithm. And you know, I think that'll be fine because when the bubble collapses, we'll go back to where we were pre 2019, which is a small number of companies will need people who can actually do machine learning statistics.
And you just want to be well positioned and networked at that point. I wouldn't want to get into the field now, not because you can't get a job, but because it's so hard to find a job where you're actually going to pick up real skills. If you just join a random company out of university, you are just going to be hanging out with morons.
Like it's, it's so hard to find a good place.
Corey Quinn: It takes some time in the industry to develop a fine tuning on your bullshit filter to figure out is this founder high on their own supply or do they actually have something that they're doing that is legitimately useful? Because without a bit of experience under your belt, it's very easy to instead get fooled by whoever [00:31:00] sounds the most convincing and that's dangerous.
Nikhil Suresh: Yeah, and you know, as a student, you're just not going to have that kind of background. And what's horrifying is there's still more well positioned than non technicians, right? So I think the healthy attitude for anyone who's graduating is find a place doing something hard that isn't Gen AI. And if you're not a technician, it's like, it's like crypto, right?
The moment someone starts talking about it, just kind of walk away. They might have an actual product. In the same way that there must be a real blockchain applicant. I've never found one, but I haven't looked that hard. There must be somewhere on the planet.
Corey Quinn: I've been looking, but 15 years in, it feels like it's a solution looking for a problem.
And honestly, it feels like it's speculation, fraud, and fraud adjacency.
Nikhil Suresh: I think it'd be generous to say 1 percent of crypto projects actually have something backing them. But let's, let's be generous and we'll even say 10%, right? If someone starts talking about even a 10%, unless you're really deeply invested in the field, just don't listen to them.
And the same with GenAI. If someone's like, I've got a GenAI product, I'm like, just don't talk to them. They're [00:32:00] probably on the balance of things trying to scam you.
Corey Quinn: You introduced me to Sturgeon's Law, which comes from some sci fi work in the 50s that states, quite simply, that 90 percent of everything is crap.
The problem is, in this case, How do you, how do you weed, how do you weed out the wheat from the chaff, so to speak? Not that you weed wheat, but that's okay. I'll abuse metaphors to death.
Nikhil Suresh: It's an interesting thing because, so I came from a psychology background, and psychology is very competitive in Australia.
Corey Quinn: Competitive psychology is a tournament I would watch, but please continue.
Nikhil Suresh: That'd be pretty good. Just a bunch of people trying to psychoanalyze each other until someone cries.
Corey Quinn: That's called scrum.
Nikhil Suresh: Scrum's a good example of Sturgeon's Law, right? But, so my psychology is meant to be super competitive. And I very, very easily, like, just got into, like, whatever the highest end program is in the country.
Again, not because I was a genius or anything. I was just like, people weren't even trying. You were, I was sitting at a university with supposedly the best students in the country. And they would fail at assignments and go, how did you [00:33:00] do it? And it would turn out they didn't open a book. Didn't open the textbook.
Like, well, it's pretty easy. I read the book they assigned. And then I left psychology because I'm like, no one's serious here. Gone to data science. Same with that, right? I'm at the end of a two year master's, and students are coming up to me going, what is machine learning? And I'm going, well, we've been studying it for two years, brother.
I don't know what to tell you if you haven't figured it out now.
Corey Quinn: Did you pay attention to anything? Yeah. It's like, where do you even start with someone when someone hits you with that?
There is a counterpoint that I've been working in cloud for a decade and change now. Like, every time someone asks me, what's the cloud, it's like, increasingly, I have no idea.
It feels like it's become such a wishy washy term. And I think on some level, that's what machine learning is turning into. It's become a hot button, uh, hot button phrase that people want to pile into, and it's diluting the actual meaning of the term.
Nikhil Suresh: They all are just starting to boil down to like a computer was involved somewhere.
Corey Quinn: How many services that you use that require no Gen AI are now referred to as a Gen AI service, particularly from cloud providers? It's, well, why are they calling it that if it's [00:34:00] not really using anything? Ah, this is where we learn how politics work and why project managers who are ambitious would like to get promoted and build larger orgs.
It's this modern day feudal lords of Amazon that we wind up seeing from time to time.
Nikhil Suresh: Yeah, it's the um, you know, you mentioned Agile is how you torture people. Agile is actually very useful as a judgment signifier, because when someone talks about it too much at a job, even before you get in, if they even bother saying something like, we're an Agile team, you know that they either have no idea what they're talking about, or, and this happens sometimes, They actually do something that approximates, uh, approximates working agile, but they're not socially savvy enough to realize what they sound like.
In which case, you still don't want to work for them, right? A smart person doesn't talk about agile.
Corey Quinn: Or within the last three months, they had a massive reset and took all the engineers for a week and threw them in a room with an agile coach at ruinous expense and people's time, and they have to say the right things for the next quarter.
Nikhil Suresh: I know of at least one company that said everyone is agile trained, uh, and it turned out that meant they all did a 20 minute [00:35:00] LinkedIn course and then saved the PDFs to a network drive. So, you know, digital transformation, very powerful, very good. That's a huge organization.
Corey Quinn: Yep. It's amazing how people love to play these games.
It'll work. It has to, because we need it to, otherwise we're going to have some awkward questions on the earnings call or to our regulators or to our investors.
It just feels like it's an overheated hype cycle. I'm someone who sees value in Gen AI in a bunch of different ways. I love tricking different models into ranking the U.
S. presidents by absorbency, which is something that's very hard to get a human to do, and surprisingly simple to get a robot to do if you give it the right prompt. Whether I necessarily want to have it do my taxes, I don't think that's gonna end well for me.
Nikhil Suresh: No, no, it certainly isn't. And, uh, you know, I realized that you asked about Sturgeon's Law earlier.
It's something that comes up in my writing and people get really, really upset. They go, how can you call 90 percent of things crap? And it's like, it's, it's kind of [00:36:00] look at them. Exactly. You know, and it's like, it's just how they are. And it doesn't look that way. If you're not a specialist in that field.
For instance, most people can't actually tell that 90 percent of writing is not particularly good. Every writer or journalist I've spoken to since the blog post comes out just endorses that immediately. They go, of course, 90 percent of writing is not terribly good. It's not a judgment about the person, it's a judgment about the writing.
You know, I'm not a very good piano player and everyone who plays the piano knows that. My random friends don't. But the reason I raise this is because I, Think of that as kind of the mechanism people defend themselves from this GenAI, you know, just absurd hype site, which is just keep developing your professional judgment, and you can work yourself into spheres where Your output matters more than ChatGPT just spamming crap out, right?
Just embrace Sturgeon's Law. It is not a bad thing. People think it's pessimistic. It's a profound source of optimism. Because if things weren't 90 percent crap, I would, I would be homeless. I'm not smart enough. [00:37:00] But because people are trying, it's like, yeah, this is tremendous.
Corey Quinn: Back when I had my SRE days, people were somewhat surprised with my job hunting approach.
Like, don't you want to work in the best environment you can? I was like, no, all I can do there is f it up. I'd much rather work someplace that's an active burning fire, because I know how to fix at least some of those things. It's, how do you want this to, to, to evolve? I mean, so many of these companies are telling on themselves where, Oh, we're using RAG to wind up bringing in our documentation to answer things through a chatbot.
It's like, that's a lot of words to say your documentation is dog shit. Maybe you should fix that. And suddenly you don't have that problem anymore. It's, it's a technology that people are using to paper over other cracks, but you're just building the house of cards a deck higher.
Nikhil Suresh: The rag thing is so interesting to me because you have companies that, they have hundreds of confluence pages that no one can navigate, no one's updated.
You go on there and it's just like this graveyard of employees who all left one year after they spread the page.
Corey Quinn: Almost to the day. Yeah.
Nikhil Suresh: You know, it's, it's just so, so common and they go, well, [00:38:00] this is an unnavigable mess, right? It's all out of date. They'll acknowledge that and then go and then if we hook all that documentation up to an LLM, you know People will be able to work with it
Corey Quinn: We're gonna customize an LLM on our code base or our internal documentation like that That's a polite way of saying we're gonna take a really smart robot and then give it a traumatic brain injury to see what happens
Nikhil Suresh: Yeah, just like delete all the pages.
Why are you wasting your time on this? And again, it's so hard to tell which which of the people doing this think that's going to work in which you're grifting You I'm no longer sure there's like a coherent enough world model in their head that you can really categorize them. I think they just flip between the two modes randomly, but yeah, like it's just everyone I hit up I go, hey, you know, they hallucinate and then they come back with, you know, oh, we'll do rag.
Corey Quinn: Well, it works in our internal stuff, say Microsoft and Google and Amazon. It's, yeah, you understand that your companies internally may as well be alien organisms compared to 99 percent of the companies on this planet. [00:39:00] So it works for your use case and you trained it for your use case. That's great. If we all suddenly start acting like you, but the last time one of you tried to get us to do things the way that you do, you inflicted Kubernetes on us.
And here we are.
Nikhil Suresh: Yeah. It's also, it is fascinating to me that, uh, You know, when these companies talk about their stuff working, that might be what they're saying, but their actual senior and staff engineers email me to tell me it doesn't work that way internally. So even though, like, if it can work, it would work there, but it's still mostly not working there, right?
Like, they're also trying to trick us.
Corey Quinn: During conference talks, I love the back channels with the speaker's colleagues who are basically calling all the bullshit in the talk that they're seeing right now. It's like, I sure wish I could work at a place that did that. Yeah, me too, because I do work there and we don't do anything like this.
Maybe they do in fantasy land, which we call conferenceware, but for the rest of it, not so much.
Nikhil Suresh: Yeah, it's just a whole bit of the industry that's based off of just lying to people about how cool you are. I'm pretty happy to admit like a lot of my early [00:40:00] projects were dumpster fires.
Corey Quinn: Most of mine still are.
The goal is to get far enough along where you can bring responsible grown ups in to push you out of it and take it over to do it correctly.
Nikhil Suresh: Yeah, yeah. A lot of consulting is just selling fire extinguishers, right? And you rarely end up in some beautiful, uh, I think I've only had two projects ever with an existing codebase where there was a genuine chance of, like, making it something pristine and beautiful.
And none of that was because I came in as like this brilliant consultant. It was because the team internally, I don't know what happened. They got like real drunk one day or something. It just had like a soul searching moment. And they just approached us and were like, we're actually all, we're ready to clean all of this up.
We just want some advice on how to do the thing. And we'd really end up doing it for them because it's hard to hair drop consultants in to do that. What we can do is stuff like you do, because I believe you do like cloud cost optimization.
Corey Quinn: Oh yeah, and I know a lot of companies that have died through not, through not having product market fit or not being able to succeed as a business, but I know [00:41:00] very few who died because their code was so bad it killed them.
It's a, there are, you can generally work your way around engineering if the business has found success, the inverse is never true. Our code is pristine. Yes. And you're out of money. So turn it off.
Nikhil Suresh: Yeah, exactly. Uh, because that's the thing. Even, even if a code base is really, really bad until you're like late stage enterprise, you know, whale on the beach dying.
It's like basically get away by just dropping more and more bad engineers under the product. And like revenue tends to be non linear, right? So you, you tend to end up with either nothing or way more money than you need. Very rarely, like on the exact, like we're barely surviving once you're past the early stage of the business.
So, yeah, I see companies all the time, they just, they just keep saying they're retraining data analysts into data engineers, which, like, you can't do that in, in two months. It takes more than two months to learn, like, the basics of programming.
Corey Quinn: Imagine that.
Nikhil Suresh: But they just keep, they just keep doing that.
They keep moving the analysts over until you have, like, 40 people ingesting five gigabytes of data a day. Like, they're alive, those [00:42:00] companies are kind of doing fine.
Corey Quinn: I really want to thank you for taking the time to speak with me about all this. If people want to learn more, where's the best place for them to find you?
Nikhil Suresh: So there's the blog, which is the main reason people are talking to me. So that's ludic. madaroa. blog, which we'll have to put a link, I suppose, because that's hard to spell. Uh, and then there's hermit-dash-tech, which is where I work.
Corey Quinn: And we will include links to both of that in the show notes. Thank you so much for taking the time to speak with me.
I really appreciate it.
Nikhil Suresh: Thanks very much. It was good to be on.
Corey Quinn: Nick Suresh, Director at Hermit Tech. I'm Cloud Economist, Corey Quinn, and this is Screaming in the Cloud. If you enjoyed this podcast, please leave a five star review on your podcast platform of choice. Whereas if you hated this podcast, please write a five star review on your podcast platform of choice, along with an obnoxious comment that a Gen AI thing wrote for you badly.
And then one of your executives will not shut up about on stage.
