A Life Engineered · A Life Engineered · Transcript & summary by WhipScribe
It is a quick, skeptical inside view of why AI mandates and layoffs are reshaping who keeps a tech job, with a concrete number for what AI actually adds.
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“Stock price goes up when you lay off a bunch of people. That's just the reality of it.” Steve Huynh · at 6:17 — open in the transcript →
“I think the big reason why lines of code or token maxing is a bad measure is because they're input metrics.” Steve Huynh · at 10:47 — open in the transcript →
Steve Huynh, a former Amazon principal engineer, uses Meta's latest round of cuts, which came during record profits and were followed by reports of rehiring managers, to ask whether Big Tech is trimming too close to the bone or rethinking its workforce entirely. His guest, who is not named in this clip, offers two explanations. The first is the X precedent: Elon Musk cut roughly 80% of Twitter's staff, the platform did not collapse, and executives elsewhere may now wonder whether a 20% cut is survivable. The second is a feeling, stressed as speculation, that the moment resembles 2007 and 2008, when soaring numbers made risky moves feel affordable and outside conditions served as cover for layoffs. Huynh adds that cuts lift stock prices and clear out low performers who are hard to manage out.
On who stays, the two largely agree that engineers seen to lean into AI and direct agents are being rewarded, while those who are not are more likely to go. The guest's caveat gives the clip its title: specialists who were strong long before AI, in areas such as security or cryptography, sometimes resign because they cannot meet their own standard of diligence in an AI-first workflow.
The last stretch is about measurement. Mandates like closing 20% of tickets with AI invite people to simply label work as AI-assisted, and Huynh compares token counts to the old lines-of-code metric: both measure inputs, not output. The guest's preferred test is comparing six months of a small team's work against what it would have shipped without AI, and in client studies that gap comes to roughly 20%, never tenfold. The episode closes with a sponsor read for Linear.
Steve Huynh, the former Amazon principal engineer behind A Life Engineered, points out that Meta cut a large share of its workforce while posting record profits, then was reported to be trying to rehire managers who understood its systems and to coax former managers back from individual-contributor roles. With profits that strong, he asks why the company would shake things up at all.
Steve Huynh's guest on A Life Engineered, speculating and without inside information, suggests some executives look at X as proof of concept: Musk cut roughly 80% of Twitter's staff, many predicted the site would fall over, and it kept running, now serving more traffic and live video. If that survived, he imagines leaders at Meta or Google asking whether a 20% cut could work for them.
Steve Huynh says he is highly suspicious that many recent layoffs are financial moves, because a company's stock tends to rise when it cuts staff, and that AI also serves as cover for removing low performers who are hard to manage out. His guest draws a 2007 to 2008 parallel, when people suspected external financial conditions were being used to justify layoffs that would look good to the market.
Steve Huynh's guest says the reward for using AI only helps people seen as high performers through AI. He knows highly skilled specialists who were objectively strong before AI and are now handing in notice, because they feel they cannot do their work with the diligence it demands in the AI-first style their employers want. He uses a security engineer with decades of experience in cryptography and protocols as a hypothetical example.
Steve Huynh compares counting AI tokens to the old lines-of-code metric, which led to bloated software because engineers could, for example, unroll a loop to inflate their count. Both are input metrics, he says, while productivity is about what actually gets produced. His guest adds that rules such as closing 20% of tickets with AI lead people to simply label 20% of their work as AI-assisted.
Steve Huynh's guest says the hard-to-fake test is taking a team of five or ten engineers, looking at what they produce over six months, and estimating what they would have produced in that time without AI. From informal versions of this study with clients, he estimates reasonable AI use adds about 20% to the economic value a team produces, and he has not personally seen anything near a tenfold gain.
Our own transcript of the public episode, with a timestamp about every 90 seconds. Tap a timestamp or the link button on a paragraph to copy a link that opens this page there.
0:00Meta just recently laid off a large percentage of their workforce in their latest round. There were reports that certain groups were going to be eliminated, I think like 75% or 80%. Like they're doing a radical restructuring despite the fact that they have record profits. There are also reports that they're desperately trying to hire back a bunch of managers that knew where all the dead bodies were buried. And then they're also desperately trying to convince people that were managers that went to IC to come back to being managers. And so do you have any commentary or thoughts around the cycle of layoffs? Is it a situation where seeing companies trying to get more efficient and maybe cutting a little bit too close to the bone with their efficiencies?
0:48Or is this a situation where there's like a radical paradigm change in the way that we're thinking about tech workforces? Well, I think there are at least two things happening here. One is that some of the C-suite and the VPs right under them do believe in this radical change. That theory, which is not my theory, but it's the theory that I think many people there have, was bolstered by, like him or hate him, what Musk ended up the user experience on X. But the thing you cannot deny is that they had radical layoffs. Many people were predicting that it would just fall over. It did not fall over. It's serving much more traffic now. It's now also serving live video streams. And so at a minimum, it didn't die, right?
1:41There are reasonable arguments about how much it's prospering, but at a minimum, it didn't die despite, I remember something like 80% layoffs. It might have been more than that. It might have been more than that, right? Yeah. I think some people are looking at that and saying, yeah, this supports the idea that maybe it will be ugly in the short term, but actually it's possible, right? right imagine now hypothetically i have no such information but just imagine you are the c-suite at meta or google and you're thinking could we get away with firing 80 percent of people right because i mean that's a big bill that's a really if i if i look at the financial statements that's a lot of money right and maybe we just tank a year or two of really bad pr and unpleasantness
2:22again i have no such information i'm only saying that I can imagine someone saying, look, it seems not to have killed Twitter or Musk. So maybe it's possible, right? I think that's one thing that people are thinking about. Another thing is, this is outside my economic or financial expertise, but I lived through the 2007, 2008 era as well, just as you did. And I saw what was happening in big tech at the time, but also partly in finance at the time, because I'm not in finance, but I have friends who are. It feels a little bit like that. No, certainly not 100% like that, but it feels like maybe 50% in that direction. The things are blowing up. So blowing up in the good, in the line go up sense, not in the explosion the lines are going up so quickly
3:06and people are wondering hey maybe this is a time when we can afford to do some risky things because the lines are going up so quickly i don't know that's pure speculation but it does it feels a little bit the same i emphasize not 100 but like 50 the same as what I saw happening in tech and finance in 07, 08. That was also followed by substantial layoffs. I don't have the economic or financial expertise to judge how similar the situations are, but just from the inside, it sort of feels the same. I think the Twitter case is particularly interesting because I think that was much more like Elon Musk stating that most people here are useless and not actually contributing. I don't think it was an AI layoff per se. It was
3:52basically like he had a thesis that there's just a small amount of people that are actually keeping this place running and I'm going to fire most of the people and then we will try to patch up the holes afterwards. I was one of those groups of people that thought Twitter was just going to keel over and die. It was pretty clear, I think, that Elon Musk didn't understand GraphQL. And so he thought that, you know, there were like hundreds of calls that were being made from the client to fetch all of the little pieces of data. Not that the backend was, you know, had some sophisticated technology to actually aggregate all of the information that was there. Super weird. Sometimes I'm like, wow, this Musk guy must be pretty smart. And then he
4:30starts to talk about software development. And I'm like, wow, is this what he's talking about? Is this his level of sophistication with electric vehicles and with space? Because then maybe things might be in trouble. But I was totally one of those people that thought that Twitter was going to keel over and die. It did not. And you might make the argument that there's the video stuff that's there. A bunch of people moved to other platforms, but it might be stronger than ever. I think Twitter and X are more relevant than ever. Possibly. And I think if I put on my Musk skepticism hat for a moment and say, okay, they fired 80% of people. Maybe it wasn't that motivated by deep analysis of the technology stock, right? I think that's probably fair that
5:11it was not motivated by deep analysis of the technology stock. I think the fact that despite all that, despite the crudeness, despite the fact that it was such a large layoff, that it's still working. I think if you're a C-suite person, you have to be thinking, okay, well, could we do like 20%, right? I don't know anything about Google C-suite, right? But probably Google C-suite does not think that most of Google is useless, but they might think, hey, I mean, if he got away with 80%, could we get away with 20%? Again, pure speculation on my part. And just connecting this back to the AI, sorry, but the parallel that I see with the 07-08 was that at the time people were very very skeptical that the layoffs really needed to happen and they were wondering whether they were
5:50using external financial conditions as cover for justifying the layoffs so that they would look good in the stock markets and maybe now ai is being used in a parallel way again i emphasize i'm not any kind of financial or economic analyst, but these are just as experiences of being in the tech business, things I was hearing back then, I'm starting to hear now as well. I'm highly suspicious that a lot of these layoffs were a big financial move. Stock price goes up when you lay off a bunch of people. That's just the reality of it. But also using it as cover to get rid of low performers that are difficult to fire, to get rid of, right? And so we're sort of like using that as cover to like get more efficient and more lean,
6:33but also just like move people out that were kind of hard to manage out through other mechanisms. When it comes to the meta case, I think it's really interesting because it's like, it's such a self-inflicted wound. When you have record profits, like why rattle the cage and try to like remove so many people from the workforce? You know, and this is kind of the thesis that I'm thinking through. High performers, people that are valuable to the company, people that are making really big contributions, people that really understand AI technology and how to control agents. Those people are being rewarded more than ever in the workplace. Six months ago, we thought that all engineering teams were going to get replaced with product managers and a Cloud Code subscription. We don't
7:20need any of these people. Turns out, at least from what I'm seeing, people that are really leaning into AI and applying it to their current workforce, those are the people that are being retained. You know, where it's really apparent that they're not making a really big contribution, they're not really leaning into AI, those are the people that are more likely to be let go. But what's your counterpoint? I'm super curious. So I agree with everything you said. And then I want to add, and then. The and then is all of that is contingent on being a high performer viewed as a high performer through AI. But what if you're a high performer not through AI? And I know several such people who are having unpleasant
8:02discussions at their firms and people who are very specialist knowledge. I probably, if I told you the domains, it would be enough to localize where these problems are. But anyway, it is not computer security engineering. So let's take computer security engineering as an example. You can imagine someone who has decades of experience in computer security engineering and cryptography and protocols and business policies and whatever such a person saying i can't do my job at the level of diligence that the job demands of me using ai in the way you want me to use ai and so i i know i emphasize this is a hypothetical example but i know people who are in a parallel situations who have just said, look, I'm putting in my notice.
8:48I can't work this way. And this is a person who was prior to AI objectively very high performer, but they feel that they can't do what they should be doing in this AI first style. I agree with everything you said about the people who are most rewarded right now are the people who are most able at least to appear to be increasing productivity through AI. I think some of that is performance. I don't know if it's 20% or 40%. I mean, I certainly know people who tell me, they tell me that I have to be using AI to close at least 20% of my tickets. So I just pick 20% and call it AI and that's it, right? So some amount of that flow of reward is I think being maybe misdirected is not the right word,
9:35but there is some amount of, like if you tell me that I have to say that I used AI to get a reward, then I'm going to say that I used AI, right? This is just ordinary corporate dynamics. Well, I think it's very similar to like the lines of code metric that we might've used in prior years. Yes. Which was like, we're going to measure your productivity based off of the amount of code that you create. Well, that just led to bloated software, right? Like you can unroll a for loop and make as many lines of code as you'd like to, right? Remember when can, you can unroll a for loop and make as many lines of code as you'd like to, right? Remember when token maxing was a thing and everybody's like, look how many tokens I'm burning for all of this stuff. Well, if you just simply counted tokens,
10:14like you could just, you could basically tell the AI, like, Hey, go do a bunch of stuff and burn these tokens. And then like, you know, spend even more tokens to go and clean it up. Voila, right? The thing you want is good code. You observe that lots of ambitious software has a high line count. Therefore, having a high line count is good. Therefore, we reward engineers for producing many lines. And now you have engineers who are incentivized to produce many lines, not engineers who are incentivized to produce useful software. And you just substitute AI in there for line count, as you say. I think the big reason why lines of code or token maxing is a bad measure is because they're input metrics.
10:54Ultimately, what we care about is outputs. When we talk about productivity, it's about the thing that was produced after that. Increasingly, I think a good way to have an insurance policy is to use AI to ship more things at higher speed. Yes, I agree. And this connects back to what I was saying earlier about if you really try to understand what your organization has gained from whatever you did with AI, whatever adoption pattern you chose. I think the relevant metric that is very difficult to fake is take your team of five or 10 engineers, see what they produce in six months, and try to figure out what they would have produced in those six months without AI, and what's the gap. I've done informal versions of this study with clients before.
11:45My estimate is that you can get maybe 20% boost in the economic value of what you produce by using AI reasonably. And I have not, again, in my personal experience, I have not seen anything that's like we produce 10 times the economic value in those six months. Quick word from today's sponsor, Linear. Linear has been a sponsor for almost a year now. And in that time, a lot has changed in how teams build software. Coding agents like Clog Code blew up, and they're being used to write more and more code. Writing code got fast, but reviewing it didn't. A lot of the speed that you get out of agents just gets swallowed back up in review. Now, I spent 18 years as an engineer at Amazon, a lot of it reviewing other people's code,
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