2026-07-08 20:03:35
Everybody is saying, “It’s not the model, it’s the harness,” but almost nobody stops to explain what a harness actually is. So I did. I built one live on the show: a Sentry bug-debugging harness for my company ChatPRD, using the Claude Agent SDK, a custom terminal UI built with the Ink library, and opinionated adapters for Sentry, Linear, GitHub, and Vercel. The harness handles evidence gathering, root-cause analysis, and follow-up artifact creation, all without me needing to type “dear agent, please fix this bug” ever again. I also walk through the architecture, share the code structure, and give you the exact process I used so you can build your own harness for any repetitive, structured workflow in your business.
Listen or watch on YouTube, Spotify, or Apple Podcasts
What a harness actually is
When to build a harness versus when to stick with a general-purpose tool like Claude Code or Codex
How to encode specific permissions into a harness
The three components every harness needs
How I used GPT-5.5 and Claude Opus to build the harness code itself (and where they both initially resisted)
How to structure the artifacts your harness produces so the whole team can use the output
Bolt.new—Turn your idea into a real product
Customer.io—Build customer engagement campaigns from a single prompt
(00:00) What is an AI harness?
(03:19) When to build a harness
(04:33) Why Claire picked bug triage
(06:00) Why not just use Claude Code?
(07:48) Demo: The custom harness interface
(11:04) Architecture: runs, tasks, tools, and artifacts
(13:44) Building it with Codex and Claude
(15:08) Code map and file layout
(16:51) A look at the code
(19:18) The live investigation result
(21:01) How to build your own harness
• Claude Agent SDK (Anthropic): https://code.claude.com/docs/en/agent-sdk/overview
• Claude Sonnet 4.6 (model used inside the harness): https://www.anthropic.com/news/claude-sonnet-4-6
• Claude Opus (used to build the harness): https://www.anthropic.com/claude/opus
• GPT-5.5 (Codex, used to build the harness): https://openai.com/index/introducing-gpt-5-5/
• Ink (terminal UI library for Node.js): https://github.com/vadimdemedes/ink
• Sentry (error monitoring): https://sentry.io/
• Linear (project management): https://linear.app/
• GitHub: https://github.com/
• Vercel: https://vercel.com/
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
2026-07-07 21:04:22
👋 Hey there, I’m Lenny. Each week, I share deeply researched product, growth, and career advice. For more: Lenny’s Podcast | Lennybot | How I AI | Become an AI-Native Builder and other favorite AI/PM courses
P.S. Get a full free year of Google AI, Cursor, Lovable, Notion, Manus, Replit, Gamma, n8n, Canva, ElevenLabs, Factory, Wispr Flow, Fin, Supabase, Bolt, Linear, PostHog, Framer, Railway, Granola, Warp, Gumloop, Magic Patterns, Mobbin, ChatPRD, and Stripe Atlas, by becoming an Insider subscriber. Yes, this is for real.
A year ago, we ran our first large-scale survey of how tech workers feel about their jobs and careers. We summed up what emerged in four words: burned out, but optimistic. Today we’re back with the results from our 2026 survey, and it’s a tale of two workforces.
One half feels amplified by AI—more capable, more confident, more excited than they’ve been in their entire career. The other half feels shaken by it—less sure of their value and whether there’s still a place for them. Which side of that line people fall on predicts how they feel about their career more than anything else, including their current role, seniority, company size, or any other measure we collected. The workforce is bifurcating into two realities.
But there’s more: Burnout overall jumped 11 points in a single year, and four in 10 respondents are worried about losing their job. Even those who feel optimistic about their own career may not recommend that friends follow their path. In the AI era, everyone agrees the ground is moving. No one is sure yet if it’s an earthquake or a launch.
We think these findings are important enough that we’re making this post free for everyone.
Let’s break it down.
The workforce is splitting in two. Tech workers are either amplified by AI or shaken by it, and that divide shapes their feelings about work more than any title, tenure, or company.
Burnout is surging, and optimism is fading. Significant burnout rose from 44.7% to 55.7% of respondents, while career optimism fell from 54.8% to 48.7%. Those who feel destabilized by AI are feeling the least optimistic and the most burned out. A worrisome trend.
Tech workers wouldn’t recommend their own field. More than half (53%) would steer a newcomer away from a career in their role, even though they’re optimistic about their own future.
Productivity is up, but quality is questionable. 82% say AI is making them measurably more productive, but many worry the gains are coming at the cost of the sharpness of the work and the worker.
The underlying fear is of being overworked. Only 22% worry about “losing my job to AI.” Far more worry about being expected to do more for the same pay (51%), getting trapped in an unsustainable pace (46%), and the quality of their work going down (41%).
Almost everyone is ambivalent. 77% of respondents picked at least one positive and one negative emotion about AI. The average person selected more than five emotions. The defining feeling about AI is ambivalence.
Designers and researchers are the most worried. They report the most AI anxiety, the most fear of job loss, the worst-rated managers, and the lowest willingness to recommend their field. It’s a continuation of a trend we flagged last year.
Founders are still the happiest people in tech, and small companies are still the best places to work. Both findings replicate from 2025, and both are statistically robust.
Managers are still the biggest lever for happiness. Manager quality remains the strongest driver of burnout and one of the strongest drivers of everything else.
The industry, in tech workers’ own words, is “chaotic.” Asked to describe the state of tech in a sentence, the most common theme by far was chaos, though the sentiment was split almost evenly between excitement and dread.
To understand AI’s deeper impact on people, we asked an existential question: How has working with AI shifted how you see yourself as a professional? We gave respondents five options. Here’s how they responded:
“Amplified (I can do more, and better)”: 49.0%
“Redefined (My role is changing shape, but I don’t see that as clearly positive or negative)”: 27.4%
“Destabilized (I’m less sure where I stand or what’s really mine)”: 13.9%
“Diminished (I feel less essential or less valuable)”: 5.0%
“Unchanged”: 3.2%
When we lined up that question against the rest of the survey, the four identity groups differed dramatically:
As you go from “amplified” to “diminished,” optimism collapses, burnout climbs, layoff fear climbs, and willingness to recommend the field falls off. The people who feel amplified by AI are thriving. Those who feel diminished by it are in distress on every measure.
To make sure this wasn’t an artifact, we ran the numbers a few different ways:
In a regression pitting every variable against each other, AI-identity stance was the single strongest predictor of career optimism (standardized β = +0.39) and of whether someone would recommend their field (β = +0.60)—stronger than role, level, and company size combined.
As an effect size, the gap between the “amplified” and “diminished” groups on optimism is large (Cohen’s d ≈ 1.55). For context, the famously strong “founder effect” we’ll discuss later clocks in at d ≈ 0.56. The AI divide is roughly three times as large as that. It is, by a wide margin, the biggest effect in the dataset.
The question that best predicts how a tech worker feels about their work, in 2026, is no longer “What do you do?” or “Where do you work?” It’s “What has AI done to your sense of who you are?”
We did one more pass on this data: instead of using a single identity question, we clustered respondents based on the full pattern of emotions they reported about AI. Four types emerged, and you almost certainly recognize them.
The Energized (41%). The all-in adopters. They lead with “excited” (91%), “curious” (83%), and “hopeful” (59%). They’re the most optimistic group, the least burned out, and the only segment with a clearly positive read on their field. For them, AI truly seems like a superpower.
“Product has become fun again! You become an explorer, you play around . . . you spend long hours full of excitement. We’re in an amusement park.” —PM, Principal IC
The Conflicted (35%). The ambivalent center of gravity—and the largest group after the Amplified. Their signature emotions are “conflicted—holding positive and negative feelings at once” (68%)—and “curious” (64%), trailed closely by “overwhelmed” (56%) and “tired” (55%). They haven’t soured on AI; they’re just exhausted by the work of keeping up with it while holding two feelings at the same time.
“I’m simultaneously having the most fun I’ve had as a product builder and also feeling the most uncertainty I’ve felt. I’m confident I’ll be able to keep my skills sharp and adapt, but I’m not yet sure what it is that I’ll need to adapt into.” —PM, Senior IC
The Disoriented (12%). Defined almost entirely by one feeling: “disoriented—my role keeps shifting,” layered with “overwhelmed” (74%) and “tired” (73%). These are people watching their job change shape beneath them faster than they can find their footing again. They still think AI is somewhat useful. They’re not “refusers.” They’re just losing the thread of their role in the workplace.
“Things are so uncertain, we’re like farmers on the cusp of the industrial revolution. We know going into farming is the wisest long-term career choice, but we don’t see a clear path. This kind of uncertainty crowds out productivity.” —VP Product
The Resentful (12%). The burned-out and checked-out. Every one of them selected “resentful—I feel pressured to use AI,” and they cluster with “tired,” “conflicted,” and “overwhelmed.” They report the lowest optimism, the lowest willingness to recommend their field, and the lowest sense that AI is helping them at all. This is AI fatigue transformed into resistance.
“Tech overall kind of sucks right now. We used to adopt new technology because we were excited about the cool new things we could do. Now all we hear is ‘Use AI or you will lose your job’—and then people get fired anyway. I hate it.” —Director of Product
Significant burnout is now the majority experience for tech workers. 55.7% of working tech professionals report significant burnout—meaning they describe themselves as “moderately,” “very,” or “completely” burned out. Last year, that number was 44.7%. More than a quarter (26.2%) are now “very” or “completely” burned out.
Career optimism is dropping. Fewer than half (48.7%) of respondents are optimistic about the future of their career (down from 54.8% being optimistic last year). We’ve gone from “burned out but optimistic” in 2025 to “significantly burned out, and not that optimistic” a year later. We’re curious (and a little scared) about how this will look in a year.
That being said, job enjoyment is holding up: 42.6% enjoy their work “very much” or “extremely”; another 36.7% rate it “moderately”; and only about one in five (20.6%) enjoy it slightly or not at all.
Why the apparent contradiction? Enjoyment, burnout, and optimism are different constructs. Enjoyment is about the work itself, and people still like the work. Burnout is about pace, and people are increasingly worn out by how much they have to do. Optimism is about where things are heading. You can love your craft, be worn out by how much of it you’re doing, and feel doubt about the future all at once.
This year, we also added a question to the survey: How worried are you about being laid off in the next year?
41.2% are at least moderately worried, including 19.9% who are “very” or “extremely” worried. 28% aren’t worried at all. So roughly four in 10 tech workers are carrying real job-security anxiety into their week—a sizable undercurrent.
What makes layoff worry worth its own question is how tightly it’s bound to everything else. Of all the things we measured, layoff worry is the single strongest correlate of career pessimism (r = –0.47). Nothing else tracks negative outlook as closely. When people are scared of losing their jobs, their optimism goes first.
We’ll come back to who is most worried later. It’s not who you might guess.
This year, we asked an NPS-like question about people’s careers and roles: On a scale of 0 to 10, how likely are you to recommend a career in your role to a friend starting out today?
More than half of working tech professionals would actively steer a newcomer away from the path they chose. That translates to an average NPS score of –39. Moreover, a third of the people who call themselves optimistic still wouldn’t recommend their own field.
The cleanest way to say it: “The water’s fine; don’t come in.” People have largely made peace with their own trajectory. They’ve got the skills, the relationships, and the seniority to ride it out. But they’ve lost faith that the on-ramp still works for someone behind them.
“I’m lucky I’m later in my career . . . AI can augment what I’ve built. I think I won’t be in a position to hire and mentor new PMs, but I’ll be safe. Which feels really crappy to say.”
“I’m at the point where I can just retire and choose not to, so I’m not worried about my own career. But I’m worried about the younger generations.”
The recommendation score varies enormously by role, and the spread is its own story.
Founders would (just barely) still wave you in. Designers and researchers very much would not. And the score climbs steadily with seniority: senior and staff-level individual contributors are the least likely to recommend their field (both at NPS –49), while VPs (–23) and founders (–5) are the most. The further up you’ve climbed, the more the ladder still looks worth it; the people on the rungs below are the ones telling others not to start the climb.
Given the rising burnout, the layoff anxiety, the doom in the discourse, you’d expect tech workers to be rather sour on AI. They’re not.
At the individual level, the AI numbers are among the most positive in the survey. 82% say AI is already making them at least moderately better at their job, and nearly half (49.4%) say “very much” or “extremely.” 60% feel confident or ahead of their peers in AI skills, compared with just 22.5% who feel anxious or behind.
But then we looked closer at what “better at my job” means. When we asked people to describe in their own words how AI had changed their work, “better” turned out to mean producing more and faster, but not higher quality. The productivity gains are coupled with deep unease about the costs of leveraging AI.
“I can do more, faster, but not better.”
“Amplified and destabilized at the same time. We just set a new denominator for the job. And it moves higher and higher every month.”
And the cost isn’t only in the quality of outputs. A striking number of people described their focus, their judgment, and their thinking as suffering:
“I’m amplified, but my brain is rotting, and my work feels worse.”
“I feel like I don’t think hard enough anymore—I just follow Claude. I don’t fully understand what I merge.”
“I miss feeling smart and having aha moments. I miss talking [to] and brainstorming [with] humans instead of machines.”
The productivity gains are real, but the quality of the work and the sharpness of the person producing it are taking a hit. The bar keeps rising to match what AI makes possible, and a growing share of people feel that neither the output nor their own mind is keeping up.
Respondents’ number-one worry about AI’s impact on their career is the squeeze—AI raised the bar for output, and the reward was . . . more output expected, for the same paycheck.
They’re scared that the work will get harder, faster, and cheaper, and that they’ll be expected to keep smiling through it.
It feels like the dominant narrative about AI and work has been about replacement: the robots are coming for your job. Clearly, that’s not what tech workers are most afraid of. “Losing my job to AI” came in near the bottom of the list, at 22%.
Remember the “AI is replacing parts of my job” question? Half of the respondents say it’s happening to at least a moderate extent. You’d expect that feeling to drive layoff anxiety, but it doesn’t. The correlation between “AI is taking over parts of my job” and “I’m worried about being laid off” is essentially zero (r = +0.05).
What people are actually worried about is being asked to do more for the same pay, and watching the quality of their work slip.
It shows up vividly in the open-ended answers:
“More and more work is being handed off to me because I can use AI to get it done. But that makes it impossible to keep up with quality standards and not burn out.”
“AI helps with the toil, but then it’s also an enabler to do even more toil.”
“When we automate intellectual tasks, we’ll have to do high-value creative or strategic work only—doing that eight hours a day is not realistic. I used to take rest during repetitive tasks.”
This might sound like it contradicts the layoff worry from earlier. It doesn’t. People fear layoffs, but they mostly don’t blame AI for them. What they fear from AI is being buried in more work.
Add this all up, and you get a workforce that’s more productive than ever but quietly dreading what comes next. The speed AI unlocked got plowed straight back into expectations. Every gain becomes the new baseline, and the people expected to hit it are running out of room to breathe.
If there’s one feeling that defines tech workers’ relationship with AI in 2026, it isn’t excitement, and it isn’t fear. It’s both, at the same time.
We asked people to check off every emotion that described how they feel about AI in their work. Here’s the full list, in order:
The two leaders are unambiguously positive (curious, excited). But the next cluster (if we ignore “conflicted”) is made up of people who are overwhelmed and tired. People are curious and overwhelmed. Excited and tired. Only 33% feel “hopeful,” even though 64% feel “excited.” Excitement about the present is running well ahead of hope about where this all goes.
Nikhyl Singhal named this phenomenon “smiling exhaustion.” The burnout of a few years ago was grim—all overhead and no agency. Today’s is different. People are shipping again, compensation has climbed, and many roles seem reborn. The catch is that there’s no off-switch: the tempo is brutal, and the rules rewrite themselves every month. It’s relentless, but it can also be exhilarating.
You see this in that 51% explicitly selected “holding positive and negative feelings at once.” But that undercounts the real ambivalence. When we looked at who picked at least one positive and at least one negative emotion, the number jumped to 77%. The average respondent selected five or more emotions (one person selected 13). It’s a workforce in which three out of four individuals are carrying a complex set of emotions about work.
If AI is dividing the workforce, the obvious question is: along what lines? Who’s getting amplified, and who’s getting left behind?
The clearest pattern is by role: designers and researchers are at the epicenter of AI anxiety across the board, while founders and executives are feeling the best. We measured the share of each role that landed in negative identity or emotional buckets, and the spread is stark:
Among researchers, 51% are “anxious about my job security,” versus 15% of founders. Among designers, 63% feel “overwhelmed by the pace of change” and 61% feel “tired,” the highest of any role. Researchers are among the most likely to fear “losing my job to AI” (36%, just behind Data/Analytics at 38%), and designers are the most likely to feel the comp squeeze (61% selected “expected to do more for the same compensation”). Both report the lowest willingness to recommend their field of any role, and designers, as we’ll see, report the worst-rated managers in the survey.
Last year, designers and researchers showed the largest negative sentiment shift of any group. A year later, they’re the most negative on nearly every measure we have.
As a researcher, I’m acutely aware of the years of insecurities plaguing the research community. The biggest discussions for us have always been about getting a seat at the table and democratizing research across other functions. Many now feel the seat is being pulled from under us, and the work is being democratized, not to other roles but to AI.
By level, the most identity-destabilized group is early-career ICs (27%). (This is a wrinkle we’ll untangle in a moment, because those same early-career folks are, paradoxically, among the more optimistic.)
And the bigger the company, the more likely its people are to feel adrift in the AI transition: 23% feel destabilized at 10,000-plus-person companies, versus 15% at companies of 1 to 10.
AI is hardest on people in creative and research roles, on the most junior people, and on people working at the largest companies.
For all the AI upheaval, some of last year’s biggest findings came back almost unchanged, and their persistence through such a turbulent year makes them all the more convincing. Founders are still the happiest people in tech, and smaller companies are still better places to work than big ones. Before you read those as good news, it’s worth saying what “best” means here. The whole industry is sitting on a high baseline of burnout and a rather negative career view, and the winners of this section are the people who feel a little less of it.
Founders aren’t just the happiest people in tech—on most measures, they’re genuinely happy.
Founders and executives top nearly every measure in the survey: the highest optimism, the highest job enjoyment, the lowest burnout, the lowest layoff worry, and the most excitement about AI. That gap between founders and execs versus everyone else holds up statistically. On career optimism, it measures d ≈ 0.56, a medium-size effect and the second-largest in the entire dataset, behind only the AI divide.
As we wrote last year, the likeliest explanation is ownership: founders have the most control over their own destiny, and control turns out to be one of the best buffers against everything else. 71% are optimistic about their careers, they enjoy their work more than any other role, and they’re the least worried about layoffs of any group.
Ownership has limits, though. Nearly half of founders (47%) are still at least moderately burned out, with 18% very or completely burned out, even with the most control and the most upside of anyone in tech. And when we asked whether they’d recommend their path to a newcomer, founders landed at an NPS of –5. That is far healthier than the field’s –39, but it’s still bad. Even the happiest people in tech come out slightly net-negative on telling someone to follow their path.
One caveat: we only surveyed people who are founders today. The ones whose startups failed aren’t represented, and most startups don’t make it. Keep that in mind before you quit to go start something!
Smaller companies are still better places to work than big ones.
Company size predicts sentiment with almost eerie consistency. Walk from the smallest companies to the largest, and every measure of well-being gets steadily worse as the company grows:
People at small companies are more optimistic, less burned out, less worried about layoffs, and even feel AI is helping them more, likely because they have more freedom to actually use it. The “big-company blues” we described last year have settled in.
Look at the absolute numbers, though, not just the slope. Even at the smallest companies, 42% of people are at least moderately burned out, and the would-recommend score never climbs out of the red, sitting at –28 at 1-to-10-person shops. Small companies are winning a race to the least bad.
Where you physically work still hardly matters. There are barely any differences between how fully remote, hybrid, and in-office workers feel. Hybrid workers come out marginally the happiest (and in-office workers rate their managers the worst), but the gaps are small, just as we found last year. Employment type tells a familiar story with one twist. Founders and the self-employed are the happiest and least burned out, while contractors and freelancers are an interesting split—they are among the least burned out (less of the grind) but the most worried about layoffs (no job security).
One wrinkle you may have noticed: the largest companies (10,000+) tick up slightly in optimism and down in burnout compared with the 5,001–10,000 tier, breaking the smooth gradient. But neither difference is statistically significant (5,001–10,000 is our smallest sample), so the line flattens at the top rather than reversing. The one measure that does keep climbing to the very top is layoff worry. Workers at 10,000-plus-person companies are the most worried of anyone in the survey.
One more finding held firm from last year, and it may be the most actionable of all. Manager effectiveness remains the strongest driver of burnout in the entire dataset (it beats role, company size, and AI sentiment), and one of the strongest drivers of everything else. The gradient is dramatic:
Workers with an extremely effective manager report roughly 65% higher job enjoyment and dramatically lower burnout than those with an ineffective one. Yet only 25.5% of tech workers rate their manager as highly effective, while 36.5% rate theirs as ineffective, numbers that have barely budged since last year. The most powerful retention lever in tech is also the most neglected. (Notably, the worst-rated managers cluster in Data/Analytics and Design. The latter is a double blow, since designers are also among the most AI-anxious.)
We asked, “In a sentence, how would you describe the state of the tech industry right now?” About 70% of respondents answered, and the single most common theme, by a wide margin, was chaos: roughly three in 10 explicitly used words like change, chaotic, uncertain, unstable, and in flux. Another one in six described an industry moving too fast to keep up with—treadmills, hamster wheels, hurricanes, “drinking from a firehose.” After that came AI hype and bubble talk (12%) and then, finally, a note of excitement and opportunity (11%).
A few responses capture the sentiment better than any percentage can:
“We’re in the 2nd inning of a massive shift, and no one knows how it will end, but all you can do is keep taking at-bats.”
“It feels like working on pure software is like picking up pennies in front of a steamroller.”
“The industry feels like it has lost its center of gravity—replacing curiosity about customers with an obsession over AI, automation, and efficiency.”
The chaos plus hype is well-described in this quote from a senior PM:
“Manic. Half are out of touch, clinging to the bandwagon, making the problem worse by pouring into the overhype. The other half are exhausted by the first half.” —Senior IC PM
When we ran sentiment analysis on the chaos-related quotes, the split was nearly even: 37% positive, 37% negative, and 26% neutral. The dominant theme is disorientation, but the emotional charge is truly bimodal. The same churn reads as thrilling to one person and terrifying to the next.
We confirmed this by splitting the responses by who wrote them. Career optimists and career pessimists describe the same industry in opposite terms. Optimists reach for “exciting,” “transforming,” “opportunity,” “fast-moving.” Pessimists reach for “chaos,” “layoffs,” “greed,” “dystopia.” Same disruption, opposite forecasts: half the room is anxiously bracing for AI’s impact; the other half is eagerly leaning into the AI era.
The 2026 workforce is more burned out and less optimistic than a year ago, splitting along the fault line of AI into those who are thriving and those who are struggling, and a large, ambivalent middle caught between. Tech workers are mostly afraid of being squeezed by their jobs and increasing productivity expectations, privately convinced the field is no longer worth recommending to newcomers, while individually still finding real power and even joy in the tools. It’s a complicated moment. It’s also not a hopeless one.
So here’s what the data suggests you can actually do about it.
Find something impactful to do with AI—then go deep. The “amplified” are the people who found the two or three tasks where AI measurably changed their output and got very good at those. Trying to use AI for everything is how you end up overwhelmed and conflicted, not empowered.
Watch the squeeze. The biggest career risk is silently absorbing a higher and higher bar for the same pay until you’re burned out. Take our burnout test here, and if your output has doubled this year, talk to your manager about scope and compensation.
Your manager matters more than almost anything. A great manager is associated with about 65% higher job enjoyment and far less burnout. If you have one, protect that relationship. If you don’t, getting closer to a better one may be the highest-leverage career move available to you.
Consider a smaller company—or your own. Every well-being measure in this survey improves as company size shrinks, and founders are the happiest group in tech. More autonomy and control is, year after year, the most reliable buffer against burnout and pessimism we find.
If you’re early-career, find mentors. The rungs are disappearing, but strong mentorship remains highly effective. Seek the teams and managers who still invest in developing people. That investment is rarer and more valuable than it’s ever been.
Invest in managers—it’s still the best money you’ll spend. Only a quarter of tech workers rate their manager as highly effective, and nothing else in the data moves burnout, enjoyment, and retention as much. This was our top recommendation last year. It’s our top recommendation again.
Manage the squeeze. Your people can feel AI raising the bar, and they’re watching to see whether you turn productivity gains into impossible expectations or actual relief. The fastest way to end up with resentment on your team is to pocket the productivity and turn saved time into more work for them.
Don’t let the bottom rung rot. If AI is doing the entry-level work that juniors used to learn on, you’re optimizing this year’s output by starving next year’s senior talent. Be deliberate about how early-career people develop when the old apprenticeship tasks are gone.
Pay special attention to design and research. For two years running, people in these roles report the worst sentiment, the highest AI anxiety, and some of the worst-rated managers. That’s a retention problem and a signal worth understanding before it becomes an exodus.
Treat AI adoption as a sorting risk rather than a productivity win. The same technology is lifting one part of your workforce while destabilizing another. The companies that come out of this ahead will help support the destabilized group instead of leaving them behind.
The throughline, if there is one, is the same as last year’s: Having the most advanced AI or the fanciest offices won’t determine which organizations succeed. Remembering that there are people underneath all this change will—people who, right now, are excited and exhausted, hopeful and scared, often all at once. Those people are watching closely to see whether the future they’re helping to build will still have a place for them.
Huge thanks to the 5,920 tech professionals who shared how they’re really feeling. Your candor is what makes this possible, and it’ll help us keep tracking where the industry is headed. 🙏
Have a fulfilling and productive week.
Noam (and Lenny) 👋
This year’s survey reached 5,920 tech professionals, of whom 5,332 are currently working. All analyses are based on currently employed tech workers.
Role. As with last year, this is a product-centric audience: Product Management 46.9%, Engineering 12.6%, Founder/Executive 9.1%, Design 7.9%, Operations 4.3%, Product Marketing 4.0%, Research 3.2%, Data/Analytics 2.9%, Sales/GTM 2.8%, with a long tail of other functions.
Seniority. A senior crowd: IC–Senior 28.9%, IC–Staff/Principal 19.9%, Director 15.4%, Manager 12.1%, Founder/Exec 10.8%, VP+ 7.4%, and IC–Early career 5.5%. Roughly 54% individual contributors and 46% managers and above.
Company size. A fairly even spread, from 1–10-person startups (11.1%) up through 10,000-plus-person enterprises (18.0%), with the middle bands well represented.
Work setup. Fully remote 47.0%, hybrid 43.0%, fully in-office just 10.0%.
A methodological note for the careful reader: We made year-over-year comparisons only for questions whose wording matched across years. We redesigned much of the survey this year to focus on AI, which means a few 2025 themes (engagement, belonging, quitting intentions, career clarity) aren’t measured here, while others (layoff worry, the AI block, the career-recommendation score) are new. We also didn’t collect age, tenure, or geography this year, so when we discuss career stage, we’re using job level as a proxy.
If you’re finding this newsletter valuable, share it with a friend, and consider subscribing if you haven’t already. There are group discounts, gift options, and referral bonuses available.
Sincerely,
Lenny 👋
2026-07-06 23:01:17
Listen now on YouTube • Spotify • Apple Podcasts
Brought to you by:
Runway—The creative AI platform for images, video, and more
Hyperagent—Deploy fleets of agents that handle real work
Claire puts Anthropic’s new Sonnet 5 through a real benchmark. She builds the How I AI Bench live using Claude Code, then blind-tests Sonnet 5 against Sonnet 4.6, Opus 4.8, GPT-5.5, and Gemini 3 Pro across PRDs, prototypes, agentic tasks, and agent personality. She breaks down what won, what failed, and how builders can create their own repeatable benchmark before trusting the next model release.
Sonnet 5 is priced closer to previous Sonnet models than to Opus, but it doesn’t automatically replace either one. At $2 per million input tokens and $10 per million output tokens through the end of summer, Sonnet 5 sits in an interesting middle band. In Claire’s benchmark, it finished near the bottom of her personal preference ranking, which means the cost argument only holds if the quality argument also holds for your specific use case.
One-off vibe checks feel useful, but they’re not repeatable—and repeatability is what makes a benchmark actually matter. Claire has tested GPT-5.5, open-weight models like GLM-5.2, and others this way before, but she could never compare results across time. The How I AI Bench fixes that by using frozen inputs, a fixed rubric, and the same tasks every time a new model comes out.
Claude Code can read old session history and use it to generate benchmark ideas tailored to a person’s actual work. Claire gave Claude Code a simple prompt asking it to brainstorm eval tasks based on what they’d worked on together, and it pulled from stored sessions on her desktop. Builders can do the same with Codex. That context is sitting there unused for most people.
Building an HTML scoring page to rate outputs based on gut feel and export JSON takes maybe 45 minutes with Claude Code, and Claire argues it’s worth every minute. She scored 64 generations across five models by hand, gave each one a 1-to-5 gut score, added loose notes, and found that the human signal turned out to be the most useful part of the whole benchmark.
LLM-as-judge evals are too generous and cluster toward the middle of the scale. Claire had both GPT-5.5 and Opus 4.8 judge the outputs, and neither was spiky enough. They missed things she flagged immediately on a visual pass, like broken prototypes and ignored wireframe constraints. Models can’t yet see what the human eye catches in the first screenshot.
Claire’s taste and the automated benchmark disagreed almost completely, and she thinks her taste was at least partly right. The LLM judges ranked Gemini 3 Pro highest and Sonnet 4.6 lowest. Claire’s ranking was almost exactly the opposite. When she ran a 70/30 Claire-to-LLM weighted index, Sonnet 4.6 jumped to first. That divergence tells her the eval rubric needs to encode more of what she actually cares about before she can trust the automated scores.
Sonnet 4.6 is still Claire’s choice for daily agent work because of its personality, not its benchmark scores. She pays for API credits to run her OpenClaw on Sonnet 4.6 specifically because she likes how it talks to her. No other model in this test matched it on the voice eval, which asked things like “ugh, deploys are red again” and waited to see how the model responded.
For builders, Claire recommends GPT-5.5 for PRDs, Sonnet 4.6 for prototypes and chitchat, and Opus 4.8 or Sonnet 5 for codebase navigation. Those are the task-by-task recommendations that came out of the Claire-weighted index. Complex, dense UI work is where Opus 4.8 still earns its price premium; for everything simpler, Sonnet 4.6 holds up.
The How I AI Bench is version one, and a lot of it needs to get sharper. The agentic bug-hunting task turned out to be too easy: every model aced it, which means it can’t differentiate between good and great. Claire plans to retire that task, encode more of her taste into the rubric, and keep running the benchmark blind every time a new model drops. The goal is to make this a benchmark the labs actually care about.
Building a Custom Benchmark for Sonnet 5, and Why the Results Surprised Me: https://www.chatprd.ai/how-i-ai/sonnet-5-review-and-custom-benchmark
↳ How to Conduct a Blind ‘Vibe Check’ to Evaluate AI Model Quality: https://www.chatprd.ai/how-i-ai/workflows/how-to-conduct-a-blind-vibe-check-to-evaluate-ai-model-quality
↳ How to Build a Custom AI Model Benchmark Using Claude Code: https://www.chatprd.ai/how-i-ai/workflows/how-to-build-a-custom-ai-model-benchmark-using-claude-code
↳ How to Create a Weighted Index for AI Model Benchmark Results: https://www.chatprd.ai/how-i-ai/workflows/how-to-create-a-weighted-index-for-ai-model-benchmark-results
Listen now on YouTube • Spotify • Apple Podcasts
Brought to you by:
Firecrawl—Power AI agents with clean web data
Jira Product Discovery—Prioritize with insights, build with confidence
Alessio Fanelli, the founder of Kernel Labs and co-host of Latent Space podcast, shows Claire how he manages autonomous coding agents from his phone using OpenAI Symphony, Linear, and a cloud VPS. He walks through the shift from “agent prompter” to “agent manager,” explains why Linear works as a state machine for async agent work, and shares what he’s learned from tracking token costs, purging skills files, and giving agents better senses. He also demos a very different use case: using Codex with browser access to hunt for underpriced Pokémon cards on eBay.
The shift from “agent prompter” to “agent manager” is the unlock most people are still missing. Alessio described how early agentic workflows felt fine until the second or third intervention, when the friction of local runtimes and clunky interfaces killed momentum. Moving to a cloud VPS with multiple communication channels (Linear, shell, mobile) made real async management possible.
Symphony isn’t magic; it’s just a very opinionated Markdown spec that tells the model how to behave. Claire made this point explicitly during the episode, and it’s the most important corrective to the “complex agent orchestration” framing that intimidates people. The whole framework is a Markdown file, and the models are good enough now to lock to it faithfully.
Token cost tracking per task is the primitive that most agent setups don’t have, and it should be table stakes. Alessio showed tasks ranging from 15 million to 221 million tokens, and the 221-million-token job (making an app deployable on Vercel) made complete sense in hindsight. Without that ledger, you have no feedback loop for improving your specs or your tooling.
Purge your skills files every few months or they become a liability. Models have a strong tendency to add instructions rather than replace them, so a skills file that grows over time eventually contradicts itself. Alessio’s advice is to keep files short, tight, and explicit about what the agent needs to ask for, not exhaustive lists of every possible behavior.
AI’s biggest unlocked opportunity is businesses built on heterogeneous data. The category Alessio described—things like trading cards, vintage clothing, and fish inventory—has always been impossible to scale because the data is inconsistent, visual, and contextual. LLMs are the first technology malleable enough to handle that messiness without extensive preprocessing.
Giving agents better senses (screenshots, visual diffs, video) extends autonomous runs dramatically. Kernel Labs built Glimpse, a Playwright extension for coding agents, specifically because the bottleneck wasn’t orchestration but rather agents hitting ambiguity in the UI and calling for help. Better tooling at the perception layer keeps the run going.
Context offloading is an underrated AI use case, and it’s worth building deliberately. Alessio’s email monitoring setup gave him the certainty that nothing important was slipping through, which removed a low-grade background anxiety. The same logic applies to personal finance, inventory, and any domain where staying on top of information is taking cognitive bandwidth you’d rather spend elsewhere.
The Pokémon card demo is the clearest proof that AI is compressing the information advantage that scale used to provide. Finding underpriced PSA-graded cards at $10,000-plus price points was previously a function of having more time, more people, and more domain expertise than competitors. Codex with browser access and a custom pricing skill collapses that advantage to a single well-written prompt.
Small businesses are the most interesting AI story, and they’re being underreported. Alessio’s observation from Japan, that small two- and three-person operations are running happily and profitably, points to a different kind of AI opportunity than the enterprise narrative suggests. The leverage AI gives a one-person operation is asymmetric in a way that bigger organizations can’t replicate at the same cost.
How Alessio Fanelli uses Open AI Symphony for Autonomous Coding and Pokémon Card Trading Workflows: https://www.chatprd.ai/how-i-ai/alessio-fanelli-uses-open-ai-symphony-for-autonomous-coding-and-pokemon-card-trading
↳ Build an AI Agent to Find Underpriced Pokémon Cards for Arbitrage: https://www.chatprd.ai/how-i-ai/workflows/build-an-ai-agent-to-find-underpriced-pok-mon-cards-for-arbitrage
↳ Automate Software Development with an AI Agent Manager using OpenAI Symphony and Linear: https://www.chatprd.ai/how-i-ai/workflows/automate-software-development-with-an-ai-agent-manager-using-openai-symphony-and-linear
If you’re enjoying these episodes, reply and let me know what you’d love to learn more about: AI workflows, hiring, growth, product strategy—anything.
Catch you next week,
Lenny
P.S. Want every new episode delivered the moment it drops? Hit “Follow” on your favorite podcast app.
2026-07-06 20:03:37
Alessio Fanelli, founder of Kernel Labs and co-host of Latent Space podcast, walks us through two very different AI workflows: (1) a fully autonomous coding setup using OpenAI Symphony + Linear, where Linear acts as a state machine and Symphony manages agents through the whole dev lifecycle with zero babysitting; (2) Codex with browser access searching eBay for underpriced Pokémon cards—autonomously browsing, extracting PSA certificate numbers, and flagging deals on $10K–$20K cards for his San Carlos card shop, Merlin Games.
Listen or watch on YouTube, Spotify, or Apple Podcasts
Why “agent manager” is a better mental model than “agent prompter”
Why local Mac Minis don’t scale, and what a cloud VPS unlocks
How to wire Symphony and Linear together as an agent state machine
How to track token costs per task (and what 221 million tokens buys you)
What Glimpse does, and why better agent senses extend autonomous runs
Why your CLAUDE.md probably needs a full purge, not more instructions
How Codex scouts underpriced $10K Pokémon cards on eBay at scale
The new category of small business that AI just made possible
Firecrawl—Power AI agents with clean web data
Jira Product Discovery—Prioritize with insights, build with confidence
(00:00) Intro
(02:24) Prompter vs. agent manager
(04:31) Live demo: Symphony + Linear
(09:31) Setting up Symphony
(14:15) Purging your skills files
(18:06) The benefits of this system
(19:10) Demo: Using Codex to hunt for Pokémon cards
(24:17) The benefit of AI for small businesses
(28:23) Lightning round
• OpenAI Codex: https://openai.com/codex
• OpenAI Symphony (open-source framework): https://github.com/openai/symphony
• Linear (project management/agent state machine): https://linear.app
• PSA (Professional Sports Authenticator) grading: https://www.psacard.com
• TCGplayer (card pricing): https://www.tcgplayer.com
• eBay (used for card price scouting): https://www.ebay.com
• Meta Ray-Ban glasses: https://www.ray-ban.com/usa/ray-ban-meta-smart-glasses
• The Monk and the Riddle by Randy Komisar: https://www.amazon.com/Monk-Riddle-Creating-Making-Living/dp/1578516447/ref=sr_1_1
• The Divine Comedy by Dante Alighieri: https://www.amazon.com/dp/0451208633
• AS Roma (football club Alessio and Claire are both fans of): https://www.asroma.com/en
Latent Space podcast: https://www.latent.space/
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].
2026-07-05 00:58:47
👋 Hello and welcome to this week’s edition of ✨ Community Wisdom ✨ a subscriber-only email, delivered every Saturday, highlighting the most helpful conversations in our members-only Slack community.
2026-07-01 07:22:23
I’ve been testing every major frontier model release since the start of the year, and when Anthropic dropped Sonnet 5, I wanted more than a vibe check. I got tired of one-off tests I couldn’t repeat or compare over time, so I built something better: the How I AI Bench, a repeatable eval harness I constructed live using Claude Code while recording this episode. I ran Sonnet 5 blind against four other frontier models (Sonnet 4.6, Opus 4.8, GPT-5.5, and Gemini 3 Pro) across PRD quality, prototype generation, agentic task completion, and agent personality. The results were not what I expected.
Listen or watch on YouTube, Spotify, or Apple Podcasts
What Anthropic claims Sonnet 5 improves over Sonnet 4.6, and where the benchmark data actually backs that up
How I built the How I AI Bench in under 45 minutes using Claude Code, starting from my own stored session history
Why I combined human vibe scoring (70%) with LLM as judge scoring (30%) instead of trusting either alone
How to set up a local HTML scoring page so you can rate AI outputs on gut feel and export those scores as JSON
Which model I recommend for PRDs, which for complex prototypes, and which for chatting with an agent daily
Runway—The creative AI platform for images, video and more
Hyperagent—Deploy fleets of agents that handle real work
(00:00) Sonnet 5 is out
(01:55) What Anthropic claims
(04:02) Why I’m done with one-off vibe checks
(05:05) Building the How I AI Bench live with Claude Code
(07:42) The scoring system
(10:43) Agent voice eval
(11:57) Quick recap
(13:58) Results: The How I AI index leaderboard
(21:21) What I’m improving for the next run
(22:16) Generating a Claire-weighted index
(23:53) Model-by-task recommendations
• Claude Sonnet 5: https://www.anthropic.com/news/claude-sonnet-5
• Claude Opus 4.8: https://www.anthropic.com/news/claude-opus-4-8
• GPT-5.5 (OpenAI): https://openai.com/index/introducing-gpt-5-5/
• Gemini 3 Pro (Google DeepMind): https://deepmind.google/models/gemini/pro/
• Cursor: https://www.cursor.com/
• SWE-bench Pro (agentic coding benchmark referenced): https://www.swebench.com/
ChatPRD: https://www.chatprd.ai/
Website: https://clairevo.com/
LinkedIn: https://www.linkedin.com/in/clairevo/
Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email [email protected].