Kanjun 🐙

helping humans fight Moloch. CEO @imbue_ai. support founders @outsetcap.

[Kanjun 🐙 @kanjun]

Twitter’s algorithm is optimized for addiction, not for us. We deserve better.

We’re releasing Bouncer today so you can take back control of your feed. Describe what you don't want, and Bouncer removes it.

It’s free, doesn’t collect your data, and will be open source soon.

[Kanjun 🐙 @kanjun]

@v_raja_ @benedictevans Whether it's 3 years vs. 10-15 years does not make the difference between "this is another industrial revolution, let's upskill" vs. "we need to rethink our economy because the labor class is not going to be valuable"

[Kanjun 🐙 @kanjun]

“AI is only as big a deal as the internet or mobile” doesn't seem like a claim that will age well.

What @benedictevans perhaps doesn't account for is that model capabilities have no ceiling. Friends at Anthropic believe Claude will outperform them by 2029, and there’s no fundamental reason why models won't keep getting better, except for limits on compute and data. [1]

It’s comforting to think this will be just another technology wave, but I think something much more radical is in store for our society, and it’s honestly kind of irresponsible to convince people it’ll be business as usual.

I don't think this means we should panic. But it means we should take seriously the problem statements that are coming, e.g.:

1) Market incentives drive AI labs to grow at all costs, so "thoughtful deployment" is wishful thinking. We need to attack the underlying growth incentive structure.

2) It's not clear how economically useful humans will be in the future. Given this, people in the labor class will have a lot less leverage relative to capital. Capital will beget more capital, so it will concentrate. We need to think seriously about where an individual's leverage will come from, economic or otherwise, else we'll lose our freedom and autonomy.

We should consider that we all live in a society, not just an economy.

3) Our legal environment is currently unable to regulate internet technologies well, let alone AI. This is partly because our laws are predicated on outdated ideas of how the world works. Amazon, Google, Meta have somehow managed to escape serious antitrust cases. @linamkhan was one of the first to question some of these assumptions, in Amazon's Antitrust Paradox. We need more serious rethinking on how to handle vertical integration, bundling, interoperability/portability, information collection, distribution advantages, and the variety of other issues that have led to software companies extracting from users the past 10 years.

This is obviously not a comprehensive list of problem statements, but I'd be more excited to see this kind of thinking/work around AI, rather than "this is just like prior waves of automation; there will be displacement and people will need to upskill".

--
[1] When there are data limits, there will be huge market demand for more such data — we already see this with expert data providers like @SnorkelAI. And the world is building compute as fast as it can, with chips more optimized for LLM training/inference, like MatX.

[Lenny Rachitsky @lennysan]

My biggest takeaways from @benedictevans:

1. We’re in 1997 for AI—it’s as big a deal as the internet or mobile, and only as big a deal as the internet or mobile. We’re at the stage where most stuff kind of doesn’t work yet, most of what people will build hasn’t been built, and it’s not clear how any of it will work when it does. Some people in tech have bought clusters of Mac Minis, while even among 13-to-18-year-olds, only about 15% to 20% are daily active users of AI. The companies that win may not exist yet, and the use cases that matter most are probably invisible to us today.

2. Every technology wave brings ways to ruin people’s lives, deliberately or by accident, and we need to be conscious of that without panicking. Every wave of technology—databases in the 1970s, social media in the 2010s, AI today—creates new ways to harm people. We need to be conscious of these risks, build safeguards, and hold people accountable. But we also can’t let fear of potential harms stop us from capturing the benefits. The goal is thoughtful deployment, not paralysis.

3. Things will probably be okay—but “on average” hides a lot of individual pain. We’ve been automating jobs and creating new jobs since 1800. Each time, you can see the jobs that will disappear but not the new jobs, because they don’t exist yet. We go through frictional pain, dislocation, people lose jobs, towns get hollowed out, and it all sucks. But we come through richer, and we’re not worried about crops failing anymore.

4. If you’re worried about your job, the worst thing you can do is stick your head in the sand and declare AI evil. Yes, some professions face major questions, particularly if you’re an associate or would have been thinking about becoming one. The pyramid structure of professional services may fundamentally change. What helps is submerging yourself in AI, understanding what you can do with it, how it changes things, and how you can be a great hire in this new environment. That may still not be enough, but it’s the only path forward.

5. The history of accounting shows us how automation often increases employment rather than decreasing it. Despite adding machines, punch cards, mainframes, databases, ERP systems, cloud software, spreadsheets, and PCs, the number of accountants keeps going up. This is the Jevons paradox: when you make something cheaper or easier, you don’t do the same amount of work for less money. You often do vastly more because the ROI changes.

6. Distribution is becoming a more valuable moat as software gets easier to build, which favors incumbents. As AI makes building software cheaper and faster, the market gets noisier. More products launch, more companies compete for attention, and breaking through becomes harder. This means distribution—the ability to reach customers and get them to use your product—matters more than ever.

7. Foundation AI model companies won’t have lasting pricing power, and value will likely accrue up the stack. The models don’t seem to have network effects, so there’s no winner-takes-all dynamic. If you have indefinite competition between three to six foundation model providers, and the models look like undifferentiated commodities to users, why would anyone have pricing power? The current pricing chaos—people spending $1.5 million on inference in a month—is temporary disequilibrium, like someone getting a $50,000 mobile data bill in 2010. The steady state will look different.

8. OpenAI and Anthropic are buying consultancies and PE firms. This seems counterintuitive—aren’t these the companies that should need consultants least? But the reality is that companies don’t have people sitting around waiting to reimagine all their internal workflows and figure out which could be automated with AI. That’s a project requiring five to 10 people spending months working it out, then actually implementing it across vertical and horizontal systems.

9. The fundamental question isn’t whether AI automates your job—it’s whether your profession is a "task" or a job. Some jobs are just tasks, and when you automate the task, the job disappears (i.e. elevator attendants). But in most professions, the task you think you’re being paid for isn’t actually what you’re being paid for. McKinsey doesn’t get hired to produce a 75-slide deck—they get hired to walk through your enterprise, understand the politics, talk to customers, and figure out what you actually need to do. The deck is just the artifact.

10. The anti-AI backlash is real, and a fuzzy mass of different concerns, some real and some not—much like the social media backlash. There are tangible concerns: electricity bills went up in some places, though this applies to very few locations objectively. The water consumption issue is largely false; data centers use about 0.017% of U.S. water consumption. There are real questions about jobs, though economists can’t yet find clear consensus in the data about AI’s employment impact. There’s also the culture war over AI-generated content and “AI slop.” The challenge is that all of this creates political pressure even when the underlying facts are unclear or contested.

[Kanjun 🐙 @kanjun]

@v_raja_ @benedictevans I agree that we have ~10-15 years for the transition, but that's an extremely short amount of time to change a huge number of economic and incentive issues, with a super powerful technology that has all our information and can be modified at will by the entity that owns it :/

[Kanjun 🐙 @kanjun]

@tuhin I really like how you framed this. It's what I've been focusing on at @imbue_ai — figuring out what it takes to shift our political, economic, and moral structures to support humans in living in a *society* given the post-AI economy.

[Kanjun 🐙 @kanjun]

@luminxbt Truth, new memes will emerge

[Kanjun 🐙 @kanjun]

I wish we could emoji react on X posts instead of only being able to 🩷 them

[Kanjun 🐙 @kanjun]

@justgrm See I want to react 😂 to this but I can only heart

[Kanjun 🐙 @kanjun]

@karrisaarinen Resonate a lot with this. I made the mistake a lot of young founders do, which was to keep pushing through the resistance — that's what YC lore says, right? — but it was actually both the world and my intuition telling me something was wrong, and I was ignoring the data.

[Kanjun 🐙 @kanjun]

I love Karri's take on tech culture:

"I sometimes wish we could move the culture more toward a Zen master.

Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort."

Reminds me of the Daoist tale of the butcher who, instead of cutting forcefully through the bone, cuts at the joint, finds in each moment the area of least resistance, and moves in that direction.

I used to lead Imbue with a lot of forcefulness. This year I've been trying out the Zen/Daoist route of finding where the energy wants to go and following it, and it's both much more effective and more fun.

[Karri Saarinen @karrisaarinen]

The fallacy of this is that more creates more. More hours, more hiring, more something.

And it is true in a sense. If you put in more work, more work will happen. But I think for most startups, the leverage is really in how differently you approach the problem, how well you cultivate your team, and the strategy.

Any large company can outspend you on hours. They have thousands or tens of thousands more people, spending more hours. If hours worked were the metric, every large company and government organization would always win and do the best work. More hours, better output.

This thinking is often representative of younger founders, where the startup becomes their identity and life. They have a hard time doing anything else, and cannot understand that your work is not the person that is you. But activities outside of work can grow you as a person too and make you do better work.

I’ve never worked this way. As a designer, I always saw the need to take a step back, to take a break. At times, I might work 12 hours or 16 hours, or whatever amount was needed, but it wasn’t the norm. You just can't grind design, you need inspiration. But taking that step away from the work, would give me more perspective, inspiration and I could approach the problem differently or I could just see the solution.

Grinding is never good for any creative problem, and startups or creating new products are often mostly about creative problem solving. Grinding works ok for email jobs, or where you just executing on very clear playbook.

With Linear, we’ve never worked this way. We work reasonable hours, 5 days a week. All of us founders have families. Many of our employees have families. I personally stop every evening, spend time with the family, cook dinner for the family, eat dinner together, and focus on things outside of work. Sometimes I work in the late evenings or weekends, but to me the pride is that I don’t need to. Company should be succesful without it.

My goal is to build a company that is sustainable in the long term, and doesn’t require heroics or personal sacrifices every single day.

There are times when our team is heroic. Launches, incidents, some other work that just needs to be done. They will work late into the night because they know it is the right thing. But we don’t require that every day or every week, and the more this happens, the more I think it is a failure of our company and leadership. The team and the leaders should always keep a reserve to use when something is needed.

Our thinking was also that quality, which we value, doesn’t emerge from working more or stressing people more. It emerges when you create the conditions for it to emerge. Often it is the appreciation, space, time, and how the person feels. A person who is rested will do better work.

I wouldn’t attribute much of our success to working a lot. The success came from having clear thinking, ideas, and focus to do the right things.

I sometimes wish we could move the culture more toward a Zen master.

Real mastery is not exerting the most effort. It is achieving the outcome with the least necessary effort.

[Kanjun 🐙 @kanjun]

Full video of our Raising Kids in the Age of AI event!

[Imbue @imbue_ai]

Friction helps kids learn.

So what happens when AI answers before they have a chance to wonder? 💭

In April, we brought together a group of experts to explore how AI is reshaping childhood, curiosity, and learning:

• @nikunj Partner @fpvventures
• @celestekidd Professor of Psychology @UCBerkeley
• @sonjasg Professor of AI Ethics @UF
• Ari Krakowski Director of Exhibit Services @lawrencehallsci

Watch the conversation, moderated by @lauren__dash, Head of Brand at Imbue.

0:00:00 Why curiosity matters for kids
0:01:12 Meet the panel
0:07:35 What AI shortcuts in learning
0:08:38 Celeste's Bigfoot story
0:14:31 Creativity, STEM, and childhood
0:21:48 Teaching AI literacy
0:25:03 Raising skeptical thinkers
0:31:18 AI companions and kids
0:40:52 Safety, regulation, and children
0:43:43 Where AI can actually help kids
0:46:05 Teaching kids to think, reason, and act
0:49:07 Preparing children for an AI future
0:57:05 The case for hope, building better technology for kids

[Imbue @imbue_ai]

mngr has 12+ plugins that help developers

👉 pull files from stopped agents
👉 schedule nightly code reviews
👉 sync changes with a remote agent
👉 track everything from a CLI Kanban board

We broke down every plugin, what it does, and when you'd actually reach for it.

🔗 https://t.co/B1Fs02YkBE

[Kanjun 🐙 @kanjun]

this is a lovely reflection on something I've struggled with (making ideas legible)! I like the idea of getting others to reflect things back in phrases legible to them.

fwiw, having read your Substack, I was quite (pleasantly) surprised at the way you articulated yourself in the Notion doc — it was very commercial-literate while retaining a lot of voice and personality

[Kanjun 🐙 @kanjun]

@majamediaco @10footinvestor trying! ;)

[This Week in AI @ThisWeeknAI]

"Big tech doesn't want to train people anymore. They're taking their 10x employees and making them 11x, 12x, 13x." - @Jason

This week's roundtable:
1. Kanjun Qiu @kanjun, CEO of @imbue_ai, open source agents.
2. Jeremy Fraenkel @fraenkelj, CEO of Fundamental, large tabular models for enterprise data.
3. Karri Saarinen @karrisaarinen, CEO of @linear, product development system for teams and agents.

TWiAI Episode 14 out now

[Kanjun 🐙 @kanjun]

@SP1NS1R @majamediaco @ASalyers3 @jaesmail @paulscherer @startingfromnix @aishdoingthings much gratitude, thanks @SP1NS1R :)

[Kanjun 🐙 @kanjun]

I'm excited for Michelle to be leading our events full-time! Stay tuned for a punk software hackathon ;)

[Michelle Simonek @MichelleSimonek]

After a year and a half at @imbue_ai, I’m now our Community & Experience Lead—building community around our mission to make tech serve humans, not the companies that built it.

I’m excited to meet more people building in the space, and host you at a future event. Say hello! 👋🏻

[Kanjun 🐙 @kanjun]

@ExaAILabs Huge congrats, Will! So proud and excited for you :)