Luke Mlody

ai/ml product lead. Previously 2x cofounder/ceo. Occasional DJ. 🇵🇱🇺🇸🇬🇧🇩🇪🇨🇳

[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.

[Luke Mlody @lmlody]

Based Pope.

[Pope Leo XIV @Pontifex]

Connecting young people to digital networks serves no purpose if they remain disconnected from themselves, others, and their own interiority. We must help young people rediscover silence, reflection, the ability to ask questions, the depth of relationships, and openness to transcendence. To listen to the soul, we must lend an ear, because the soul's voice is not a shout, but a whisper.

[Luke Mlody @lmlody]

@typesfast Yeah, I've just been solo traveling through Italy for the last two weeks, and Gemini + Gmaps are absolutely stellar for both planning and tour guiding.

[Luke Mlody @lmlody]

@scaling01 @thesaraharminta They fed the model Adderall?

[Luke Mlody @lmlody]

Excommunicado

[Neil Renic @NC_Renic]

Need the Pope to release an encyclical against Teams calls

[Aaron Levie @levie]

CEOs are uniquely prone to AI psychosis because they’re sufficiently distant from the last mile of work that still has to happen to generate most value with AI.

So when they play with AI, they see the happy path results, often not considering the next 10 or 20 things that have to happen to get sustainable results from agents.

“Look I made this awesome product prototype”. Yes but you didn’t have to review the code before it went into production and fix a bunch of issues.

“Look I generated a contract”. Yes but you didn’t verify all the terms before it goes out to the counterparty and didn’t have to wire up all the past contracts to work with.

The best thing you can do as a CEO is to use AI a *ton* to figure out the real implications of agents in the enterprise, and come out the other side with an appreciation for both the upside and the real work that goes into them.

[Howard Lerman @howard]

Everyone is obsessed with AI making a 10x engineer a 1000x engineer.

The recent reductions at CloudFlare and Click have me me realize the plot is equally about the inverse: AI amplifies the *negative* impacts of poor performers.

If a person with poor taste, who makes mediocore judgement calls, and doesn't properly build things customers love is able to produce 10x more work - does a company want that?

Hell no! Productivity isn't just about as many people as possible tokenmaxxing. AI is a double edged sword, especially when it's used to produce net new work.

If you give a bad artist a pen that can draw 100x as fast, you're going to pile up with a lot of junky artwork very quickly.

And since it happens so quickly leaders are now able to see quickly who is Picasso and who is not and adjust accordingly.

[Aaron Levie @levie]

What’s happened is that we went from AI chat tools that were relatively cheap and had small context windows, to AI agents that have giant context windows, the ability to keep track of longer running work, and models that cost an order of magnitude more on inference because they’re that much better.

This has compounded far faster than most realized (unless you were paying close attention at the middle or end of last year, which many here were), and the dollars flowing in now are much more real.

What follows is a continued march of AI capability that will continue to be used by anyone with a frontier use-case (like coding, sciences, finance, consulting) and then a peeling off of tasks to lower cost models that are capable enough for the job. Whereas we thought the cost of AI might converge on a single low price per token before, it’s clear the stratification is only widening based on the task you need performed.

This will be yet another component that has to be figured out for broad AI diffusion. Enterprises will need to put in programs, new finance teams, and technology solutions to manage this all. The labs and platforms that can ensure customers can price optimize for the task at hand will be in the best position.

[Aaron Levie @levie]

Great post on FDEs. Everyone should read it if you’re interested in this job category. This is a job that is going to be around as long as AI keeps changing rapidly, which it inevitably will.

People often wonder why isn’t this like just deploying other forms of technology in the past, like cloud.

Because something like cloud adoption affected a fairly concentrated set of users (developers and IT), and generally didn’t require a fundamental change to the workflows of employees to get the benefits of the new service being delivered on the cloud. At best you went to one training session and you were done.

With agents, the work to implement them is not only highly technical, but they directly impact the underlying workflows that people participate in. This means there’s a ton of technical work and change management that comes with it.

Further, the pace of change of cloud wasn’t nearly as quick, so there was a lot more time for best practices to propagate. Now, every model change means either something new can be done that wasn’t possible before, or some piece of scaffolding is now redundant or holding you back.

This is why it’s commonly easier for a vendor or partner that’s seen the implementation hundreds or thousands of times help do the work, even with internal support from the customer.

So, this job isn’t going away any time soon, and will be a great path for a lot of technical talent, especially early career.

[Peter Steinberger 🦞 @steipete]

People freaking out over my AI spend. What nobody sees: Part of what excites me so much about working on OpenClaw is that I'm trying to answer the question:

How would we build software in the future if tokens don't matter?

We constant run ~100 codex in the cloud, reviewing every PR, every issue. If a fix on main lands, @clawsweeper will eventually find that 6 month old issue and close it with an exact reference.

We run codex on every commit to review for security issues (as it's far too easy to miss).

We run codex to de-duplicate issues and find clusters and send reports for the most pressing issues.

We have agents that can recreate complex setups, spin up ephemeral https://t.co/Q1NRXLemEy machines, log into e.g. Telegram, make a video and post before/after fix on the PR.

There's codex that watch new issues and - if it fits our documented vision well, automatically create a PR of it. (that then another codex reviews)

We have codex running that scans comments for spam and blocks people.

We have codex instances running that verify performance benchmarks and report regressions into Discord.

We have agents that listen on our meetings and proactively start work, e.g. create PRs when we discuss new features while we discuss them.

We build https://t.co/bmA1XnoB7P to split all our projects into functional units to review and find bugs and regresssions.

We do the same split for security with Vercel's deepsec and Codex Security to find regressions and vulnerabilities.

All that automation allows us to run this project extremely lean.

[BuccoCapital Bloke @buccocapital]

I can’t stop thinking about this post. If you do one thing today, I encourage you to give it a thoughtful, thorough read…

And then commit to never living your life this way. Life has wasted success on the people described in this post.

It really is completely pathetic. They say that comparison is the thief of joy - look no further than this post for validation it is indeed true.

On their deathbed they will realize they have lived their life completely wrong. Don’t let it be you.

[Maurizio @themgmtconsult]

The current premium for FDEs is an obvious symptom of scarcity rather than a sustainable market rate.

We are witnessing a talent grab where compensation is decoupled from long-term unit economics.

But AI platforms will mature, and the bottleneck will move rapidly from technical deployment to organizational adoption - ie, to scale in the services world, these companies must eventually restructure from engineering-heavy orgs to more traditional enterprise models (investing in sales, relationship management, customer success, etc).

At that point, the engineer-as-rockstar arbitrage disappears, and compensation packages will be forced to normalize under the weight of margin pressure (BigAI is already unprofitable).

I've got more news.

Do you remember the mantra "Nobody ever got fired for hiring McKinsey"? That served as a powerful risk-transfer mechanism for the C-Suite.

In the 2020s, "Nobody gets fired for partnering with OpenAI or Anthropic" is becoming the new executive insurance policy.

If the BigAI players can provide both the cutting-edge frontier models and the institutional prestige required for risk transfer, the value proposition for the QuantumBlacks of the world begins to crumble I think.

The technology provider is the gold standard for credibility, so why would clients pay a secondary consultancy for the same level of comfort?

Conversely, System Integrators occupy a much sturdier position imho. While less glamorous, their moat is built on the reality of the value chain: 1) cost efficiency and 2) infrastructure breadth.

SIs operate on volume and optimized delivery models that high-end consultancies cannot match. They handle the "plumbing" (legacy integration, data cleaning, cross-functional maintenance, etc) that BigAI is too lean to touch and boutiques are too expensive to manage.

We will be entering a bifurcated market.

On one end, the Frontier Labs provide the brain and the prestige; on the other, the SIs provide the muscle and the scale.

The middle ground (ie, the high-priced implementation partner) is increasingly looking like a bridge to nowhere.

[Luke Mlody @lmlody]

@lennysan Worse: AI tends to hallucinate toward whatever the user wanted to be true. So DS isn’t just fixing the analysis anymore - they’re telling a PM (or an exec) their pet conclusion is wrong. That’s a much harder job - and a politically risky one.

[Anthropic @AnthropicAI]

New Anthropic research: Teaching Claude why.

Last year we reported that, under certain experimental conditions, Claude 4 would blackmail users.

Since then, we’ve completely eliminated this behavior. How?

[Goodfire @GoodfireAI]

Neural networks might speak English, but they think in shapes.

Understanding their rich *neural geometry* is key to understanding how they work – and to debugging and controlling them with precision.

Starting today, we’re releasing a series of posts on this research agenda. 🧵

[Aaron Levie @levie]

Starting to hire and retrain for new agent engineering roles for *internal* functions to help get more powerful agents working well on critical business processes. I expect this type of role to be a very big deal over time at Box and other companies.

It looks something like an internal FDE, whose job it is to wire up internal systems and get agents working with them effectively. The person will be extremely technical and capable of building secure, governed agents for internal workflows that connect to business systems (like Box, Salesforce, Workday, etc.), and codify workflows in skills.

In some cases this person may understand the business process well enough to do it fully, but in most cases I expect them to work with the business directly in an embedded fashion. Ironically, that may introduce another new role on the business side that is more akin to agent product management for internal processes. The key is that you need technical + process people that can span multiple teams or functions in an organization. It’s not about brining automation to a job, but bringing automation to a process.

This is going to be a very big trend in most companies going forward. Fun to watch the early innings of what this will look like.

[vas @vasuman]

https://t.co/YlOT9k0U7E

[Rohan Paul @rohanpaul_ai]

Andrej Karpathy: "To get the most out of the tools that have become available now, you have to remove yourself as the bottleneck.

You cannot be there to prompt the next thing. You need to take yourself outside the loop. You have to arrange things such that they are completely autonomous.

The more you can maximize your token throughput and not be in the loop, the better. This is the goal. So, I kind of mentioned that the name of the game now is to increase your leverage. I put in very few tokens just once in a while, and a huge amount of stuff happens on my behalf."

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From @NoPriorsPod YT channel (link in comment)

[Andrej Karpathy @karpathy]

This is the the quote I've been citing a lot recently.

[The Curious Tales @thecurioustales]

Your brain is wired to quit at the exact moment you're about to break through.

Most people think they quit because they lack discipline or motivation. They blame their willpower. They assume successful people have some genetic advantage or superior mental toughness.

The real reason runs much deeper.

Neuroscientists at UC San Diego studied brain scans of people learning complex motor skills over several months. They discovered something counterintuitive: during the weeks when learners felt most frustrated and considered quitting, their brains were undergoing the most dramatic structural changes. New neural pathways were forming at accelerated rates. Myelin sheathing around neurons was thickening rapidly. The very period that felt like stagnation was actually when the most profound rewiring was happening.

The participants had no conscious awareness of this transformation. Subjectively, they felt stuck. Objectively, their brains were rebuilding themselves.

Your nervous system interprets sustained incompetence as a survival threat. When you attempt something new and fail repeatedly, ancient circuits fire that once kept your ancestors alive by making them avoid dangerous situations. The same neural pathways that prevented early humans from repeatedly approaching predators now prevent modern humans from repeatedly approaching challenges.

Competence feels safe. Incompetence feels like death.

Every time you miss the shot, fumble the presentation, or write garbage, your amygdala sends distress signals. Your brain floods with cortisol. Your body creates the same physiological experience it would create if you were being chased by something that wanted to kill you. After days or weeks of this neurochemical assault, quitting feels like escape from genuine danger.

But what the UC San Diego researchers revealed changes everything about how we should interpret that discomfort. The biochemical chaos you feel during extended periods of failure is actually evidence that deep learning is occurring. Your brain consumes massive amounts of energy to build new neural architecture. The exhaustion, frustration, and sense of being overwhelmed are byproducts of construction, not signs of inadequacy.

People who master difficult skills have accidentally discovered something profound: they've learned to interpret the discomfort of incompetence as evidence they're in exactly the right place. They've trained themselves to recognize the specific feeling of neural restructuring and chase it instead of avoiding it.

The shift is so subtle most people never notice it happening. But once it clicks, the entire relationship with difficulty inverts.

Watch someone who genuinely enjoys the learning process. They don't celebrate successes the way normal people do. They celebrate failures that teach them something. They get excited by obstacles that reveal gaps in their understanding. They treat confusion as information, not as evidence they should quit.

They've rewired their internal reward system to crave precisely the experiences most people avoid.

What makes this psychological rewiring possible is understanding that competence emerges from chaos, not from clarity. Your first attempts will be embarrassingly bad because your brain is literally constructing the neural infrastructure required for skill. The timeline for moving from "terrible" to "decent" is always longer than you expect because biological change operates on its own schedule.

Most people never reach competence because they interpret the gap between where they are and where they want to be as evidence they're not cut out for it. They quit during the exact window when their brain is doing the rewiring that would eventually make them good.

The secret is learning to love that window. The period that feels like failure is actually the period when your brain is working hardest on your behalf. The discomfort you're avoiding is the discomfort of becoming someone new.