ai/ml product lead. Previously 2x cofounder/ceo. Occasional DJ. 🇵🇱🇺🇸🇬🇧🇩🇪🇨🇳
Joined November 2009
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Luke Mlody retweeted
In July we open-sourced a dynamic graph of everything a child learns in primary school. 1,590 concepts, 3,221 dependencies.
Now we’re turning every concept into interactive experiments kids can play and learn from.
First one, age 7: heat and water.
Everything that used to be a boring textbook can now be an engaging experience, potentially tailored to every child.
Kids education just got a massive upgrade.
Luke Mlody retweeted
I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic. Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives. More thoughts below.
Luke Mlody retweeted
Wonderful has raised a $550M Series C at a $5 billion valuation.
This latest funding was led by @insightpartners, with @salesforce joining as new investors and @IndexVentures, @BessemerVP, @IVP, @VineVenturesLP, and @9yardscapital returning.
In 20 months since launch, we're in production with hundreds of agents, systems, and workflows across a dozen verticals in over 30 markets.
With this new capital, we’ll be overdelivering for our customers, hiring hundreds of cracked engineers, FDEs, and former founders, and expanding our product capabilities across the entire enterprise.
Stay tuned 👀
Luke Mlody retweeted
Really believe that ADHDs, if they play their cards well, might be the biggest winners of this AI gold rush
Cause unlike corporate careers rewarding consistency & compounding, the new world disproportionally rewards curiosity, obsession, rapid switching and intensity
The basic problem with ADHD in the old world was that curiosity was expensive. You’d get obsessed with some random idea, spend 3 days learning everything about it, want to build something, then realise you need 6 months to learn how to code / design / whatever (or pay someone who can) and eventually move onto the next thing
But AI completely changes that equation because the distance between “wait this would be cool” and actually testing it has collapsed from months to sometimes hours. You can get curious about some niche industry in the morning, understand the basics by lunch, vibe code something in the afternoon and potentially have people testing it that evening. So this disproportionately rewards people whose natural state is basically jumping between rabbit holes, connecting unrelated things and occasionally becoming insanely obsessed with one of them
AI is also slowly deleting the boring middle. A lot of ADHD people aren’t necessarily bad at starting things or going insanely deep when something captures their attention, they’re bad at the 200 repetitive steps between “this is exciting” and “this is finished” (boilerplate, formatting, basic debugging, admin, iteration, etc.) And those are increasingly exactly the things you can delegate to AI, which means the economic value of being insanely curious + occasionally obsessive probably goes up, while the cost of being bad at repetitive execution goes down
But there’s a massive catch, because AI is also creating the greatest distraction machine ever invented. Every morning there’s a new model, agent, tool, business model or “once in a generation opportunity” to chase, and the same brain that benefits most from cheap experimentation is also the one most likely to spend 5 years being extremely early to everything and compounding absolutely nothing
So the actual winning combination might actually be ADHD-style exploration + artificially imposed constraints: explore aggressively, follow weird rabbit holes, test way more things than a normal person would, but once something works force yourself into sprints where you’re simply not allowed to play another game
For most of history the economy disproportionately rewarded people who could reliably do the same useful thing for 8 hours a day, 250 days a year. For this weird period of AI, the returns temporarily shift towards people who can obsess over 20 things, connect 2 that nobody else connected, then disappear for 3 days and build the thing
message to all ADHDs:
you just need to hack your brain's dopamine to finish a week's work in 5 hours.
your prefrontal cortex is understimulated. ADHD means your brain has lower baseline dopamine activity so it constantly hunts for stimulation
but every productivity system was built for neurotypical brains that can sit down and grind 8 hours. yours can't. yours needs intensity - urgency - rewards
here's what actually works with ADHD:
- externalise everything: the reward has to be visible and immediate. timers, streaks, session logs. turn invisible effort into something you can see after every sprint
- work around people bc ADHD brains respond to perceived observation. working in silence alone is the worst possible setup. when someone can see your output = spikes and focus sharpens (this is why ADHD kids do homework better in the kitchen than their bedroom)
- do sprints (cc @aporia9n) only. your dopamine system rewards finishing. 25 to 90 minute blocks with a clear end point. dedicate a part of your year to work, another to chill, rest. split your month
- make a leaderboards where you see your friends or colleagues grinding + concrete missions
we will never remove dopamine from social media. so we have to inject it into work instead
the trick is making your brain crave the work session the way it craves opening X or TikTok.
if you want something built for this, try Promethee. it tracks work, gives XP for sessions, and puts you on a leaderboard with other builders. just ask below and i'll give you access code
Luke Mlody retweeted
I finally understand what Machiavelli meant when he said, "Never play fair in a game where others cheat." It doesn't mean become evil. It means stop being naive. Stop bringing honesty to people who study manipulation, stop giving access to people who weaponize closeness, and stop expecting clean hands from people who already showed you they'll throw dirt. Sometimes wisdom is not revenge. Sometimes wisdom is learning the rules of the room before the room uses your goodness against you.
This quoted post is unavailable.
Luke Mlody retweeted
we just made research index 100% free
we also added 41M+ life science papers. your agent can search them all. no API cost
available now
Luke Mlody retweeted
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.
"If you are not working 7 days per week, you are going to lose".
Corgi Insurance is the most intense workplace culture in startups.
- The company works 7 days per week.
- Founder (@nico_laqua) lives and sleeps in the office.
- He built a cafe in the office because there was no local cafe that was open 24/7.
- 2/3 of the first 30 team members have the Corgi logo as a tattoo.
Today I went behind the scenes with Nico, who has used this culture to scale the company to a $2.6BN valuation in just two years.
My condensed notes below:
1. If You Are Not Working 7 Days Per Week, You Are Going to Lose:
Whatever you can get done in 5 days, you'll get more done in 6 and 7. If you are trying to solve the world’s hardest problems, a standard 5-day workweek will not cut it.
2. Work Trials Repel the Mediocre:
Corgi forces candidates into mock work trials over the weekend. If seeing a full office on a Saturday scares them, they don't belong. True intensity acts as a natural filter to attract killers and repel clock-watchers.
3. Lead from the Front Lines
You can’t demand 7-day weeks while sitting on a yacht. Nico sleeps 3–4 hours a night on a mattress inside the office. If you want your troops to bleed, you have to be in the trenches with them.
4. Culture Only Means One Thing: Winning
Forget superficial jargon like "hackers" or "ex-founders." Strip away the corporate fluff. A great startup culture is aggressively optimized around one single word: Winning.
5. Lifespan vs. Victories
Building something world-historic requires radical sacrifice. When asked if he'd rather build a trillion-dollar company and die at 50, or fail and live to 80, the answer was easy. "I would rather measure my lifespan in victories."
6. Reject the Comfort of "Quiet Quitting."
If you are operating in a hyper-growth environment and your days off happen to be Saturday and Sunday every single week, you are quiet quitting. To win, you must deliberately bypass the off-ramps of personal comfort and low volatility.
Corgi isn't for everyone—and that’s exactly the point.
Based Pope.
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 retweeted
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.
Luke Mlody retweeted
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.
Today we reduced headcount by 22%. The business is the strongest it's ever been. So I think it's important to be direct about what I'm seeing and why.
First, I made this decision and I own it. I did it because the way to operate at the highest level of productivity is changing, and to win the future, ClickUp needs to change with it.
Second, this wasn't about cutting costs. Most savings from this change will flow directly back into the people who stay. We'll be introducing million-dollar salary bands. If you create outsized impact using AI, you'll be paid outside of traditional bands.
Most importantly, I have the deepest gratitude for those affected. We're doing this from a position of strength specifically so we can take care of people properly. Everyone affected receives a package aimed at honoring their contributions and easing the transition.
I only see two options: wait for this to play out gradually in the market or be honest about what I'm seeing and act proactively.
THE 100X ORGANIZATION
The primary change is that we're restructuring around what I call 100x org. The goal is 100x output. The roles required to build at the highest level are fundamentally different than they were a year ago.
Incremental improvements to existing systems won't get us there. We need new ones. That means creating enough disruption to rebuild rather than iterate on what's already broken.
The common narrative is that AI makes everyone more productive. It doesn't. Many of the workflows of today, if left unchanged, create bottlenecks in AI systems.
These roles will evolve. But waiting for that to happen naturally means falling behind now.
The 100x org is actually heavily dependent on people - infinitely more than today. This is only possible with 10x people that have embraced and adopted new ways of working.
THE BUILDERS, AGENT MANAGERS, AND FRONT-LINERS
— THE BUILDERS: 10X ENGINEERS
I don't think most companies have internalized what's actually happening with AI in engineering. The common narrative is that AI makes all engineers more productive. That may be true in isolation, but at an organization level - that is the farthest thing from reality.
Here's what we've validated recently at ClickUp: the great engineers, the ones who can orchestrate, architect, and review, are becoming 100x engineers. They're not writing code. They're directing agents that write code. The skill is judgment.
AI makes the best engineers wildly more productive, and everyone else using AI slows these engineers down.
Think about it - the bottlenecks are (1) orchestration - telling AI what to do, and (2) reviewing - what AI did. Everything is leapfrogged and no longer needed.
So who do you want orchestrating and reviewing code?
And how do you want your best engineers to spend their time?
If your best engineers are spending time reviewing other people's code, then this is inherently an inefficient bottleneck. These engineers can review their agent's code much faster than reviewing human code.
The new world is about enabling your 10x engineers to become 100x.
The wrong strategy is to push every engineer to use infinite tokens. Companies doing this are celebrating 500% more pull requests. But customer outcomes don't match the volume of code being generated.
I call this the great reckoning of AI coding, and every company will face this soon if not already.
More code is just another bottleneck to the best engineers, and ultimately to your company's impact as well.
— THE BUILDERS: 10X PRODUCT MANAGERS
Product management and design roles are merging.
Designers that have customer focus, become more like product managers.
And product managers that have intuition for UX become more like designers.
The bottleneck of user research is gone. It takes us just one mention of an agent to kickoff research and analyze results.
The bottleneck of product <> design iteration is also gone. The product builder iterates on their own, along with agents and skills that ensure alignment with quality and strategy.
Also controversial today - I believe that the wrong strategy is to have your PMs shipping code - that just introduces another bottleneck that the best engineers will waste their time on.
To be clear, PMs should be coding but they should do this in a playground to iterate, validate, and scope. That code should not go to production.
Everything outside of managing systems, orchestrating AI, and reviewing output becomes a bottleneck.
That's why the other roles that are critical along with these are the systems managers (to reduce bottlenecks) along with a bottleneck you can't replace - customer meeting time.
— THE SYSTEM MANAGERS
Ironically, the people that automate their jobs with AI will always have a job. They become owners of the AI systems - agent managers. We have many examples of these people at ClickUp.
The underlying systems in which we operate are absolutely critical to get right. I think most companies are delusional to think they can iterate on existing systems and compete in this new world.
You must create enough disruption so that old systems are deprecated entirely. If there's any definition for 'AI native' that's what it is.
— THE FRONT-LINERS
In a world that will become saturated with AI communication, the human touch will matter more than anything to customers.
This is a bottleneck that you shouldn't replace - even when agents are high enough quality to do video meetings.
One-on-one meeting time with customers is something that shouldn't be automated. The systems around the meetings should be - so that front-liners spend nearly 100% of their time with customers.
REWARDING 100X IMPACT
In a world where companies are able to do so much more with less, where does that excess money go?
In our case, much of the savings in this new operating model will flow directly back to those that enabled it.
We must reward people that create productivity accordingly. This aligns incentives on both sides. Plus, in a world where your best people create 100x impact, you can't afford to lose them.
You should aim to retain these employees for decades. The context they have and their ability to efficiently orchestrate and review will be nearly impossible to replace.
Compensation bands of today should be thrown out the door. We're introducing $1 million cash/year salary bands with a path available to nearly everyone in the company if they produce 100x impact by creating or managing AI systems.
THE FUTURE
Nearly every company will make changes like these. The ones that do it proactively will define what comes next.
The future is not fewer people. It's different work, new roles, and better rewards for those who embrace it. We're already seeing entirely new roles emerge, like Agent Managers, that didn't exist a year ago.
ClickUp is positioning to lead this shift, not just internally, but for our customers too. I've never been more certain about where we're headed.
Luke Mlody retweeted
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.
🦔Microsoft canceled its internal Claude Code licenses this week after token-based billing made the cost untenable, even for a company with effectively infinite cloud resources. Uber's CTO sent an internal memo warning the company burned through its entire 2026 AI budget in just four months. American AI software prices have jumped 20% to 37%, and GitHub (owned by Microsoft) is dropping flat-rate plans for usage-based billing across its products.
My Take
The AI subsidy era is ending in real time. The same company that put $13 billion into OpenAI and built the Azure infrastructure powering most of Anthropic's compute just looked at the bill from a competitor's coding tool and decided it was not worth paying. That is not a productivity failure on Anthropic's end. Token-based pricing is forcing every enterprise customer to confront the actual cost of running these models at scale, and the number turns out to be far higher than the flat-rate experiments suggested.
This ties directly to my Gemini Flash post yesterday. Anthropic, OpenAI, and Google all raised effective prices in the last six months. Enterprises that built workflows assuming AI costs would keep falling are now watching annual budgets evaporate in months. Two outcomes look likely from here. Either enterprises scale back AI usage to fit budgets, which slows the revenue ramp the labs need to justify their valuations ahead of IPOs, or the labs cut prices and absorb the losses, which makes the unit economics worse at exactly the wrong moment. Both paths land in the same place, the numbers stop working, and somebody has to take the writedown.
Hedgie🤗
Luke Mlody retweeted
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.
Luke Mlody retweeted
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 crabbox.sh 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 clawpatch.ai 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.
Luke Mlody retweeted
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.
The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen.
Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope AI estimation).
Everyone outside that group feels like they can work their well-paying (but <$500k) job for their whole life and never get there.
Worse yet, layoffs are in full swing. Many software engineers feel like their life's skill is no longer useful. The day to day role of most jobs has changed overnight with AI.
As a result,
1. The corporate ladder looks like the wrong building to climb.
Everyone's trying to align with a new set of career "paths": should I be a founder? Is it too late to join Anthropic / OpenAI? should I get into AI? what company stock will 10x next? People are demanding higher salaries and switching jobs more and more.
2. There’s a deep malaise about work (and its future).
Why even work at all for “peanuts”? Will my job even exist in a few years? Many feel helpless. You hear the “permanent underclass” conversation a lot, esp from young people. It's hard to focus on doing good work when you think "man, if I joined Anthropic 2yrs ago, I could retire"
3. The mid to late middle managers feel paralyzed.
Many have families and don't feel like they have the energy or network to just "start a company". They don't particularly have any AI skills. They see the writing on the wall: middle management is being hollowed out in many companies.
4. The rich aren’t particularly happy either.
No one is shedding tears for them (and rightfully so). But those who have "made it" experience a profound lack of purpose too. Some have gone from <$150k to >$50M in a few years with no ramp. It flips your life plans upside down. For some, comparison is the thief of joy. For some, they escape to NYC to "live life". For others still, they start companies "just cuz", often to win status points. They never imagined that by age 30, they'd be set. I once asked a post-economic founder friend why they didn't just sell the co and they said "and do what? right now, everyone wants to talk to me. if i sell, I will only have money."
I understand that many reading this scoff at the champagne problems of the valley. Society is warped in this tech bubble. What is often well-off anywhere else in the world is bang average here.
Unlike many other places, tenure, intelligence and hard work can be loosely correlated with outcomes in the Bay. Living through a societally transformative gold rush in that environment can be paralyzing. "Am I in the right place? Should I move? Is there time still left? Am I gonna make it?" It psychologically torments many who have moved here in search of "success".
Ironically, a frequent side effect of this torment is to spin up the very products making everyone rich in hopes that you too can vibecode your path to economic enlightenment.
Luke Mlody retweeted
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 retweeted
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. 🧵
Luke Mlody retweeted
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.