
aggressively curating (see reposts) current: @superhuman Past: @copy_ai @futurism Watch my videos below 👇
New York City
Joined November 2011
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augmenting human intellect
when i started making software, i wasn’t dreaming of a factory.
i loved that something on a screen could become an extension of my mind. a place to put a thought before i understood it. to move things around until new connections appeared. to make little systems that worked the way i worked, even if it made no sense to anyone else.
the computer wasn’t interesting because it could do work without me. it was interesting because of what i could do with it.
the early lineage of computing was built around this possibility. in 1962, Engelbart described augmenting human intellect as helping a person understand complex situations and find solutions that were previously out of reach. his starting point was the human being, not the autonomous machine.
he wasn’t describing a faster black box. he was looking at the whole relationship between a person, their tools, their language, their methods, and what they could learn to do with them. the capability belonged to that whole system.
somewhere along the way, we started telling a much smaller story about a much more powerful technology.
more automation. more agents. more software producing more software. more output from fewer people. more growth. more numbers to prove that something is happening.
and less attention to the person any of this is supposed to be for.
i don’t want to pretend computing had a pure innocent beginning. replacing human labor was one of its ambitions. even in 1960, Licklider distinguished human-computer symbiosis from systems where the remaining people mainly served the machine. he wanted computers to participate in the formation of ideas, not only execute problems already defined.
that distinction feels important – does the system help you discover what you mean? or does it only want you to submit a request, and wait outside while it works?
sometimes waiting is exactly what i want. please do the search. move the files. take away the repetitive work.
but not everything difficult is overhead.
sometimes moving the pieces around is how you understand them. sometimes making the thing changes what you wanted to make. sometimes you need to see the wrong version, touch it, take it apart, and discover a better question.
i want tools that leave room for that relationship.
in his 1981 paper on Smalltalk, Ingalls described a system meant to support everyone’s creative spirit. personal mastery mattered: an individual should be able to comprehend the system, rather than remain dependent on something permanently beyond their reach.
he also argued that computer models should fit the human mind, rather than demand the reverse.
not one perfect workflow for everyone. systems that different people can inhabit differently. someone who thinks through pictures. someone who needs to write. someone who understands a problem by building a simulation. someone whose way of organizing their life doesn’t resemble a SaaS dashboard.
a system you can reshape as your understanding changes.
this is what i want AI to make possible. not just pumping out software, but having a living relationship with it. being able to ask why something works this way. change one part without surrendering the rest. see what happened. undo it. learn enough to make the next change yourself.
the machine can become more capable without making the person smaller.
but that won’t happen just because we make the models better. we have to care about it. design for it. notice when we are making someone more dependent while calling it empowerment.
i don’t want to measure the future only by how much work happens without us.
i want to know what becomes possible for the person who couldn’t participate before.
what can they understand now? what can they express? what can they change without asking permission?
when i say augmenting human intellect, that is what i mean.
not a world that no longer needs us.
a world we have more ability to shape.
Austin Calvert retweeted
Companies that grow through acquisitions often end up running the same process five different ways.
That's what happened at a private aviation group we work with. Every operator it acquired brought its own processes, documents and way of onboarding a pilot. The group wanted one place for everyone to start.
Everyone already worked in Microsoft, so that's where we built it, with one developer. The first month went into the foundation: a group hub, a site for each department and a page for each person.
Then came onboarding. Pilots have 12 items to complete before day one, including a passport, licences, training records and a background check that only security can see. Office staff have nine.
That is where post-merger integration becomes practical. You have to agree on the work behind the shared front door:
1. Role-based requirements: what a pilot, office employee or freelance flight attendant needs to provide.
2. Role-based access: which department can see and approve each item.
3. Workflow state: what is missing, submitted, rejected or complete.
4. Access lifecycle: when someone joins, who gets them set up, and who removes access when they leave.
The trickiest case was freelance flight attendants. Some join for just three days. The process has to get them in quickly and remind someone to switch off their access when they leave.
During the demo, the client's process lead asked: what happens if a pilot uploads a picture of their dog instead of a licence?
Each section locks when it's complete, so one mistake could have meant uploading 20 documents again. Now the department can reject one document with a comment, and the person only fixes that item.
That's exception handling. A process needs a way to recover from mistakes without restarting everything.
We shipped six onboarding steps in one week and the remaining three the following week.
If you run a buy-and-build, this is the work that comes before AI. Agree on the process, the permissions and the person responsible for each handoff.
Then you have something an agent can help operate.
Austin Calvert retweeted
has anyone noticed gen Z can't type
millenials are the ONLY ones using keyboards
Austin Calvert retweeted
Our robots built this in the city of Almere: curved walls, different types of bricks and bonds, indented text.
Real world deployment, insane complexity, 100% of every brick done by our robots, with another 15 houses to come in the coming weeks.
Austin Calvert retweeted
AutoResearch... is the means of AI production: a model factory that creates custom frontier models for your workflows, fully owned by the sovereign institution.
Datasets. Techniques. Weights. All owned.
AutoResearch and the Fine-Tuning Ontology
The full 22 page pdf here: palantir.com/assets/xrfr7uok…
The most effective antidote to demoware is AI built as a suit the operator wears.
Models are rented. Standards are owned.
"Neither the model nor the data schema is the differentiator, because a competitor can buy both."
We handed these whiteoapers out at our sovereignty event yesterday but wanted to share with everyone, there are 24 pages in the link below with great applied learnings from @sidharthajha and team.
Well worth the read for everyone!
palantir.com/assets/xrfr7uok…
Austin Calvert retweeted
Marc Andreessen explains why AI might be the end of James Burnham's "managerial society"
In his book The Managerial Revolution, Burnham divided capitalism into two phases.
The first phase was what he called "bourgeois capitalism"
"Think of this as name on the door -- it's Ford Motor Company because Henry Ford runs the company. It's a dictatorial model and Henry Ford just tells everyone what to do. He said the problem with bourgeois capitalism is it doesn't scale because Henry Ford can only tell so many people to do so many things."
The second phase was what Burnham called "managerial capitalism", which was Marc describes as, "the creation of a professional class of managers that are trained -- not to be car experts or experts in any particular field but to be experts in management. And then that led to business schools and management consultant firms and all these things."
Marc continues:
"You look at every big company today and most executives at most of the Fortune 500 companies are not domain experts in whatever the company does. And they're certainly not the founders of those companies. They're professional managers. And in fact, over the course of their careers, they'll probably manage many different kinds of businesses. They might work in healthcare for a while and then work in financial services and then go work in something else."
"What Burnham said was that transition was absolutely required because the problem with bourgeois capitalism is it doesn't scale... So if you're going to run capitalist enterprises that are going to have millions to billions of customers, they're going to be operating at a level of scale and complexity that will require this professional management class."
Burnham acknowledged that this professional management class has its downsides -- they're not as inventive and not going to come up with the next breakthrough. But whether you think that's good or bad, that's what's going to be required.
And basically that's what happened. Marc continues:
"Managerialism basically took over everything. What I'm describing is basically how all big companies run and how all governments run and how our large-scale non-profits run."
Marc believes venture capital is a protest movement to that -- backing founders under the Henry Ford model, and trying to find the next Elon Musk or Steve Jobs who can out-innovate the incumbents.
"I've always viewed my entire life as: we're raging against the dying of the light. We're constantly trying to fight off managerialism from swamping everything and everything getting boring, gray, dumb, and old."
But AI may now offer a third model. Marc explains:
"Maybe the new Henry Ford, Elon Musk, or Steve Jobs + AI is the best of both because it's the spark of genius of the name on the door model but gives that person AI superpowers to do all the managerial stuff... What's the thing these bots are really good at? They're really good at doing paperwork, writing reports, and doing all the managerial work."
"The challenge is going to be for the innovators to figure out how to leverage AI to actually do this."
"The other challenge is going to be for the incumbents that are managerial to figure out: what does that mean? Because now they're going to be facing a different kind of insurgent competitor that has a different set of capabilities than they're used to."
"I think this is going to force a lot of big companies to figure out innovation - figure out innovation or die trying."
Ben Horowitz elaborates on this point further in his article Databricks & the Future Firm below.
Video: @latentspacepod (Apr 2026)
Austin Calvert retweeted
design slop indicators
- tags
- italicized words
- instrument serif, negative kerning
- over the top detailed UI
- dividers
- multiple color shades and opacities
- containers with borders
- diagrams. the more detailed, the worse
- code breadcrumbs
- section numbering
Austin Calvert retweeted
I feel like I need a word for the type of task that agents are really good at
I.e. where it has 100% of the data, it can run experiments, verify independently, and hill-climb towards its destination
On these tasks my trust level is totally maxed-out and the results are incredible
Just need a word for them
The length of software tasks AI agents can complete has doubled roughly every 7 months since 2019, and recent models are moving even faster, from 30 seconds of human work in 2022 to a full workday today.
Yet inside most companies, AI still isn't trusted to reliably update records in Salesforce. "AI does not have an intelligence problem," says Databricks CEO Ali Ghodsi. "It has a context problem."
Ali's answer is what he calls a "system of record for agents."
From the latest Profiles of the Future by @bhorowitz and @mikemcg0 on Databricks & the Future of the Firm: a16z.news/p/databricks-and-t…
Austin Calvert retweeted
Just spoke to the Head of AI at a huge company.
The #1 thing they're struggling with is... what do I do with the AI laggards at our company?
Like most companies, they’ve found that a Super Pareto Principle is true within their org. 10% of their employees are responsible for 90% of the uplift in productivity/token usage from software engineering to non-technical knowledge work.
They’ve spent a lot of time & resources on AI enablement from LLM foundations to function-specific trainings and skill building. And yet, the power law of productivity increases hasn’t budged.
So now they’re left with a few sobering thoughts and hard questions:
1) 90% of people are overpaid and 10% are underpaid
2) We’d be better off allocating tokens proportionally to the power law, but how do we do that without killing culture?
3) Significant layoffs are likely warranted and wouldn’t impact overall output, but cultural backlash is a big concern?
4) How do we hire more power law people as a non technology company?
5) Is all of our efforts on training and enablement ACTUALLY worth it?
6) How do we not experience a brain drain where the top 10% all leave because they want to be surrounded by other AI-native 10x’ers?
Austin Calvert retweeted
This is the Singularity Economy in a nutshell
It's not an economy of money but delta-vee, mass to orbit, megawatts, and silicon.
Money is not the bottleneck in such an economy but rather raw industrial capacity and basic physics.
I say let er rip.
Austin Calvert retweeted
this is ArkenOS
> define a hard research problem
> deploy a swarm of autonomous scientists
> agents search the hypothesis space in parallel
> does the work of an entire research org full of PhDs
> models, bio, math, physics, materials, anything
> runs experiments, learns, iterates 24/7
> used by 1000s of researchers across 100s of teams
> dm me for access!
This is exciting; I expect we are going to see a lot more things like this and it will be one of the most important impacts of AI. Congrats to the Future House team.
edisonscientific.com/article…
Austin Calvert retweeted
New Anthropic research: How AI agents shape experts' judgment calls
When experts delegate work to an agent, do they still make the key decisions? We tested this in a randomized trial with 50 professional data analysts.
Austin Calvert retweeted
I know I've been prompting my agents correctly when they present interfaces at the grammar, noun, and verb level. Took a lot of skill crafting to get here but god it's refreshing to work at the correct abstraction level and not like "my brother in christ why is this a modal?"
Austin Calvert retweeted
A few thoughts on agentic video editing I’ve had over the last few days:
1. 99% of ads should be edited agentically now. If your editors are still editing in the timeline, they’re falling behind. I got a message from my senior VE saying that he’s putting out 5x edits/day now at the same quality he was before.
2. The conversation with video editors will be uncomfortable because the timeline is all they’ve ever known. Some will be hesitant and some will be fearful for their job. I think the best way to address this is head on. The tech is inevitable, but having taste is still going to be incredibly valuable to train and give feedback to agents. Those who have this will become more valuable, those who don’t will struggle.
3. Opus 5.5 can natively edit all of your ads without needing any MCPs or plugins. I’ve tried the alternatives and couldn’t find anything that was significantly better than doing everything inside of Claude.
4. Have your senior editor(s) build skills for each style of edit (e.g. mashups, UGC, AI animations…). You can build these by feeding 4-5 inspo edits to Claude and tell it to make a skill out of it. I prefer building lots of mini-skills to one big video editing skill, so that the context stays as relevant as possible.
5. Once you have a base skill (e.g. ugly ads edits), the best way to train it is through feedback loops. Have the skill re-edit the last 25 ads each senior edited then put them in an artifact, and have the senior VE give feedback on each. Keep doing this until the skill is refined and it understands what a good edit is.
6. If you do this, there’s no reason why you shouldn’t be able to make edits that need little/no feedback. Most of ours are approved after v1 or v2, sometimes v3.
7. Only A-player editors should be training the skills.
8. Re adoption in the team, the AI champion builds the v1. Then get out of the way. I’m not the best editor on my team, so my job is to build the harness that lets the best editors train the skills, not build the skills myself.
9. If you want to supercharge this process, have your top editors record their screen as they edit. Turn on Gmeet or loom and get them to narrate the reasoning behind their process. E.g. why they chose this cut, pacing or b-roll clip…this is all context that gets fed into your skills.
10. It takes time to build, train and refine these skills. You have to go through the dip to get out the other side. Budget time in for senior VEs to work on this - otherwise you’ll end up with skills that put out subpar edits and don’t save you much/any time.
11. Operate under the assumption that this is the worst video editing is ever going to be. Opus 5.5 can do most of our editing today…what will Opus 6/Fable 6 be able to do? The editing skills you build are only going to become more valuable as the tech evolves.
I still don’t think people realise how big video editing being unblocked is. Things that I thought were 3-6 months away are now on the table.
Will continue to share more as I learn. CRAZY, crazy times.
Austin Calvert retweeted
My vibe editing tutorial:
have gone so much further since this one, so I need to make another now that Terra is claiming to be really good for this👀
GPT-6.1 Terra means business.
Our new fast, efficient model pairs sharper visual reasoning and precise computer use with clearer, more natural writing.
From decoding technical drawings to drafting proposals, filling forms to editing videos—it’s built for the whole workday.
GPT-6.1 Terra is rolling out today to Plus, Pro, Enterprise, and Edu users in ChatGPT Work and Codex.
Austin Calvert retweeted
Every PE call I've had this quarter ends up at the same AI problem: nothing is getting shipped.
I've heard it from operating partners, CFOs, a holdco's AI lead and someone about to join a portco board.
- One fund now puts its own engineers inside each portco and calls them "almost special ops"
- a CFO sees more pull requests than ever and a codebase that isn't getting any better.
- a holdco learns the same lesson from zero in every company it buys.
- plenty of portcos have one technologist who has decided that makes them the AI strategy.
- a new board member's whole brief is choosing which AI firms to trust.
None of them are doing anything wrong but AI moved faster than their operating models did.
What helps is one workflow, shipped and measured, then taken to the next company.
That's what we've done for clients for 10 years.
Austin Calvert retweeted
The end of detailed summarized thinking traces for Claude and ChatGPT (likely both anti-distillation & for UX) has made being a human in the loop challenging. It pushes us towards automation, rather than augmentation, as it has become much harder to weigh in on in-process AI work
Austin Calvert retweeted
Instagram launches long-form video called "Features"
Creators can link up to 8 clips that show up in the feed and drive to the full video per @mosseri































