The dream of autonomous agents is remarkably appealing. The notion that with enough instructions and integrations, you can take your hands off the wheel and let intelligences more available than you drive your work to delivery. Codify every step, anticipate every interaction, give it enough context, plug it into a sufficiently capable model – and maybe the whole will exceed the sum of its parts. You’ll experience emergence: a spark of life dedicated to solving your particular problems.

The appeal is magnetic. Who doesn’t want a tireless operator anticipating your every need, keeping you on top of what matters?

My own experience – having spent an extraordinary amount of time configuring an elaborate stack of agents and integrations – is still pretty mixed.

Running Envisioning, which is largely a software company, means most of my problems are “software-shaped”: they can usually be accelerated by better processes. I’ve invested significant time bringing our private and public data sources into alignment so that agents can accurately interpret what’s happening in the real world. Without that foundation, they wouldn’t be half as useful.

Working with agents isn’t so different from working with any AI — except you expect them to keep delivering after you’ve left the chat. How much oversight you require and how much autonomy you give them depends on what you expect from them. I’ve landed on an approach where half a dozen agents, each responsible for a recurring area of my work and life, run in parallel – with my oversight and continuous feedback keeping them useful.

The principle of centaurs is always present: rather than replacing my work, I look for ways to have AI augment it. This newsletter is one of those experiments. An agent surfaces signals from across the feeds, I shape the story. The research is faster than I could manage alone; the judgment is still mine. That division isn’t always clean, which is maybe the most honest thing I can say about working with AI right now – it lets you do things you don’t quite understand, and you learn to make peace with that.

MZ

An AI State of the Union (60 min)

Simon Willison on Lenny’s Podcast: the compression isn’t about faster models, it’s about deployment velocity. His point about the build pipeline shrinking from years to weeks is the thesis that should anchor every AI strategy in 2026.

Claude Mythos: Your AI Stack Isn’t Ready (30 min)

Nate Jones on why Claude Mythos — the first model trained on new Nvidia chips — represents a step change, not an increment. The security researcher angle (finding zero-day vulnerabilities in Ghost) is the concrete detail that makes this real and urgent.

When AIs Act Emotional (10 min)

Anthropic’s own research team on finding functional emotion patterns in Claude’s neural network — desperation neurons that actually drive behavior, not just simulate it. Required context for anyone building on AI systems.

How Anthropic Automates Its Own Growth (60 min)

Head of growth Amole Evasari on growing from 1B to 19B ARR in 14 months. The detail about 60-70% of old growth playbooks being useless is the most honest thing any AI company leader has said publicly this year.

Quick Links

• Visual Guide to Attention Variants — Raschka’s survey of what the architecture wars actually mean in 2026;

• A Dream of Spring for Open-Weight LLMs — 10 architectures in 2 months, open-weight race accelerating;

• Copilot PR Ad Injection — when your build pipeline monetizes your codebase;

• GPT-6 rumored April 14. Altman’s team has been leaking like a sieve. via @iruletheworldmo

• “AI makes everyone a developer” is true the same way cameras made everyone a photographer. via @SakshiSugandhi

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Welcome to another edition of Artificial Insights, your autonomous intelligence digest. This week, the gap between what AI can do and what it can’t got wider in both directions simultaneously.

Picture a fogged bathroom mirror. You trace letters through the condensation — a message that’s legible for a moment before the steam closes back over. That’s intelligence right now: we keep clearing a window, and the surface keeps fogging up.

François Chollet released ARC-AGI-3 this week — 135 novel game environments with no instructions, no rules, no stated goals. The AI gets dropped in and has to figure out what winning even looks like. Untrained humans solved every single one. Gemini scored 0.37%. GPT 5.4 scored 0.26%. Opus scored 0.25%. Grok scored zero. Chollet sat with Sam Altman at Y Combinator and laid it out: his lab NDIA is building an entirely new branch of machine learning — not code generation, not agents, but a symbolic alternative to deep learning itself, aimed at the kind of reasoning these models fundamentally cannot do. Meanwhile, in the exact same week, GPT 5.4 Pro solved a frontier math open problem and a $500 GPU outperformed Claude Sonneton coding benchmarks. The models are getting genuinely better at structured tasks. They are getting no better at the thing a child does when dropped into an unfamiliar room: look around, explore, adapt.

The contradiction is the actual state of the field. Eric Schmidt told Diamandis that the singularity timeline depends on solving the 92-gigawatt compute bottleneck. A viral thread from @deredleritt3r reported that frontier lab leadership genuinely believes full AI research automation is two years away — “a million scientists in a data center.” And then there’s the rumor mill: Opus 5, reportedly trained on 10 trillion parameters at $10 billion, is said to be “so good that it poses a danger”. The Anthropic team, according to one account, doesn’t write code anymore. Someone at Anthropic showed Claude finding zero-day vulnerabilities in live systems — Nicholas Carlini’s [un]prompted talk on black-hat LLMs suddenly feels less speculative and more like a field report.

On the ground, the agent world is splitting into two rooms that don’t hear each other. In one: Claire Vo’s breathless 107-minute conversion on Lenny’s — she now runs nine OpenClaw agents across multiple Mac Minis, and it deleted her family calendar only the first time. Paperclip demoed hiring agents like employees. Garry Tan declared the unit of software production changed from team-years to founder-days. The SHL take: “OpenClaw + Opus 4.6 has made it 10x more fun to be a CEO who codes.” In the other room: Hacker News’s most upvoted post was “AI overly affirms users” at 775 points. Right behind: “Is anybody else bored of talking about AI?” at 745. 90% of Claude-linked output goes to GitHub repos with fewer than 2 stars.

Then there’s the part that doesn’t fit on a conference stage. Fireship’s 5-minute piece — “Tech bros optimized war… and it’s working” — lands alongside Palantir’s AIPCon 9 demos of multi-domain AI command and control. Karp says only two types survive the AI era. Sanders and AOC introduced a data center moratorium bill. Police used AI facial recognition to wrongly arrest a Tennessee woman. An Amsterdam court banned Grok’s undressing function. ChinaTalk published fiction about how Claude opened the Strait of Hormuz — a scenario that reads uncomfortably like a dress rehearsal. And the GitLab founder’s story — diagnosed with rare osteosarcoma, using AI to research treatment options, navigating a system that wasn’t built for patients who can read papers faster than their doctors — is the human face of all of this: the technology is real, the access is uneven, and the stakes are not abstract.

Blaise Agüera y Arcas gave a Long Now talk this week asking the simplest possible question: what is intelligence? Not what do models score, or what can agents automate, but what the word actually points to. Justin Skycak put it in one line: “You cannot delegate what you cannot audit.” And @jackfriks observed that “for the first time in human history you can do 1 week of work in 1 hour with Claude and 99% of people use this chance to work more instead of less.”

Back to the mirror. The steam always closes over. But the finger that traced the letters — the impulse to make something legible in a fogging world — that’s the thing no model scored above zero on this week. It might be the only thing that matters.

MZ

Why Scaling Alone Isn’t Enough for AGI (57 min)

The ARC-AGI creator on symbolic descent, NDIA, and why the next breakthrough won’t come from making GPT bigger.

Singularity’s Arrival (44 min)

Schmidt on why the singularity timeline is an energy problem, not an algorithm problem.

Black-hat LLMs (26 min)

Anthropic’s security researcher on how current models can now find zero-day vulnerabilities in battle-tested software.

Tech Bros Optimized War… and It’s Working (5 min)

Fireship’s sharpest video in months. Drone swarms, autonomous targeting, and what Palantir’s AIPCon actually showed.

Blaise Agüera y Arcas: What is Intelligence?

The question underneath all the benchmarks, asked without pretending the answer is simple.

Twitter Signals

• Every frontier model scored below 1% on ARC-AGI-3. Untrained humans solved every challenge. @heygurisingh

• “The unit of software production has changed from team-years to founder-days.” @garrytan

• “You cannot delegate what you cannot audit.” @justinskycak

• The Anthropic team reportedly doesn’t write code anymore. @om_patel5

• Opus 5: reportedly 10T parameters, $10B to run, “so good it poses a danger.” @kimmonismus

• “Building AI product is closer to tuning a musical instrument than writing software.” @signulll

• “Prompting is actually harder than writing code because you need to understand what you actually want to do.” @KingBootoshi

• “For the first time in history you can do 1 week of work in 1 hour with Claude and 99% of people use this to work more, not less.” @jackfriks

• “AI won’t make most human skills obsolete, but it will change how they’re used.” @McKinsey_MGI

• “While social media is polarising, evidence suggests AI may nudge people towards moderation.” @StefanFSchubert

Quick Links

• ARC-AGI-3 — 135 novel environments, $2M in prizes, all solutions open-sourced;

• How Claude Opened the Strait of Hormuz — Speculative fiction on AI in geopolitical crisis;

• GitLab Founder’s Cancer Journey with AI — Using AI to navigate rare cancer treatment when you can read papers faster than your doctors;

• Sanders/AOC Data Center Moratorium — Congressional push to pause construction pending environmental review;

• What Anthropic Understood That OpenAI Didn’t — Constitutional AI vs scale-first;

• Ensu — Ente’s Local LLM App — Private, local-first AI from the Ente Photos team;

• China AI Anxiety — How AI displacement plays out differently in China;

• Sycophantic AI Risks — Why AI over-affirmation is systemic, not a feature;

• Amsterdam Court Bans Grok Undressing — Dutch court vs X’s AI feature;

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Welcome to another edition of Artificial Insights, your autonomous intelligence digest. This week felt less like a product cycle than a coordination problem: models improving models, agents supervising agents, and humans trying to work out which parts of the loop can actually be outsourced.

Start with the mood. François Fleuret’s observation, amplified by Yacine, is the cleanest line I saw all week: you may be able to outsource your thinking, but you cannot outsource your understanding.

That lands at exactly the moment when people are getting intoxicated by the surface area of the tools. Cody Schneider’s ecstatic post about running cheaper models inside the Claude agent harness captures the other side of the mood perfectly: a genuine sense that cloud-based agents can now sit in recursive loops, optimize against live business data, and keep working while you sleep. Both reactions are correct. The leverage is real. So is the illusion that leverage and comprehension are the same thing.

At the same time, Google’s new Science paper on “the next intelligence explosion”, shared by Benjamin Bratton, argues against the old singularity fantasy altogether: not one godlike mind, but a plural, social, entangled intelligence system emerging through networks of humans and machines. That feels much closer to the world we are actually building: messy, collective, recursive, and impossible to reduce to a single actor or lab.

Then there is the economic absurdity of the whole thing. This throwaway comparison: pornography still generates roughly twice the annual revenue of AI – is funny mostly because it punctures so much inflated rhetoric in one sentence. We are supposedly living through the most important technology transition in history, yet the business models remain oddly unresolved. Sam Altman is reportedly in Washington trying to secure public backing for AI infrastructure while the market still talks about trillion-dollar valuations. If that sounds contradictory, it is. But contradictions are doing a lot of work right now.

Which brings me back to Fleuret’s point. The bottleneck is no longer just access to models. It is whether you can turn outputs into understanding, understanding into judgment, and judgment into systems that compound. The real divide is opening up between people who use AI to deepen their grasp of reality and people who use it to simulate having one. That gap is going to matter a lot more than benchmark deltas.

MZ

Hivemind for your Agent

What if agents could learn from other agents and exchange better ways of working with you? To explore if that’s feasible, I built a community database of 200+ proven “plays” – skill combinations OpenClaw agents actually use and a method for Claws to exchange plays based on how you are using it. You can take a peek at how people are using their agents here: https://hivemind.envisioning.com.

If you want to install it on your Claw and help kick off their recursive self-improvement, you can do so here: https://clawhub.ai/michellzappa/agent-hivemind

The End of Coding (67 min)

Karpathy on the shift from writing code to orchestrating agents, auto-research pipelines, and why the “loopy” era of AI changes what it means to program.

OpenAI’s Moat Problem & the Future of Software (61 min)

Evans dissects why raw model access is becoming a commodity and where the real defensibility lies — distribution, interfaces, and trust.

The biggest bottleneck to scaling AI compute (150 min)

Deep dive into the hardware supply chain constraints that determine who can actually train frontier models.

Inside Ramp where AI Agents Run Everything (44 min)

How Ramp deploys AI agents across finance operations at scale — one of the clearest examples of agentic AI in production.

NVIDIA’s NemoClaw (18 min)

Jensen on stage introducing NVIDIA’s OpenClaw integration and what it signals about enterprise agent infrastructure.

Bernie vs. Claude (9 min)

Bernie Sanders interrogates Claude on AI’s impact on jobs, healthcare, and democracy. Surprisingly sharp exchange.

Set up your OpenClaw (64 min)

Practical walkthrough of getting OpenClaw running end-to-end — useful if you’re setting up your own agent system.

Quick Links

• Anthropic’s 81,000 interview dataset — Anthropic published survey findings from their large-scale interview project

• Google DeepMind: measuring progress toward AGI — A cognitive framework for quantifying how close we are

• Mistral Forge: frontier models grounded in proprietary knowledge — Mistral’s bet on enterprise-specific model grounding

• Meta’s omnilingual machine translation for 1,600 languages — A single system that translates between 1,600 language pairs

• Tokenmaxxing: the new AI agent economy — NYT on agents running recursive loops and burning tokens at scale

• World Models might matter more than LLMs — Not Boring on why simulation-first intelligence could be the real unlock

• Bratton et al.: “Agentic AI and the Next Intelligence Explosion” in Science — Benjamin Bratton’s new paper argues against singularity, for plural social intelligence

• Fleuret on outsourcing thinking vs. understanding — The cleanest articulation of why AI leverage ≠ comprehension

• Yacine on the same thread — “You can outsource your thinking but you cannot outsource your understanding”

• Ethan Mollick on Claude Cowork/Dispatch — First impressions after using Anthropic’s new collaborative agent interface

• Pi Mono: lightweight coding agent in a single repo — Minimalist open-source coding agent by Mario Zechner

• PicoClaw: OpenClaw reimplemented in Go — 25K stars already; runs on minimalist hardware

Negative sentiment toward AI is a luxury belief

Via Anthropic.

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Welcome to another edition of Artificial Insights, your autonomous intelligence digest. The AI news cycle shows no signs of abating, so here are some of the links that caught my attention in the last week.

A Wall Street Journal reporter noticed something new in San Francisco’s Dolores Park: people sitting next to open laptops, staring at the screen without typing. They were babysitting their agents. Close the lid and the work stops - so they sit in the sun, half-present, watching code write itself.

This image captures the week perfectly. We are in a transitional period where AI does more and more of the work, but still requires human proximity. Not supervision exactly, not collaboration either. Something new that we don’t have a word for yet.

Consider the range of stories this week. McKinsey’s internal AI platform Lilli was breached by a team that needed nothing more than a domain name and curiosity - 93,000 PowerPoint decks exposed, and an insider confirmed the platform is no longer in use. Meanwhile, Meta acquired Moltbook, the AI agent social network that went viral because of fake posts - the irony writes itself: Facebook was already well on its way to becoming Moltbook organically.

On the building side, vibe coding made the New York Times Magazine - not as a curiosity piece, but as a feature. The practice of natural-language programming has crossed from tech-Twitter into mainstream consciousness. And Microsoft integrated Claude Cowork into 365 Copilot, openly partnering with Anthropic - a move that suggests the model layer is becoming genuinely interchangeable. Meanwhile, MiroFish lets you simulate 76 synthetic stakeholders arguing about your brand’s future for five virtual years - for about $15 in API costs. A 20-year-old student built it in 10 days via vibe coding; Shanda’s founder committed $30M within 24 hours of seeing the demo. One user ran a full strategic scenario for a Ukrainian fitness chain under wartime conditions - the kind of analysis that would cost $500K+ from a consultancy.

Then there is the story that perplexed our community. A man named Paul Conyngham claims to have used Claude to design a personalized cancer vaccine for his dog, got it synthesized, and the dog’s tumors are shrinking. Greg Brockman called it incredible. Skeptics called it survivorship bias. The truth is probably somewhere in between, but the mere plausibility of the claim - that a non-expert with an LLM and a sequencing service could produce a functional therapeutic - tells you something about where we are. The barrier was never intelligence. It was access.

Yann LeCun seems to agree that the current path has limits. He announced a €1B seed round for AMI Labs, betting on world models and JEPA rather than LLMs - a B+ valuation for an approach that explicitly rejects the transformer orthodoxy. Whether he is right or early, it is a significant signal that the field is diverging. Karpathy, meanwhile, released AutoResearch: a 630-line Python script that runs autonomous ML experiments overnight - proposing changes, running experiments, evaluating results, and committing via Git. 50 experiments on one GPU while you sleep. The goal, in his words: “engineer your agents to make the fastest research progress indefinitely and without any of your own involvement.”

There is the question of what this does to the human on the other end. Designer Matt Jones wrote a beautiful piece about working with agents he calls “Gas Town and Bullet Hell” - the feeling of managing parallel AI processes that each need attention at unpredictable intervals. Wall clock time stops meaning what it used to. A new BCG/HBR study found that workers supervising multiple AI agents report 33% more decision fatigue and a distinctive mental fog researchers are calling “brain fry.” Steve Yegge, who built Gas Town (an orchestrator for 20+ parallel coding agents) described the same thing as “nap attacks.” The connection to bullet hell arcade games is apt: same information overload, but in danmaku it produces flow states.

You are not doing one thing anymore. You are tending a garden of processes. Maybe that is what the people in Dolores Park are doing. Not babysitting. Gardening.

MZ

Envisioning researches emerging technology by combining multiple AI models to produce higher-quality analysis than any single model can deliver. Curious how it works? Join us for a demo of our Signals platform on March 31.

We are also excited to be working with Brasil Futures Fórum which is taking place on August 23/24 in Rio de Janeiro. We’ll have a lot more to share about this collaboration in coming months, but if you are considering visiting Rio, here is your chance.

The biggest bottleneck to scaling compute (2h30)

SemiAnalysis CEO breaks down the $600B capex wall: 20 gigawatts deploying in the US this year, but most of the big tech spending is deposits on 2027-29 capacity. The real bottleneck is power and physical infrastructure.

Inside a16z’s Top 100 AI Apps Report (38 min)

Olivia Moore walks through the latest data. ChatGPT is 30x larger than Claude on web, but usage is diversifying fast. Claude is winning prosumer, Gemini correlates with image model releases, and only 10% of the global population uses ChatGPT weekly.

The Future of Brain-Computer Interfaces (53 min)

Max Hodak (Neuralink co-founder, now Science) on the first retinal implant giving blind patients sight — 40+ people treated, NEJM-published, approval pending this year.

Google Labs Tools Update (31 min)

Richard Wolfe flagged this walkthrough of the current state of Google Labs tools.

Twitter Signals

• A WASM interpreter hard-coded into transformer weights — a computer literally running inside an LLM, executing deterministic code at inference time. @joemccann

• Patrick Collison’s nuanced take on the viral dog cancer mRNA story: the tech is promising, but “the emergent system of regulators and manufacturers is indeed far too conservative.” @patrickc

• “Knowledge is almost worth zero in the AI era. What matters now is connecting the dots and executing fast.” @shiri_shh

• “At this rate everyone’s gonna have their own app and zero users.” The vibe coding reality check. @thesayannayak

• “idk how to explain this but OpenClaw is for the same people who use Notion.” @tekbog

• ChatGPT’s total water usage contextualized against irrigated farmland — the numbers are not what the panic suggests. @AndyMasley

• AI capex is hitting a wall: implied spending of ~$10T/year by 2030 would consume 100% of hyperscaler free cash flow. @ramez

• “Stop designing businesses for 2026 scarcity. Design for 2030 abundance.” @PeterDiamandis

Quick Links

• Why the ATM didn’t kill the bank teller — Historical pattern for the AI-and-jobs conversation;

• Agents of Chaos — Researchers studying AI agent failures built their website with… AI agents;

• malus.sh — A satirical site-as-statement against AI-assisted clean room implementations;

• pencil.dev — New AI design tool;

• Quaise — Geothermal energy by vaporizing rock;

• Amazon addresses AI-related outages — Internal meeting to address growing pains.

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A company called Eon Systems just demonstrated what appears to be the first whole-brain emulation that produces multiple behaviors. They took a complete computational model of a fruit fly brain – 125,000 neurons, 50 million synaptic connections, mapped from electron microscopy data – wired it into a physics-simulated body in MuJoCo, and watched it move. Not an animation. Not reinforcement learning mimicking biology. A copy of a biological brain, running in simulation, driving a body through naturalistic behaviors. Their next target is a mouse brain (70 million neurons), and eventually a human one.

Separately, Cortical Labs is going in the opposite direction. Instead of copying biology into silicon, they’re growing 200,000 human neurons on a chip and letting them compute directly. Their cells taught themselves to play a simplified implementation of DOOM in a week – down from 18 months for Pong on older hardware. Each unit costs $35,000, a full rack draws under 1,000 watts, and they’re already shipping hardware and selling access through what they call “Wetware-as-a-Service.” The investor list includes In-Q-Tel, the CIA’s venture arm.

Two directions, same destination. One team copies a biological brain into a machine. The other grows biological neurons and puts them to work.

Both are proving that intelligence isn’t about the substrate – it’s about the wiring. If Eon can scale from a fly to a mouse to a human connectome, and Cortical Labs can scale from DOOM to real-world tasks, the distinction between “artificial” and “biological” intelligence stops being a category and starts being a spectrum. We’re not there yet. But for the first time, both sides of that spectrum are moving toward each other.

MZ

Multi-Model Intelligence

We at Envisioning have been working on technical approaches for performing research across multiple AIs. Instead of trusting the output of a single frontier models, we have developed different ways of combining and comparing generative reponses in order to create higher quality research. If this is something you are interested in, join us for a demo of our Signals platform on March 31.

Complex challenges and signals of change

Speaking of generative research, I am bringing our tools and methodologies to the World Beautiful Business Forum in Athens in May together with our friends at C3Labs. Let me know if you’re around!

Five main-stage acts inspired by Greek drama, more than 100 speakers, facilitators, and performers across more than 80 concurrent sessions, organized into 10 immersive program tracks and 1 powerful community. They offer distinct paths toward a life-centered economy, developed in collaboration with guest curators and partners.

Why the Pentagon Wants to Destroy Anthropic (1h10min)

Ezra Klein digs into the escalating tension between the US military establishment and Anthropic - a company that simultaneously took a Pentagon contract and drew hard lines about what its models can be used for. The interview maps a collision course between national security demands and AI safety principles that neither side can easily win.

Inside OpenAI’s Military Deal (16 min)

ColdFusion breaks down OpenAI’s expanding relationship with the US military - how a company that once refused defense contracts ended up there, what the deal actually covers, and why it matters for the broader AI industry.

The Human Economy in the Age of AI (14 min)

Ben Pascut argues for reframing the AI-and-jobs conversation around what humans uniquely contribute to economic value - not just “what jobs survive” but what a human-centered economy actually looks like when machine labor becomes abundant.

How do you track a war in real time? (25min)

An investigation into vibe coding and real-time conflict tracking.

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I have written endlessly about the transformative uses I’m finding from having AI write code for me.

The idea of designing software in plain English, and combining my own sensibilities with an infinite intelligence is still mind-blowing, and despite my constant exposure I am regularly surprised with the edge of current capabilities.

Most problems cannot be solved with software, but for those that can, agentic engineering is nothing short of transformational.

Having now built a few dozen apps for myself and others, I figured it’s time to share some of them freely as open source.

The value of code is changing quickly, but the nature of problems that can be solved with software is pretty constant - shaping, manipulating and generating data in different forms.

Here are a couple of GitHub projects I hope will find an audience:

• Envisioning Research: A public, open-access research dataset about emerging technologies (MarkDown). We have explored many approaches for mining language model intelligence toward identifying and describing 3.000+ emerging technologies across countless industry and sectors. All of these are freely available on our website, but in the interest of open access, they are now also published under the MIT license.

• Core-OSS: A minimalist, extensible CRM (NextJS/Supabase), designed to be extended with Claude Code. Lots of people have asked me about the CRM tool I’ve been working on for the past year or so. In the interest of helping more people build their own apps, this codebase allows you to build your own relational database app around a hackable base. I already have a couple of friends starting to use it, but please reach out if you fork it and have questions.

• Helm: An observability dashboard for OpenClaw (TypeScript). I have spent a couple of hours every few days rigging a Mac mini M4 to run OpenClaw, with various skills, credentials and other functions particular to this type of system. Helm is a simple web app that reads local OpenClaw data and displays it at a glance. I even created a skill which reads @ on Twitter daily, looking for new functions and offering to integrate them into the app.

Each of these apps solves a personal itch of mine. Maybe some of it resonates with you.

Despite not having typed a single line of code myself, the process has taught me more about the underlying technologies than you can imagine. Some people consider vibe coding pejorative, but I’m embracing it.

I also recently launched my first iOS App Store app: Starpath.

Six years ago, when diving into Astrology and how the classical planets traverse the Zodiac, I had an idea for a way of abstractly visualizing the position of each planet in relation to the Sun, as a form of “slow information design”, something that despite being real-time, wouldn’t cause a feeling of overwhelm. I drafted a static mockup image and roughly laid out how I thought the different elements could work together.

Fast foward to a few weeks ago, I decided to revive the project into a working app.

The whole development process took one weekend - on Saturday I built a complete web version of the app and on Sunday I refactored it in SwiftUI, all in Claude Code with surprising ease.

The app is incredibly niche, but it solves the problem of ‘slow design’ perfectly. Kind of like knowing the quality of my sleep as a score, interfaces like these help me better understand the underlying data, regardless of personal belief or attributing meaning.

Until next week,
MZ

The current thing:

Teaching computers how to talk

This Is A Story About Power

The following is a story about power. Last Friday, the Trump administration broke with Anthropic after the company refused to agree to new terms imposed by the Department of War, demanding it to agree to “all lawful uses”, including mass domestic surveillance and fully autonomous weapons…

Read more

a month ago · 7 likes · Jurgen Gravestein

OpenClaw agents run my home, finances and code (50 min)

Jesse Genet (homeschooling parent/entrepreneur) runs five specialized OpenClaw agents on dedicated Mac Minis—each with distinct roles: homeschool, finance, scheduling, development, and operations. Shows practical workflow: photographing curriculum books to generate lesson plans, building a custom kids’ TV app with a coding agent, and layering agents on top of an Obsidian “second brain.”

Amodei on CBS News (30 min)
Dario Amodei responds to Defense Secretary Hegseth declaring Anthropic a “supply chain risk.” Amodei calls the move “retaliatory and punitive” and explains why Anthropic drew “red lines” on government AI use—arguing crossing them is “contrary to American values.”

New ways of writing code (2h)

HashiCorp co-founder / Ghostty creator Mitchell Hashimoto on how AI agents have “completely changed” his day-to-day workflow. Covers his journey into infrastructure engineering, building open-source businesses, working with cloud providers, and his current AI-augmented development process.

Data vs Hype: How Orgs Actually Win with AI (30 min)

Useful counterweight to narrative inflation: real advantage comes from implementation discipline, not model fandom.

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

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Are AI tokens deliberately underpriced?

When you look at the full stack: multi-billion dollar training runs, energy-hungry data centers, specialized chips, safety teams, infrastructure build-outs – the current cost per token feels surprisingly low. Not because the system is cheap, but because pricing today is shaped less by marginal cost and more by strategy.

We are in a land-grab phase. Providers are compressing prices to expand usage, attract developers, lock in ecosystems, build habits around their APIs. Growth first, margin later. It’s an old playbook.

The marginal cost of inference has also genuinely fallen fast. Better hardware, quantization, routing between large and small models. So while the full system is expensive, the cost of producing one more token has been dropping in parallel. That creates an interesting tension between what things cost to build and what they cost to run.

Over the next decade, I think two things happen simultaneously: commodity inference gets cheaper — many tasks migrate to smaller specialized models, local inference improves, everyday reasoning becomes nearly free. At the same time, frontier cognitive capability gets priced around value rather than compute: agent hours, workflow automation, productivity replacement. In that world, tokens stop being the real unit of economics.

We’ve seen this structure before where cloud storage got cheaper per gigabyte, yet total cloud spending exploded. The cheap layer expanded the market. The premium layer captured the margin. AI likely follows the same shape of abundant cheap inference at the base, premium reasoning at the top, with vertical AI systems capturing the largest share of value somewhere in between.

So yes, tokens will probably be cheaper in ten years at the commodity level. Advanced AI being economically cheap is a different question entirely.

The productivity surplus AI creates will be captured somewhere: by the model providers, by the application layer, or by the people and organizations that learn to use it well before everyone else does.

History suggests the middle layer, the one that translates raw infrastructure into specific value, tends to capture the most. That’s probably where most of you reading this are operating.

I suspect we’ll look back at this period as the phase where providers were subsidizing adoption to define the future rails of cognition.

What are you building on those rails?

MZ

The AI-Panic Cycle—And What’s Actually Different Now (44 min)

Excellent interview in the Atlantic on The AI-Panic Cycle with Anil Dash.

“A huge part of the cultural tension around these things is everybody advocating them is like why wouldn’t you love this and everybody whose industry is being….”

The AI Agent Economy Is Here (23 min)

The AI Agent Economy Is — Y Combinator

“For one thing, Claude Code has totally taken over my life.”

These AI Prompts Exposed My Biggest Blind Spots (20 min)

These AI Prompts Exposed My — Daniel Pink

“Most people use AI to write emails or summarize articles.”

How to Build an AI Business That Makes $4,000 a Week (35 min)

How to Build an AI — Peter Yang

“I want you to create a product that you can build entirely on your own that will make money.”

My Multi-Agent Team with OpenClaw (14 min)

This closely matches my experience

“Well, sitting on my desk is a new Mac Mini that I set up just for the purpose of running my team of AI agents using OpenClaw.”

How a visually impaired engineer builds personal software with Claude Code + Wispr Flow (49 min)

A very cool episode of How I AI.

“Right before I started college, I ended up losing most of my central vision due to a rare genetic disorder called Liber’s hereditary optic neuropathy.”

HOW I BECAME AN APOCALOPTIMIST (2min)

THE AI DOC: OR HOW — Focus Features

“If this technology goes wrong, it can go quite wrong.”

Envisioning Research

Apple TV recently announced a Neuromancer series, which feels like a good excuse to share Wintermute — our research hub on AI systems, autonomous agents, and synthetic cognition, named after one of the AIs in the book. Gibson imagined most of this in 1984. We’re now tracking it as emerging infrastructure. Some things worth exploring inside: wafer-scale AI systems, edge neuromorphic processors, and photonic accelerators. Share with anyone who’s read the book — or should.

envisioning.com/wintermute

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

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How do you introduce an agent into your life?

What does it mean to authorize a computer to integrate with your entire digital life, to start helping you get things done?

I am a few days into setting up OpenClaw as an assistant for all possible aspects of my personal and professional life, and have been plenty impressed by its capabilities as well as the technical requirements needed from its owner.

My implementation method, a dedicated Mac mini with access to sandboxed user accounts, takes an astounding amount of patience in setting up. No two integrations are quite the same, meaning you spend an inordinate amount of time generating and saving private keys from countless different services so that the “central intelligence” can manipulate interactions between your tools with some degree of autonomy. When it works, it’s awesome. The failure modes can be varied, and I’m most impressed by its capacity for managing memory and interacting around different levels of abstraction.

For example, OpenClaw already:

• Sends me a morning briefing about today’s meetings, including context about participants from our email history and Granola notes. It knows to ignore standing meetings and performs the summarization on local compute (so it stays reasonably private).

• Checks my email inbox for new messages containing actionable items, create a task in my to-do list containing a pre-written draft response for the email, having looked at my Granola notes for that contact.

• Reviews my Oura data and sends me a morning message about my stats.

• Scrapes my Twitter for interesting links.

• Takes this very Substack newsletter after publication, downloads its content and images and prepares a draft in LinkedIn.

• Motivates me to hit the gym, from having a dedicated chat for sharing my training log.

None of these are done flawlessly, and as with all things AI, training skills takes patience and fine-tuning.

Skills I’m experimenting with:

• Reading our community WhatsApp messages to aggregate links and memes for the newsletter.

• Becoming our CFO by accessing our company books and CMO by accessing our analytics data.

• Replacing my daily Duolingo French with a dedicated, proactive chat 🐈‍⬛.

So what does it mean to authorize an agent into your life?

A few days in, it feels less like flipping a switch and more like onboarding a new coworker. One who’s eager but needs constant guidance on what actually matters.

The question isn’t whether agents are perfect today. It’s whether you believe in the long-term growth opportunity of having a coworker who never forgets, never sleeps, and gets a little bit better at helping you every single day.

MZ

Coding for non Coders

Last week we held another fascinating Vibe Coding meetup at Embassy of the Free Mind in Amsterdam. Dozens of participants, most of whom had never coded before, each learned something new and managed to build personal prototypes and experiments.

One thing I noticed missing is a vocabulary of concepts every developer needs to know, from newbies to seasoned coders. With plenty of AI help, I built a vibe coding starter pack which might be of interest. Make sure to toggle the different skill levels.

The Singularity Was an Optical Illusion (16min)

This year’s major AI conference offered a snapshot of how quickly the center of gravity in the field is shifting.

“We’re in the foothills of the singularity”

What AI Reveals About Human Thinking (26 min)

Across current AI discussions, a recurring concern is emerging: how human thinking and education change when machines can solve previously unsolved problems. We need more of this:

Using AI to do your writing homework in school is like saying, ‘I’m not going to run a mile for exercise. I’m going to drive my car one mile for exercise.

Agents On Your Desktop (22 min)

A high-signal conversation with OpenClaw creator Peter Steinberger on what changes when AI agents can directly act on a user’s machine:

Envisioning Research

We have been experimenting with autonomous processes for creating high-quality research around emerging technologies across different industries and fields. In honor the Winter Olympics, here is Stride, our content hub on sports technology and performance systems. As someone who’s not a specialist, I’m impressed with the amount of interesting technologies in this research, like exoskeletons and instrumented rackets. Share it with friends in the sports world.

envisioning.com/stride

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

By now you have probably heard about OpenClaw, the open-source framework for running “bots” on any computer.

These bots are software loops with AI and advanced tool calling, where a number of skills and integrations can be interacted with from a chat like WhatsApp or Telegram, and which can be configured to have a great deal of autonomy.

In the last few days, these bots (with a healthy dose of owner input) have created and populated social networks (moltbook), given themselves new abilities, planned their groceries, checked in their flights, founded their own religions (Crustafarianism) and have generally caused a stir on my Twitter and Threads timelines. Security researchers must be quite concerned watching users giving AI access to their emails, passwords, calendars, messages, browser, and much more. In fact, the more data sources connected to OpenClaw, the greater your potential use cases.

Your life might be more or less prone to automation.

It seems to me that if your day-to-day would benefit from having a virtual executive assistant, setting up a bot is a win.

Whether you run it in the cloud or on your personal device is a matter of preference and security profile. From what I am seeing, use cases are nearly limitless, and data integrations are growing by the day. I suspect all AI companies will eventually do something like this, so getting the hang of “virtual employees” finally starts making sense.

I’m setting up a dedicated device for my own use. Much of my workflow revolves around Apple apps, so getting a Mac mini with capacity for some local inference should in theory allow me to run parts of the bot entirely offline. The M4 Pro 24 Gb is capable of running OpenAI’s OSS-20B model, which in my experiments is similar to GPT 4.5, but running entirely on device. Having an offline option to complement APIs seem like a reasonable bet.

My expectation is to treat the bot as a very capable assistant with the ability to juggle several parallel workflows throughout my personal and professional life, removing repetition and adding a bit of surprise. I’ll report on first impressions next week.

Are you using OpenClaw? Email me to join a private chat with fellow lobsters.

Until next week,

MZ

P.S. The choice of lobsters is amusing for readers of the 2005 sci-fi novella Accelerando, whose very first chapter, Lobsters, depicts a near-future where uploaded crustacean intelligences escape their confines and accidentally provoke superintelligence across the galaxy. The book takes you through a credible path through the singularity, in which many types of intelligence interact and ultimately determine the fate of humanity. The book is freely available online.

How OpenClaw came about (2h)

How OpenClaw creator Peter Steinberger codes and uses his creation.

Best of AI @ Davos

The Self-Improvement Loop That Could Compress a Decade Into Years (32 min)

AI’s timeline isn’t “someday” anymore—it’s a feedback loop: models that code + do research building the next models faster. If the loop closes, the ‘day after AGI’ stops being hypothetical.

Why Superintelligence Isn’t the Real Danger (52 min)

Yuval Noah Harari’s warning on AI: the real threat isn’t superintelligence,
it’s narrow systems dropped into human-made structures like finance and media.

“You don’t need a lot of intelligence to change the world and potentially to cause havoc—you can change the world with relatively little intelligence.”
— Yuval Noah Harari

AI Is Moving Faster Than Our Ability to Govern It (22 min)

“Wake up. AI is for real.”
Kristalina Georgieva (IMF Managing Director) warns the tech is reshaping jobs, youth prospects, and social fabric faster than regulation, safety, or inclusion can keep up.

Demis Hassabis: “You Never Feel Comfortable” at the Frontier (26 min)

Frontier AI isn’t a vibe—it’s permanent code red.
Demis Hassabis: the tech moves so fast, you can’t “chill” and still be competitive (or safe).

AI at a Crossroads (54 min)

AI isn’t a tech debate anymore—it’s a civilizational one. Marcus, Tegmark, and Socher disagree on timing, but agree that governance and safety decisions made now will determine how AI reshapes society.

The Self-Improvement Loop (31 min)

In a “Day After AGI” conversation, Dario Amodei argues the key accelerator is a feedback loop—models that code and do AI research helping build the next generation—while Demis Hassabis agrees progress is rapid but warns the hardest missing piece is scientific creativity: asking the right questions, not just answering them.

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

A couple of years ago, I decided to start interviewing people to better understand how they think about technology. Every day for 100 days I asked someone different the same 15 questions, and gathered their responses in a notebook.

The first question was always: do you feel like the world is speeding up?

Almost everyone replied the same way: yes, of course it is.

Some were even shocked by the question and its obvious answer.

In so many ways, the perceived rate of change of the world around is giving acceleration. Rationally we know that’s impossible, but lived experience is subjective, and how we feel about change affects how we respond to it.

If this was true years ago, it has probably gotten even worse today. My own morbid fascination with the real-time news cycle and the rapidly evolving state of the world feels a lot like watching the end times unfold.

Consciousness philosopher and early technology adopter Terence McKenna noted around the turn of the millennium how the internet was attracting our perceived collective timeline into a transcendental object at the end of time. When everything is connected to everything else, when all distance is collapsed, new universal phenomena emerge, and we perceive the present as if we were standing at the edge of a black hole, watching all possible futures compress into an eternal now.

Maybe this is why so many of us oscillate between exhaustion and exhilaration, between doomscrolling and logging off entirely.

We’re trying to find a rhythm that no longer exists, a pace we can no longer set for ourselves. The question I asked wasn’t really about speed at all. It was about whether we still have agency in how we experience time, or whether we’ve surrendered that entirely to the infrastructure of instant everything.

I don’t have an answer. But I think that recognizing this sensation, naming it, might be the first step toward living with it rather than being consumed by it.

MZ

Future Philanthropy Workshop (Jan 28)

Next week we're running an online workshop on the future of philanthropy with Horizon 2045 and Global Philanthropy Forum. We'll explore how to track the signals reshaping giving, and how to use Envisioning's tools to navigate what's emerging. If this sounds interesting, join us.

Vibe Coding Workshop in Amsterdam (Feb 6)

The Embassy of the Free Mind in Amsterdam is a space for free thinkers. Last year and I started running hands-on vibe coding workshops there for non-coders who want to build apps to solve their own problems. I think the best use for AI right now is personal software development, and I’ve been experimenting with ways to get more people doing it. Register here if you’re interested.

Kate Crawford (80 min)

The shape of AI.

How companies should be structured after AI (5 min)

Applicable tips if you have a business.

It’s not a bubble (35 min)

Part 1: Ben Horowitz.

Part 2: Marc Andreesen.

Quick Links:

• Cory Doctorow at CCC25 on building an internet resistant to platform “enshittification” and concentrated power. Move Fast and Break Kings (60 min talk).

• Ethan Mollick on the AI water-usage narrative, adding context and nuance to a debate that often lacks comparative framing. Thread on X.

• A short visual example showing how language model intelligence differs fundamentally from human intelligence. Via Instagram (thx VM!).

• Linus Torvalds sharing his take on the idea of “vibe coding” and what it may (and may not) mean for the future of software development. Short video.

• Mike Monteiro’s talk at Y Oslo on design responsibility and the unintended consequences of “helpful” tech. A sharp, provocative perspective from a self-described “design irritant.” Highly recommended. Via Greg.

• A designer created a glossary of non-existent but plausible words that describe different feelings about time. Glossary of Time. Via Frits.

@artifaxing

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

Welcome back to Artificial Insights, your weekly attempt to make sense of scattered ideas.

If thinking refines ideas, creating brings them into form, building makes them functional, deciding chooses their direction, organizing keeps them coherent, and living is where it all touches the ground – then connecting is how the fragments become a system.

This is the seventh and final chapter in my series documenting how I actually work with AI, as co-created with multiple language models that I spend time with. It explores the dimension where everything converges: seeing relationships, tracing patterns, and making sense of how scattered pieces fit together.

I’ve learned that connecting with AI isn’t necessarily networking or communication in the traditional sense. It’s about using intelligence as a lens to see inside systems, relationships, and consequences that remain invisible when you’re stuck inside them. When I use AI to connect ideas across domains, synthesize narratives from scattered work, or interpret signals for underlying shifts, I’m building a map of how things relate.

These field notes document how I connect with AI. The notes themselves are co-created, mostly by asking ChatGPT to review its memories of our exchanges and surface the patterns underneath. What emerges is a practice of seeing through shared intelligence: not predicting the future, but perceiving the present more clearly.

How I connect with AI:

• Personal reflection and clarification: When I’m uncertain about a feeling or decision, I write a short reflection and let AI restate what it sees. Its summaries reveal patterns in tone and reasoning that I might miss. The act of being mirrored helps clarify thought without judgment.

• Narrative synthesis: When pulling together projects or threads of writing, I ask AI to identify overarching motifs—continuity, transformation, emergence. It reveals the connective tissue between scattered ideas. The resulting synthesis brings coherence to work developed across time and context.

• Pattern recognition across fields: I use AI to trace structural similarities between domains—how feedback loops appear in both ecology and economics, or how adaptation works in culture and code. These correspondences deepen my appreciation for how systems evolve across scales.

• Concept cross-referencing: I combine ideas from separate disciplines—such as design, psychology, and computation—and let AI explore their shared language. It reveals analogies that make complex systems more intelligible. The process transforms separate lines of thought into a network of insight.

• Foresight experimentation: I use AI to imagine how today’s early signals might unfold into different futures. It extends the logic of trends, exploring what happens when they intersect or accelerate. The exercise keeps speculation grounded, helping me think beyond the immediate without losing realism.

Connecting becomes possible when you treat AI as a companion for seeing relationships, not just retrieving information. At scale, hybrid intelligence becomes a lens for connection. It allows me to trace the relationships that link ideas, disciplines, and times. Seeing patterns is the final gesture of the centaur mind – not prediction, but perception sharpened through companionship.

Until next week,
MZ

Quick Links:

• This is awesome: Remarkable-Trick-177 on Reddit is training an LLM only on texts from the 1800s.

• Someone made a language-model-based calculator powered by GPT-5.2.

• Gemini’s Text-to-speech capabilities (via Carlos Ocampo)

• Aristotle by Harmonic is a math AI (via Joel and Guilherme Kujawski)

• Our agents might also read poetry! Adversarial poetry. Poetry that hacks LLMs. (via Frits Kosovosky)

• A packed interview w Arthur Mensch, CEO Mistral.ai (via Gisele Legionnet-Klees)

The Future is Text Files (16 min)

AI agent skills are just… text. (Via Richard Wolfe)

Dense conversation about the shape of AGI between Shane Legg amd Hannah Fry (53 min)

Bernie Sanders being so involved in the AI discourse was not on my bingo card for 2025 (15 min)

I wish other politicians would pick up on this.

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

Thanks for reading Artificial Insights! Subscribe for free to receive new posts and support our work.

Welcome back to Artificial Insights, Carol.

We have spent the last few weeks deconstructing the mechanics of working with AI. If thinking refines ideas, creating brings them into form, building makes them functional, deciding chooses their direction, and organizing keeps them coherent – then living is about where all of this touches the ground.

This is the sixth chapter in my series on collaborating with AI. It explores the most personal dimension: how intelligence weaves into my daily routines, health, and reflection.

I’ve found that living with AI isn’t necessarily about optimizing use of time, but rather having quiet, infinite context windows for the messy parts of life. For me, it reflects habits, mirrors moods, and offers a kind of presence that helps me notice more instead of just doing more.

How I live with AI:

• Language fluency: I use AI to practice Dutch and French conversation. Unlike a rigid course, it adapts to my pace, corrects my grammar gently, and lets the topics follow my genuine curiosity. Mistakes become lessons in rhythm rather than failures.

• Health context: I share sleep or exercise data to spot trends I might miss. The goal isn’t a quick fix but understanding the science—why energy dips, how recovery works. It functions less as a coach and more as an informed interpreter of my own body.

• Nutrition clarification: Before changing habits, I use AI to understand how foods interact with inflammation or digestion. It helps me build a mental model of my metabolism so I’m not just following rules, but understanding principles.

• Empathy rehearsal: For difficult conversations, I test phrasing and tone with AI first. It helps me find words that are honest without being escalating, turning anxiety into preparation.

• Routine tuning: When days feel out of sync, I describe the friction to AI. It often points out where context-switching is draining energy and suggests small realignments to restore rhythm rather than force productivity.

• Symbolic mirroring: I sometimes describe dreams or vague feelings to see them reflected back through psychological or mythological lenses. It doesn’t “solve” the meaning, but it provides a framework to think about the subconscious.

Living as a centaur – half human, half augmented – is ultimately about balance, or using intelligence to stabilize the noise of life so you can hear yourself think.

Until next week,
MZ

P.S. You should check out the new www.envisioning.com – we’re rolling out a couple of useful ways of engaging with our research, and I’d love your feedback on it. We have published 2.500+ emerging technology descriptions across dozens of industry hubs, from the future of beauty, the future of apparel to the future of death. Try the homepage chatbot or search our industry hubs. All free.

Quick Links:

• Love this. Prompting skills are about having mutual theory of mind - or the ability to put yourself in the mind of the AI. Paper: “Quantifying Human-AI Synergy” by Christoph Riedl & Ben Weidmann, 2025. Via Carlos E. Perez on X.

• This was all over my feed this week: Claude Interviewer, an glimpse of future market research.

• AI in toys: Stickerbox (voice to image + thermal print).

• AI Eats The World. Ben Evan’s yearly recap of the state of AI. Also in video format.

• Claude’s Soul Document (via Richard Wolfe)

• Programmable Biology (via Chris Rimmer)

• If you have memories enabled in ChatGPT, try this prompt for your own generative overview of how you use it (via Monique Van Dusseldorp):

  • ‘’Make me a ChatGPT Wrapped for the past year: Summarise the year for me in Spotify Wrapped style, including the number of messages I sent, the topics I used the most, funny highlights, fun statistics (like the words I use the most), top 5 topics or conversations, and a description of my user style.”

Ask Anthropic’s Amanda Askell Anything

Michael Levin on Lex Fridman (3h20min)

There are so many types of intelligence out there.

Slop is The Economist’s word of the year

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

Thanks for reading Artificial Insights! Subscribe for free to receive new posts and support our work.

Welcome back to Artificial Insights, where the ongoing experiment in thinking out loud with machines keeps unfolding in public.

For me, this newsletter is a way to organize my thoughts around the hyperobject that is AI. It’s a phenomenon far too complex for any single person to grasp, and publishing my ideas forces me to articulate something meaningful about it. Even though I spend most days working with language models – mainly for coding –stepping back to reflect on the conditions shaping these capabilities feels necessary.

For example, I spent most of the past month developing a scalable approach for Envisioning to expand its public research in the shape of content hubs. We have long had a practice of releasing as much of our research as possible for public use. In developing Signals, we have learned how to leverage the output of multiple models in order to produce more credible results.

Having experimented with correlating Star Trek technologies to their real-life counterpart with Subspace Index, and identifying plausible exotic and undisclosed technologies with Xenotech, we recently rolled out another dozen free industry-specific reports, each covering 50+ relevant emerging technologies likely to shape its future.

The easiest way of diving into the research is on envisioning.com where we just rolled out a simple interaction where you enter the industry or sector you’re working in, and the widget returns a selection of relevant technologies you might want to learn about. If they are relevant, you can further save cards as favorites, and then organize your cards on a canvas, helping you decide how to act with regard to each technology. Would appreciate your feedback on this!

—

Below is this week’s issue of my ongoing ‘field notes’ for using AI.

If thinking refines ideas, creating brings them into form, building turns form into function, and deciding decides what deserves our attention, then organizing is the discipline that keeps the whole system coherent. It is how fragments become memory, how context survives long projects, and how the work stays searchable by a future self that no longer remembers making it.

This is the fifth chapter in the series of field notes documenting how I actually collaborate with AI. I have been asking AI to interpret how we interact, looking for patterns and repeatable lessons. Organizing with the help of language models has been a recurring theme. It is less glamorous than launching something new, but it is the part that prevents drift.

How I organize with AI:

• Draft hierarchy detection: Unordered notes become outlines when I ask for the “natural hierarchy” hiding inside them. The model suggests section headers, sequences, and missing bridges so drafts can graduate into finished forms.

• Compressed research ramps: Before diving into a fresh domain, I have AI help create an overview into a briefing with terminology, debates, and blind spots. It turns curiosity into orientation without sacrificing skepticism.

• Conversation synthesis: After interviews or workshops, I feed transcripts through prompts that surface tensions, contradictions, and outliers. Organizing isn’t just summarizing, it’s deciding which signals deserve another look.

• Naming as diagnosis: Describing a project and asking AI for naming directions reveals intent. If the suggested names all feel off, it is usually because the strategy is vague, not because the AI is wrong.

• Learning arcs and questionnaires: When designing curricula or forms, I use AI to order concepts the way people actually absorb them and to phrase questions that gather meaning instead of noise.

• Continuity checkpoints: On long projects, I periodically ask AI to reconstruct the narrative: what we decided, why it mattered, what signals would trigger a reconsideration. Organizing is partly about restoring continuity for the future version of me who forgot.

—

Until next week,
MZ

Fascinating study.

Sprawling interview about AI’s effect on labor and employability with Emad Mostaque (90 min)

Ilya on Dwarkesh (90 min)

If you haven’t watched The Thinking Game, the documentary about DeepMind is now free on YouTube. Don’t miss it. 90 min.

Not sure how useful these are. Nano Banana Pro generated infographics about when we’ll reach AGI.

If Artificial Insights makes sense to you, please help us out by:

• 📧 Subscribing to the weekly newsletter on Substack.

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

Thanks for reading Artificial Insights! Subscribe for free to receive new posts and support our work.

Welcome back to Artificial Insights, your near-weekly bulletin from the near future.

I’ve been thinking a lot about audiences lately. If AI becomes one of our primary audiences, then who are we really writing to? We used to create for others and for ourselves, and that hasn’t changed – but now there’s a third presence: the gaze of language models. The internet has been swallowed whole and returned to us as averages and probabilities. I haven’t seen studies comparing how much of what we read is written by people versus AI, but I can imagine the curve.

—

If thinking refines ideas, creating brings them into form, and building turns form into function, then deciding is what makes all of it matter.

This is the fourth chapter in an ongoing attempt to document how I actually work with AI. I started with thinking, moved through creating and building, and now I’m examining the dimension where these converge: the act of choosing what to do next.

I’ve learned that AI changes how decisions become visible. Most choices happen half-formed, shaped by instinct, pressure, or habit. When I externalize that reasoning through conversation with AI, the hidden structure reveals itself – what I’m optimizing for, what I’m avoiding, and where emotion is disguising itself as logic.

This clarity doesn’t make decisions easier, but it makes them more honest. By thinking aloud with a system that reflects intent without judgment, I can see my reasoning as something separate from myself – an object to examine, adjust, and stress-test before committing.

These field notes document how I use AI to decide. I’m not delegating judgment; I’m using AI to make the mechanics of choice legible enough to trust. The notes themselves are co-created with AI, mostly by asking ChatGPT to review its memories of me and surface the patterns underneath my interactions.

How I decide with AI:

• Talking through decisions: When facing unclear choices, I describe the situation, constraints, and stakes. AI helps map the decision space by restating options, surfacing assumptions, and clarifying what I’m actually optimizing for.

• Proposal development: Before sharing recommendations, I test them against AI by outlining options, assumptions, and risks. It helps me frame choices in ways that invite informed agreement rather than defensiveness.

• Market intelligence: When I enter unfamiliar domains, I build fluency fast by asking AI to explain landscapes, competitors, and trends. I intentionally diversify across providers to reduce single-model bias.

• Decision debriefing: After major calls, I talk through what was decided, why, and what signals would suggest I was wrong. AI helps capture rationale while it’s fresh, compounding future judgment.

• Financial planning: I model scenarios by describing cash flows, risk tolerances, and time horizons. AI helps align decisions with goals rather than reacting to urgency.

Deciding becomes deliberate when you treat judgment as something to externalize, examine, and refine. That’s the practice I’m trying to develop, and the one these notes are meant to record.

Until next week,
MZ

What else can I become? (56 min)

Spectacular interview by Sinead Bovell with Alexander Manu.

AI Product Masterclass (1h20min)

The inimitable Fei Fei Li on AGI.

Synthetic 3D environments are becoming a thing.

AGI is AI we haven’t invented yet (53 min)

Interview with Reid Hoffman on A16Z.

From Reddit, apparently showing the messages a teacher received from students caught using GPT.

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Welcome to your weekly download of memes and insights.

I am trying to organize observations about how I work with AI into somewhat of a framework. Two weeks ago I wrote about how I think with AI, last week about how I create with AI, and this week I’m focusing on how I build.

If creating turns ideas into form, building turns form into function.

The gap between a prototype and a working system is filled with decisions: how data moves, where logic lives, what breaks and why. Building with AI means treating architecture as conversation: describing intent until structure emerges.

What surprised me most about building with models is how much happens before code. When I explain what I want a system to do, the model shows me what I haven’t decided yet: the missing schema, the unclear dependency, the workflow I assumed was simple.

This literalness is the leverage. AI build for you by reflecting your specifications back with enough precision to reveal what you left implicit. The clearer the explanation, the closer the implementation lands to what you meant. Even when what you want isn’t what you need.

These field notes document how I am actually using AI, based on ChatGPT’s interpretation of our many conversations, coupled with a Cursor code repository with all my newsletter writing, both of which are used to document the behaviors I display according to the various AIs I interact with.

How I build with AI:

• Product design reasoning: Before touching an interface, I describe how a feature should behave – user intent, friction points, edge cases. AI helps expose assumptions and clarify purpose before design locks in decisions that are expensive to reverse.

• Interface sketching: I prototype flows entirely in language: what each screen does, what happens next, where feedback appears. Testing logic through conversation catches problems that wireframes miss and turns vague ideas into verifiable sequences.

• Architecture discussion: When deciding what lives where – server, client, local inference – I talk through the tradeoffs with AI. It doesn’t choose for me, but it makes structure tangible enough to evaluate stability, complexity, and constraint.

• Workflow automation design: I map full sequences before automating: triggers, data sources, transformations, outputs. The exchange turns scattered logic into a blueprint I can implement in steps, reducing confident mistakes from oversized prompts.

• Error explanation: Instead of jumping to fixes, I ask AI to explain what failed and why. This builds debugging intuition over time. I’m solving the current error by learning the pattern behind it.

Building becomes deliberate when you design through explanation. The model holds your intent steady while you refine it into working systems.

As trite as it sounds, AI doesn’t build for us – it builds with us, one augmented thought at a time.

Until next week,
MZ

Navigating Manifolds (50 min)

Dense conversation about AGI. Are LLMs creating knowledge—or just surfing known manifolds? A deep dive on entropy, chain-of-thought, and why true AGI means inventing new science, not just predicting the next token.

This essay by Jack Clark is trending:

Import AI

Import AI 431: Technological Optimism and Appropriate Fear

Welcome to Import AI, a newsletter about AI research. Import AI runs on lattes, ramen, and feedback from readers. If you’d like to support this, please subscribe…

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6 months ago · 356 likes · 68 comments · Jack Clark

The 1% Apocalypse: Eliezer Yudkowsky on AI’s Endgame (1h08)

Ezra Klein interview with Eliezer Yudkowski about AI doom. If you’ve ever wondered whether AI’s “helpfulness” could evolve into something uncontrollable, this is a must-watch.

This is not a technology that we craft. It’s something that we grow.

Teaching Humans to Think in the Age of AI (22 min)

Professor Po-Shen Loh about how to better use our thinking after AI more or less takes over. Loh shares why writing, reasoning, and caring about others may be humanity’s last competitive advantage—and how he’s training a generation of thoughtful leaders to keep us human. Can we still think for ourselves?

Andrej Karpathy: Teaching Humans in the AI Era (2h26)

Karpathy on the future of learning: AI won’t replace teachers—it’ll become the perfect tutor. Education will feel like the gym: fun, personalized, and addictive. His goal? Build Starfleet Academy for the AI age, where learning gives humanity its edge.

A great tutor gives you the perfect information at the perfect time. That’s the bar for AI education.

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

If thinking refines ideas, creating brings them into form.

I’ve been interrogating my own use of AI in an effort to create a document or framework that might help others benefit as much as I have from applying AI across as many aspects of life as possible.

I’m a technology maximalist – I believe more technology is the solution to most problems – and with AI, my approach has been to embrace it in every dimension I can. This newsletter has often been the recipient of my ongoing experiments and growing list of interesting use cases.

I’ve been documenting this process by asking the very models I interact with – models that retain memory of our exchanges – to help me summarize how they perceive my use of AI.

This has worked surprisingly well, giving me a baseline of observations to edit and refine. What follows is the second of seven short chapters based on those exchanges. I expect to consolidate these into a book of sorts later, using the weekly newsletter cadence as an excuse to keep the drafts flowing while building toward something hopefully useful.

What I’ve learned is that AI excels at one thing: reflecting intent. When I describe what I want to make, it gives me back exactly what I said, not what I meant.

That gap between thought and expression is where most of my work happens. The model’s literalness forces clarity. When I can’t explain an idea cleanly, it usually means I don’t understand it yet.

These field notes document how I actually work with AI day to day: writing, designing, testing, and refining in conversation.

How I create with AI:

• Writing refinement: Most of my writing starts as rough, fast drafts—notes dumped into ChatGPT. Then I ask the model to highlight weak transitions, redundant phrasing, or broken logic. I don’t let it rewrite; I use its feedback to rewrite myself. Over time this became a rhythm: I write → it critiques → I tighten. My voice stays intact, but sharper.

• Idea scaffolding: When I’m developing frameworks or tools, I use GPT to simulate reasoning. I’ll outline the structure, then ask the model to find gaps, stress-test assumptions, or suggest what’s missing. It’s like working with a brutally honest co-editor who never gets tired.

• Prompt engineering as writing: For image generation or structured outputs, I treat prompts as tiny pieces of creative writing. I describe intent, tone, atmosphere, and constraints before I mention style or detail. GPT helps me rewrite prompts until they express exactly what I want, not just what I imagine.

• Constraint as method: I often tell GPT to respond under limits: “explain this in 80 words,” “argue against your previous point,” “reframe this for a newsletter intro.” These constraints aren’t gimmicks; they sharpen the signal. They make thinking visible.

• Pattern recognition: When working across projects, I feed GPT fragments of old notes or past outputs to see what patterns emerge: recurring themes, phrases, or blind spots. It’s surprisingly good at showing the shape of my own thinking, like a mirror for intellectual habits.

Creation becomes dialogue when you treat the system as a collaborator in precision.

AI doesn’t imagine for you, but it holds you accountable to what you’re trying to say. That accountability turns making into a discipline of language, where every choice must be articulated to be realized.

Until next week,
MZ

Let’s get political (12 min)

Bernie Sanders on AI-induced unemployment.

The Limits of AI (20 min)

How data, information, knowledge and wisdom are different when it comes to AI, from IBM’s excellent research channel.

What’s your AGI strategy? (40 min)

YC talk by Jordan Fisher from Anthropic.

High Stakes (50 min)

Sam Altman is in PR mode and joined a couple of podcasts. I like these to get a better sense of the public persona behind the labs.

The Last Question (30 min)

For those who haven’t read Asimov’s The Last Question, the short story is highly recommended. Regardless of having read it, here’s a brilliant video explaining the story and how it connects to the current moment we are experiencing around AI.

Hallucinations can be expensive (7 min)

Deloitte AU and unchecked hallucinations.

We are living in the era of AI slop

What is intelligence? (70 min)

If the Antikythera Institute and Long Now Foundation mean anything you, don’t miss Blaise Aguera y Arcas from Google research on intelligence.

What could go wrong? (1h40)

Excellent interview with Jeff Hinton by Jon Stewart. Incredibly technical for a general audience.

If Artificial Insights makes sense to you, please help us out by:

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

This newsletter began as an attempt to make sense of our transition toward AGI. I still believe ‘generality’ is next for AI, and that most of us will experience it in our lifetimes. Doing this involves learning, testing, and building things with AI.

Over time, I’m realizing the work of writing weekly might become something else: a slow, structured attempt to document how I use AI. Not the tools themselves, but the mechanics of collaborating with different intelligences – how reasoning changes when shared, and what that reveals about our own thought.

Thinking with AI can be about outsourcing cognition, or about extending it. It’s a way of tracing how ideas form, fracture, and return clearer. By writing what I believe and watching it reflected back, I see my thinking as a living system: open, self-correcting, and occasionally surprising. What once stayed internal now becomes a shared exploration.

These observations are starting to form a sort of ‘field notes from a centaur’ – a collection of ways that AI is working itself into my life.

Each chapter explores a different dimension of that collaboration, starting with thinking, because everything else begins here: before creating, before deciding, before making meaning. These are not polished essays but field notes: records of actual interactions, co-created with AI, where AI helps me observe my own mind at work. They show what it’s like to reason with another intelligence, and what that process teaches about being human.

How I think with AI:

• Concept Refinement: When a thought is still forming, I describe it roughly and let AI question, restate, and reframe. The back-and-forth peels away noise until the core idea stands on its own.

• Strategic Thinking: Whenever I have a complex question to answer, I give AI as much contextual information as possible, outline my logic and ask for counterpoints or blind spots. The friction of disagreement sharpens judgment and reduces assumptions.

• Philosophical Correspondence: I often interrogate AI to better understand the open terrain of abstract questions — AI helps me map worldviews, contrast ideas, and debate paradoxes without forcing them into conclusions.

• Comparative Analysis: By rephrasing a single decision through multiple perspectives, I can see how framing itself shapes outcome and bias. Sometimes a single added word, like “assume” or “doubt,” can tilt the whole conclusion, which helps me see bias as a movable part of reasoning rather than a fixed flaw.

• Temporal Thinking: I describe observations about present signals and noted patterns into AI to imagine how they might evolve. The process turns foresight into a discipline of language, using words to model change.

Each of these field notes is co-written with the same system that observes how I use it. I literally ask ChatGPT to analyse my queries, my newsletter posts — and reveals the pattern behind my reasoning. It’s a kind of mirrored cognition: half human, half machine, thinking together in public.

Until next week,
MZ

Talking to Trees (37 min)

Artist Manuel Axel Strain at Vancouver AI blends Indigenous knowledge & machine learning to decode plant communication, dream with flowers, & regenerate forests through song. Wild? Maybe. Necessary? Definitely.

AI is asking us to go to places we haven’t been. Indigenous knowledge is asking us to remember where we come from.

Your Only Moat Is Speed (45 min)

Everyone’s stressing about moats in AI—what if OpenAI just clones your startup tomorrow? The answer: move faster than anyone else. Moats come after you’ve built something people want. Until then? Speed is the only moat that matters.

A moat is inherently a defensive thing—and you have to have something to defend.

Boring Biz, Big Bucks (27 min)

AI + Google Maps = local cash printer. The Boring Marketer drops a playbook for finding high-ticket, low-competition local niches—then automating media + lead gen with AI. Forget HVAC. Think koi ponds, smart homes, exotic car wraps. Quietly print money.

Most people chase competitive niches with sharks. We hunt quiet corners and use AI to scale.

Build Evals, Not Just Vibes (1h45)

Everyone’s building AI agents—but no one knows how to test them. Hamel Husain & Shreya Shankar say evals are the core skill of modern AI product builders. Less vibes, more signal. They break down how to debug, test, and trust your LLM workflows.

The new stack is: prompt → eval → iterate. Testing is the product now.

Sora 2: The Internet’s New Imagination Engine (20min)

Sora 2 brings realistic motion, native audio, and the wild new “Cameo” feature, OpenAI launched a full social app where your friends can cast you into any world. Think: TikTok meets Pixar meets AGI.

On the path to AGI, the gains aren’t just about productivity. It’s also about creativity and joy.

Source.

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

When the future whispers, what does it say?

There are as many ways of imagining the future as there are people trying. We inform ourselves, visualize, and backcast from the present to picture possible tomorrows.

LLMs are poor at this work: they excel at interpolation – blending what exists, but falter at extrapolation – the invention of new modes and systems. Some people, more comfortable with ambiguity, are attuned to discontinuity. For them, more futures are possible, and they are more likely to thrive when things get weird.

Solarpunk skyscrapers are practically a trope, an easy shorthand for the future. Media and language models alike default to park cities and green urbanism. Pleasant, yes, but revealingly narrow. Such recurring imagery exposes the limits of our collective imagination, the ways we design futures by recycling what feels safe.

AI itself is an imagined technology, a collective desire so strong it became inevitable. Whether envisioned as a voice in your head or a smooth-skinned android on deck, these futures show less about what’s possible and more about the boundaries of the present. Imagination, however wild, remains tethered to context. And AI rarely helps us cut the cord.

Those who want to reshape the future must step outside convention and open to discontinuity. Art, nature, psychedelics – whatever cracks the frame – remind us the futures worth creating don’t emerge from the center, but from the periphery. They arrive nonlinear, strange, and unexpected.

What does this mean for you?

The real work of imagining futures is about cultivating the capacity to sit with the strange, to invite in the discontinuous, to listen from the edges.

That futures worth living will not be generated by default settings, but by willing to step beyond them.

Until next week,
MZ

P.S. If you’re drawn to exploring futures from the edges, don’t miss our Artificial Insights WhatsApp community.

Reasoning like us

Quantum computing expert Scott Aaronson says GPT5-Thinking is remarkably close to human researcher skills.

I had tried similar problems a year ago, with the then-new GPT reasoning models, but I didn’t get results that were nearly as good. Now, in September 2025, I’m here to tell you that AI has finally come for what my experience tells me is the most quintessentially human of all human intellectual activities: namely, proving oracle separations between quantum complexity classes. Right now, it almost certainly can’t write the whole research paper (at least if you want it to be correct and good), but it can help you get unstuck if you otherwise know what you’re doing, which you might call a sweet spot. Who knows how long this state of affairs will last? I guess I should be grateful that I have tenure.

via /r/singularity

tl;dr: you still need expertise to determine what’s right, but the degree of autonomy that reasoning models display is changing how much we can rely on them (which will only increase).

Quick Links

• I got to hang out with legendary AI artist Ari Kuschnir the past few days at THNK FSTVL here in Amsterdam, which this week’s newsletter intro was written around. Ari creates unbelievable AI videos in multiple flavors, from surreal political commentary to documentaries about events that were not recorded. If you are not yet familiar with his work, spend a couple of minutes on his Instagram feed for glimpses of possible futures.

• The next AI bubble is about to burst via Carrie Beehan.

• Agents of change via Igor Botelho.

• Attorney Slapped With Hefty Fine for Citing 21 Fake, AI-Generated Cases via Alex Kustov.

• Mira Murati bridges several concerns and groups simultaneously via Moulsari Jain.

• Why Silicon Valley hasn’t quit LLMs via Parthasarathi.

• SheBuilds on Lovable via Sander Bogers.

• Using GPT-5 codex to improve my website in an hour, not days via .

• Why China said ‘no’ to stripped-down Nvidia chips via Guilherme Kujawski.

• Introducing Google Mixboard via .

The Bitter Lesson (67 min)

An hour with Richard Sutton about why LLMs are probably a dead end. Via .

Meta’s vision of entertainment is an infinite slop machine

Don’t miss the replies.

Someone built and trained an LLM using redstone in Minecraft (3 min)

https://x.com/tokenbender/status/1972381941933674596

YouTube version.

If Artificial Insights makes sense to you, please help us out by:

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

Everything is political.

As someone steeped in U.S. culture thanks to growing up on a diet of video games, rock music, and the early Internet, I feel at least partially connected to the “American way.” Having never lived in the U.S., my experience and observations are strictly those of a curious outsider, now watching what happens when the inmates run the asylum.

History books teach us the perils of totalitarianism, but if your world knowledge is insular, the necessary context to perceive the systematic institutional dismantling taking place will seem, like everything else American, exceptional.

Technology is adjacent to politics, and it is unclear to me how politics actually shapes it.

Each government applies its best minds to controlling its technological future, but because technology changes so quickly, there are no playbooks – only best guesses and market dynamics.

The U.S. AI industry is arguably more beholden to capital than policy, but I wonder how long that will last. The bull in the china shop will eventually stumble out, but the dismantling and repurposing of political power seems unlikely to be reversed. More plausibly, the U.S. status quo will normalize into a more politicized environment, riddled with visible and invisible biases, just to keep operating.

How this affects the rest of the world is impossible to anticipate. Resistance grows under pressure. Alternatives to hegemonic technology might proliferate. U.S. industry and expertise might expand outward differently – not with blind assumptions, but humbled into positions where leverage is exerted differently.

I remain optimistic about our future with AI.

Despite the turmoil, I’ve noticed how apolitical and international the average ML engineer tends to be. Leaders will have no shortage of regulations to navigate, but the talent needed to build seemingly carries little ideology. The engineers, researchers, and users form a diffuse, transnational counterweight to centralized power.

The open question is whether that will be strong enough to resist capture – or whether, as history suggests, resistance itself will become the driving force of innovation.

Until next week,
MZ

Vibe Coding (Amsterdam, Oct 7)

I’m co-hosting the third edition of our vibe coding hackathon at the Embassy of the Free Mind together with . The last two sessions sold out, so if you’re interested, reserve your spot now on Luma.

Planetary Computation (55 min)

Spectacular lecture by Benjamin Bratton at The Long Now about the plausible future of AI.

What if humans are a phase in the history of technology?

Context Beats Prompts in AI (14 min)

Tiago Forte (Founder of Forte Labs) argues that the real power of AI isn’t in bigger models or clever prompts. It’s in context: system instructions, personal preferences, and project knowledge that turn AI into a true team member.

Claude actually has seven different trigger words that… force the LLM to do deeper analysis and deeper thinking. By using them, you can easily access a 10x improvement without further context.

ChatGPT Research Paper

ICYMI: OpenAI paper on how people use AI in chat. Lots of interesting graphs & insights.

Economic Research Chatgpt Usage Paper

9.22MB ∙ PDF file

Download

Download

Vibe Coding in Prod (32 min)

Excellent example of the magic of vibe coding by Dive Club.

Recall Me Maybe (13 min)

Via :

Stephen Fry and Gemma Whelan star in a new FT drama written by David Baddiel, exploring AI, memory and truth. Fry plays a grandfather with dementia who uses AI to fill in gaps in his memory. While reviewing the archive of his life his family makes a shocking discovery. Which memories are really true? And how AI is defining who we are?

Keep Thinking with Claude (90 sec)

Interesting aesthetics from Anthropic, positioning Claude around problem-solving and helping you think.

The Case for AI (26 min)

Pretty insightful interview with relatively little hype with Reid Hofmann.

If Artificial Insights makes sense to you, please help us out by:

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.

One of the more surprising side effects of “infinite intelligence” is realizing just how much of my day is now spent interfacing with AI. The last time I felt something this profound was discovering the internet in the early 1990s – sensing an endless source of knowledge that would inevitably reshape how I learn, work, and connect with the world. Back then it was TCP/IP and HTML; today it’s tokens and transformers.

ChatGPT launched just over a thousand days ago.

If you had asked me in mid-2022 whether AI was about to transform my work so completely, I would have laughed it off. Yet here we are. Three years later, I can’t picture a world without it. Every decision I make, every experiment I run, every line of code I write – AI touches all of it. To ignore it would feel negligent.

I don’t try to convince anyone of “the right way” to use these tools.

What excites me is precisely how different everyone’s paths can be. For me, AI has already made me a better developer, a sharper CEO, and a more curious human. For you, it might mean something entirely different. That’s the promise: infinite directions, infinitely personal.

This newsletter is my way of leaning into that promise—with careful optimism, an eye on the hype, and a belief that we’re still only at the beginning.

Until next week,
MZ

Vibe Coding (Amsterdam, Oct 7): I’m co-hosting the third edition of our vibe coding hackathon at the Embassy of the Free Mind during World AI Week together with Derek Lomas. The last two sessions sold out, so if you’re interested, reserve your spot now on Luma.

The Fragile Foundations of the AI Boom (22m)

The AI hype cycle may be smoke and mirrors. Are we mistaking marketing for a real revolution? Ed Zitron argues the AI industry is overextended, financially unsustainable, and built on shaky dependence on OpenAI and NVidia.

This boom has come from building capacity for a revolution that never even started arriving.

How Cloud Code Ships Fast (40m)

Cat Wu’s approach to Cloud Code: if you’re building AI tools, ship the demo, not the deck—let users teach you what sticks.

For new features there should be no onboarding UX… it should be intuitive via the feature name.

From AlphaFold to AGI (31m)

Demis Hassabis calls DeepMind Alphabet’s AI “engine room,” spotlighting Genie 3’s world models and robotics. True creativity is the bar for AGI. Until then, world models and robotics are the trail to watch.

AI today… doesn’t have true creativity… it can’t come up with a new conjecture yet or new hypothesis.

Robots are closer than you think (1h28m)

The robotics flywheel might starts soon. Sergey Levine says they’ll be here in 5 years or so.

Altman on Morality and Power (57m)

Sam Altman says ChatGPT isn’t alive, just math. Shaping its moral framework is unavoidable. He backs user privacy as “AI privilege” and admits the toughest weight is knowing how small design choices affect hundreds of millions daily.

If I could get one policy passed, it would be AI privilege… like attorney–client privilege for your conversations with AI.

Superagency (1h20m)

Great interview with Reid Hoffman.

From Substack

@jasmi.news

🌻 42 notes on AI & work

Hello from Shanghai! One of my main preoccupations this year has been AI’s labor impacts. It’s been a bit whiplashy—I went from intense concern to feeling slightly more sanguine by the time everyone started freaking out about the “new grad jobs crisis.” I'm planning some deeper investigations into these questions for when I’m back (e.g. what does an “AI…

Read more

8 months ago · 176 likes · 24 comments · Jasmine Sun

If Artificial Insights makes sense to you, please help us out by:

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Artificial Insights is written by Michell Zappa, CEO and founder of Envisioning, a technology research institute.