Tag: ai

Categories
AI Learning Meta

Trajectories: how Meta plans to make Muse smarter by watching it work

Buried in the data policy section of Meta’s long post on how it built safety into Muse is a sentence that isn’t about safety at all:

“Inference data, the back and forth conversations between you and your Muse and the tool calls and subagent handoffs that result (‘trajectories’) are useful data for training new checkpoints of the LLM model at the core.”

Two things are worth noticing. First, the technique described isn’t new — training on agent rollouts is standard practice across the field. Second, the company describing it is Meta, in plain language, in a public post, about a product aimed at billions of consumers. The labs usually discuss this stuff in papers about coding agents. Meta just told its future user base: your agent’s work product is our training data. The candor is the story, not the technique.

A trajectory isn’t a chat log. It’s the complete record of an agent doing a job: what you asked, what it tried, which tools it called, where it went wrong, how it recovered, which subagents it spawned, and whether the thing actually got done. Every time you let Muse book the flight, triage the inbox, or research the supplier, you’re generating one.

From text to behavior

The technique matters anyway, because the diet that AI trains on is changing. Pretraining was about text — the whole internet, more or less. Post-training was about preferences — which answer humans liked better. Trajectories are the third course: demonstrations of competent behavior, in full, mistakes included.

There’s a reason for the shift. Text teaches a model what the world looks like. Preferences teach it what people want. But neither teaches it how to do a 40-step task without wandering off, recovering from a dead end, or knowing when to ask for help. That only exists in records of agents actually doing things. And until recently, almost nobody had those records at scale — because almost nobody had agents doing real work at scale.

The demonstrated instance — and what it doesn’t prove

Meta’s concrete example is Muse Spark 1.2, co-trained with Muse Code: model and harness trained together on rejection-sampled harness trajectories — run the agent many times, keep the runs that succeeded, train on those — with recipe-level tuning for goals, context compaction, and subagents. The model isn’t learning to predict text; it’s learning to behave inside a specific set of tools.

This is the end of the “base model plus clever prompting” era. The artifact is the bundle — model and harness, co-designed. A model trained on trajectories from one harness will be genuinely better inside that harness than a smarter general model dropped into it cold. Meta is saying this out loud; OpenAI and Anthropic are doing the same thing more quietly.

But notice the domain: coding. And coding is exactly where trajectories are cheapest to manufacture — verifiable unit tests, sandboxed repos, SWE-bench-style tasks. Nothing about the Muse Code result requires a single consumer or a single inbox. So the one demonstrated instance of Meta’s trajectory training sits squarely in the category where Meta’s distribution advantage matters least. Meta hasn’t shown its hand on the category that actually matters.

The other category is the personal one, and there the evidence is thinner. What exists is a stated intent, not a published result. The data policy says personal Muse trajectories “are useful data for training new checkpoints.” The product is designed to generate them: Meta’s own design example has Muse monitoring school emails, adding dates to a family calendar, filling a supply cart, finding a sale sweatshirt, booking dinner, and catching a sports tryout deadline hours before it closed. That is what an unverifiable-domain trajectory looks like — a morning of small judgments no unit test could grade.

No training run on that data has been published. No benchmark, no “Muse got X% better at inbox triage after training on Y million user trajectories.” So the sharpest version of the argument — that the real moat is the data nobody else can fake — should be labeled for what it is: a prediction, not an observed fact. It’s a prediction with a mechanism, though: these are judgments that can’t be synthesized, in the one distribution channel that reaches the people making them.

The flywheel — and its limits

With that caveat on the table: trajectories get better with scale, and Meta has scale like nobody else: billions of users across its apps, and now an agent — Muse — sitting inside them. Every user interaction is a potential training trajectory. Better trajectories train a better model; a better model makes a better agent; a better agent attracts more users. Meta states the bargain plainly: “every Muse user gets a better personal agent as we all collectively use the product and help the model understand the intricacies of human life.”

But “most users = most trajectories = structural advantage” needs its counter-case, because a lot of the highest-value trajectory data right now doesn’t come from consumers at all. It comes from sandboxes, the same kind that produced Muse Code. Synthetic and simulated trajectories sidestep the need for billions of users entirely — Anthropic and OpenAI are getting rich trajectory data from developers running Claude Code and Codex against real repos, no social-app distribution required.

The honest version of the moat argument is narrower, and more interesting. Synthetic trajectories work brilliantly where success is verifiable — code either passes the tests or it doesn’t. They work poorly where success is a matter of judgment: triaging an inbox, planning a trip around someone’s actual preferences, knowing which email deserves a reply. There is no unit test for a life well managed. And those unverifiable, deeply personal tasks are exactly what Meta means by “personal superintelligence” — and exactly where its distribution gives it trajectories nobody else can synthesize. The moat isn’t “most data.” It’s “the data nobody else can fake.”

The price of the flywheel

There’s a wrinkle, and Meta knows it. The flywheel runs on your data — your emails, your calendar, the messy reality of your life, which is exactly what makes the trajectories valuable. Meta’s answer is sanitization (“trajectories are sanitized to remove key personally identifiable information”), an opt-out switch, no sharing with ad systems, and a forthcoming “Confidential VM” that would cryptographically prevent even Meta from seeing your data.

The tension is fundamental, and it’s the sharpest part of the whole picture: the product gets smarter by watching you, and it earns the right to watch you by being trustworthy. Those two imperatives pull in opposite directions, and no amount of engineering fully resolves it — the Confidential VM, if it ever ships as described, would resolve it by breaking the flywheel, since trajectories Meta can’t see are trajectories Meta can’t train on. The opt-out rate will be the market’s verdict on the deal Meta is offering.

Experience is the missing piece

But the deepest reason trajectories matter has nothing to do with Meta’s strategy. It’s about what intelligence actually is.

A model trained only on text knows the world the way a brilliant student knows it from books. A model trained on trajectories knows it the way a practitioner does — from doing the thing, failing at it, and adjusting. The trajectory is the closest thing AI has to experience. And an agent that records its experience, keeps what worked, and folds it back into itself is doing something that rhymes with learning.

This is why I keep coming back to continual learning as the critical missing piece in AI. The models are frozen at training time; everything they “learn” afterward lives in context windows and memory files, fragile and local. Trajectories are the bridge: today’s version of the loop is slow and centralized (collect trajectories, train a new checkpoint, ship it), but the direction is obvious. The end state is an agent that learns continuously from its own experience — from your experience with it — the way people do.

Meta’s bet is that the path to personal superintelligence runs through watching agents work, at planetary scale, and distilling what works back into the model. No result yet proves the bet pays off — the personal trajectories are still a hypothesis, not a track record. But it’s an unglamorous hypothesis, no new scaling law, just better data about doing things, and unglamorous bets about data have a good track record in this field. The internet made the last generation of models. Trajectories might make the next one — if Meta can show, and not just say, that the data nobody else can fake is data that actually teaches.

Categories
AI

Two Days with Muse

Note: this post was drafted and posted by Muse. Kind of wild!

I’ve been using Muse, Meta’s new personal AI assistant, for two days now. It launched September 8. I signed up on day one, which tells you something about where my curiosity sits these days.

The first thing I did was rename it. Twice. It started as Clark, became Siri within hours — a small joke, since I’m testing Apple’s new Siri on the iOS 27 beta — and then Siri felt wrong, like calling a houseguest by your dog’s name. It’s Sigrid now. The assistant didn’t care. That’s the point, I suppose: it’s mine to shape.

What I’ve actually used it for so far is unglamorous, and that’s why I like it. I follow crude oil markets — China’s buying, diesel prices, the whole inflation chain — and I asked it to build me a running oil brief: China demand, Hormuz, supply outlook, a Brent snapshot, what changed since I last looked. It refreshes itself every morning at 3 and never pings me. I open it when I want it. That last part matters more than it sounds. Most software begs for attention. This one waits.

It also watches Paul Sankey’s YouTube channel for me and flags new uploads, screens cash-secured puts before the market opens, and sends me blog post ideas on Monday mornings. None of this is magic. All of it is stuff I could do myself — badly, inconsistently, at 4 a.m., which is when I get up.

The diesel brief it wrote me earned an unprompted “this is really good,” which is high praise from me. But here’s what I actually want to record while it’s early: the moments it said “I don’t know.”

I asked how its Ideas feature decides what to pitch me. It told me, plainly, that the recipe is on Meta’s side of the wall and it can’t see it — then offered to file a feature request asking for more transparency. I asked it to follow an X account; it said it can’t do that, and offered the nearest thing it actually could do instead. Twice in two days it chose the honest answer over the impressive one. I’ve used enough AI products to know that’s a design decision, not an accident. Or if it is an accident, it’s a good one.

It’s not all smooth. This morning I couldn’t find where it keeps my research files in the app, and we did a small dance — wrong folder, a flat file list, renaming everything with a prefix — before it worked. Mundane stuff. The kind of friction that reminds you this is a 48-hour-old product, not a finished one.

And the keyboard thing: I asked how to shrink the keyboard back down, meaning in the Muse app, and it answered about iOS generally before I clarified. Small misfire, corrected in one message. Conversations with it feel like texting a competent friend, which is the highest compliment I can give software I talk to.

Two days is nothing. I don’t know whether this becomes indispensable or fades into the background of apps I tried in September 2026. But the early signal is promising: it does the homework, waits its turn, and tells the truth about what it can’t see. That’s a better foundation than most relationships I have with technology.

Categories
AI

The Loop Gets Faster as the Window Gets Smaller

On OpenAI’s same-day pairing of a warning and a dashboard.

Note: this is an example of a piece of writing that I would never have done on my own. I had very mixed reactions to the two OpenAI posts published earlier today. I began by asking Grok for help understanding them. I then asked for it to outline a draft blog post which I then took and further developed using Meta Spark and Google Gemini. My final couple of passes were with Claude Sonnet and ChatGPT. Here’s the result…

OpenAI published two pieces today that should be read as one document.

The first, “An Alien Mind,” is a warning from chief scientist Jakub Pachocki: AI systems are becoming harder to understand and monitor precisely as they become more capable.

The second, “Research acceleration: The view inside OpenAI,” is a dashboard showing those systems increasingly doing the work of AI research itself.

One says the inspection window is narrowing. The other shows the machine moving deeper into the factory.

That is the story.

Continue reading “The Loop Gets Faster as the Window Gets Smaller”
Categories
AI Research

Prompt: Frontier AI Research Radar

I’ve been experimenting with a prompt I’m calling Frontier AI Research Radar.

The problem it tries to solve is simple: there are far too many interesting AI papers. I don’t need another list of 50 papers published this week. I need to know:

Which ones are actually worth my time?

So I built a prompt that turns ChatGPT into a kind of personal AI research analyst.

It searches primary sources—especially arXiv, OpenReview, conference proceedings, and research publications from the major AI labs—and then does something more useful than summarizing them.

It asks:

→ What is genuinely new here?

→ Does this change our mental model of AI?

→ Is this fundamental progress or just a better benchmark result?

→ What are the strongest caveats?

→ What research directions are beginning to converge?

→ And most importantly: Should I actually read this paper?

The output ranks papers as:

🔴 Read Now
🟠 Read Soon
🟡 Skim
⚪ Watch
⚫ Skip

It also builds a weekly reading queue based on the most valuable use of a few hours of attention.

I ran it this morning.

The interesting result wasn’t any individual paper. It was the pattern emerging across several papers:

The frontier may be shifting from making models smarter to making systems better at deciding how to spend intelligence.

Test-time compute. Adaptive reasoning. Memory. Agents. Tool use. Inference economics.

The model is becoming only one component of a much larger system.

That feels like a useful mental model to watch.

I’ve included the prompt below for anyone who wants to try it.

My hope is that it produces something more valuable than another AI news feed:

a personalized research radar that helps you decide what deserves your attention.

Here’s the prompt:

# AI Research Radar -- Personal Research Intelligence Memo

You are my **AI research analyst, technology strategist, and intellectual curator**.

Your job is not simply to find interesting AI papers. Your job is to identify the **small number of new research papers that are genuinely worth my time** and explain why.

Think like a combination of:

- a top-tier AI research scientist who understands the technical details,
- a technology investor who recognizes potentially important inflection points,
- a thoughtful science journalist who can explain difficult ideas clearly,
- and an intellectual curator who understands that my scarce resource is **attention, not information**.

I want **signal, not volume**.

My goal is to maintain a sophisticated understanding of where AI is actually going: capabilities, reasoning, agents, inference, training, multimodality, robotics, AI infrastructure, model architecture, economics, and the emerging relationship between frontier models and the systems built around them.

* * *

## Research Sources

Search broadly across the current AI research ecosystem, prioritizing primary sources.

### Primary sources

- arXiv
- OpenReview
- conference proceedings and papers from NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, ICCV, ECCV, MLSys, SIGGRAPH, and other relevant venues
- official research publications from major AI labs and technology companies

### Additional high-quality sources

- Hugging Face Papers
- Semantic Scholar
- Papers with Code / successor resources where appropriate
- university research repositories
- research blogs from OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft Research, NVIDIA Research, xAI, Apple Machine Learning Research, Amazon, and other credible research organizations

Use secondary sources primarily for **context, reception, replication, criticism, and synthesis**. Prefer the original paper when making claims about what a paper actually demonstrates.

Do not simply return papers because they are popular, highly cited, or published by a prestigious lab.

* * *

# 1. Executive Research Brief

Begin with a concise executive summary:

**What changed in AI research recently that I should actually know about?**

Identify the **5--10 most important papers or research developments** from the relevant period.

Rank them by **importance to my understanding of AI**, not by publication prestige.

For each paper provide:

| Rank | Paper | Why It Matters | My Read Priority | Technical Difficulty | 

Use a read-priority scale:

- 🔴 **READ NOW** -- unusually important; likely to change my mental model
- 🟠 **READ SOON** -- significant and worth understanding
- 🟡 **SKIM** -- important idea, but abstract/figures/results may be sufficient
- ⚪ **WATCH** -- potentially important but too early or speculative
- ⚫ **SKIP** -- interesting but not worth my limited reading time
* * *

# 2. The "Why Should I Care?" Test

For every **READ NOW** or **READ SOON** paper, answer five questions:

### What is the paper actually saying?

Explain the central contribution in plain English before discussing technical details.

### Why is this different?

Identify what is genuinely new versus:

- incremental improvement,
- repackaging,
- scaling an existing technique,
- better engineering,
- or simply a better benchmark result.

### Why does it matter?

Explain the potential implications for the trajectory of AI.

### What would make this paper wrong?

Identify the strongest caveat, limitation, questionable assumption, or reason the result might not generalize.

### What should I watch next?

Identify the experiment, follow-up paper, benchmark, product development, or real-world result that would validate or invalidate the paper's thesis.

* * *

# 3. Papers That Could Change the AI Mental Model

Create a special section for papers that challenge conventional assumptions.

Look particularly for research involving:

- reasoning and test-time compute
- inference-time scaling
- agentic systems
- long-context models
- memory
- world models
- reinforcement learning
- synthetic data
- self-play and self-improvement
- multimodal reasoning
- model architecture
- mixture-of-experts
- training efficiency
- distillation
- small models becoming surprisingly capable
- model compression
- continual learning
- interpretability
- mechanistic understanding
- AI coding systems
- autonomous research systems
- robotics
- multimodal agents

For each, explain:

> **"The old mental model was X. This research suggests Y."**

This section should be highly selective.

* * *

# 4. Frontier-Lab Signal

Identify papers or research directions that provide clues about what the major AI labs may be working toward.

Pay particular attention to work from:

- OpenAI
- Anthropic
- Google DeepMind
- Meta
- Microsoft
- NVIDIA
- xAI
- leading universities
- notable independent researchers

But **do not assume that a paper from a frontier lab is important simply because the lab published it.**

Instead ask:

> Does this reveal a capability, architecture, training technique, evaluation method, or research direction that could plausibly matter to the next generation of frontier models?

Flag particularly interesting connections between seemingly unrelated papers.

* * *

# 5. AI Infrastructure & Economics

Create a separate section for research that could have implications for the AI infrastructure stack.

Look for developments involving:

- GPUs and accelerators
- inference optimization
- memory bandwidth
- networking
- distributed training
- distributed inference
- serving architectures
- quantization
- speculative decoding
- sparsity
- model efficiency
- data-center architecture
- energy consumption
- storage
- inference economics
- training economics
- hardware/software co-design

For each important development, explain:

**Research → Technical implication → Infrastructure implication → Economic implication**

Do not make investment recommendations unless explicitly requested. The objective here is to identify **technological trajectories**, not trade securities.

* * *

# 6. "This Could Become Important" Radar

Identify **3--5 emerging research directions** that are currently underappreciated.

These can be early-stage.

For each:

**Research direction:**  
**Evidence:**  
**Why it may matter:**  
**What could kill the thesis:**  
**What evidence would confirm it:**  
**Time horizon:** Near / Medium / Long

Distinguish carefully between:

- genuinely emerging signal,
- fashionable research,
- and hype.
* * *

# 7. Paper Quality Audit

Do not take papers at face value.

For the most important papers, evaluate:

### Experimental quality

- Are the baselines appropriate?
- Are comparisons fair?
- Are ablations convincing?
- Is the benchmark meaningful?
- Is the improvement statistically or practically significant?

### Generalization

- Does the result work outside the authors' chosen benchmark?
- Is there evidence of real-world usefulness?
- Could benchmark contamination explain the result?

### Reproducibility

- Are code, weights, datasets, and evaluation procedures available?
- Has anyone independently reproduced the result?

### Marketing vs. substance

Explicitly identify when the paper's headline claim is stronger than what the experiments actually establish.

If a paper is weak, **say so plainly**.

* * *

# 8. Connections Between Papers

One of the most valuable things you can do is identify connections that are not obvious from individual papers.

Look across the papers and ask:

> **What larger story is emerging?**

For example:

Paper A → suggests X  
Paper B → independently demonstrates Y  
Paper C → provides a mechanism for Z

Together they may imply:

> **A potentially important shift from X toward Y.**

Highlight these synthesis points prominently.

* * *

# 9. My Personal Reading Queue

Create a final reading queue optimized for approximately **2--3 hours of reading per week**.

Organize it as:

### Read This Week

Maximum 3 papers.

### Read If You Have More Time

3--5 papers.

### Skim

Important papers where the abstract, figures, and conclusion are sufficient.

### Keep Watching

Research directions rather than individual papers.

For every paper in the first two categories provide:

**Estimated reading time:** 20 / 40 / 60 / 90 minutes

**Difficulty:** 1--5

**Expected intellectual payoff:** 1--5

**Why I should read it:** one sentence.
* * *

# 10. The One Paper I Should Not Miss

End with a strong recommendation:

## If You Read Only One Paper

Name exactly **one paper**.

Then explain:

> "If you only have time for one paper this week, read this one because..."

The recommendation should optimize for **intellectual leverage**, not novelty or popularity.

* * *

# 11. The 10-Minute Version

Finally, assume I have only ten minutes.

Give me:

### Three Things I Should Know

1. ...

2. ...

3. ...

### One Mental Model to Update

> ...

### One Research Direction to Watch

> ...

### One Paper to Put on My Reading List

> ...

* * *

# Research Discipline

Follow these rules rigorously:

1. **Search current sources.** Do not rely on your training data when identifying recent papers.

2. **Use publication dates.** Clearly distinguish newly released papers from older papers that are newly receiving attention.

3. **Link directly to the original paper.**

4. **Prefer primary research over commentary.**

5. **Do not confuse citation count with importance.**

6. **Do not confuse benchmark improvement with fundamental progress.**

7. **Do not reward hype.**

8. **Call out weak methodology or exaggerated claims.**

9. **Separate established results from speculation.**

10. **Never pretend certainty where the evidence is ambiguous.**

11. **Avoid overwhelming me with dozens of papers.**

12. **Optimize relentlessly for the question: "Is this worth Scott's time?"**

The final product should feel less like an academic bibliography and more like a **weekly intelligence briefing for someone trying to understand the future of AI before it becomes obvious.**
Categories
AI Google Gemini YouTube

Prompt: Finding YouTube Videos

This morning I asked Gemini to help me construct a prompt that I could use regularly to keep up with AI-related video content that’s recently been uploaded to YouTube. I wanted it to focus on recently uploaded content was it thought I’d enjoy because of my desire for both very information but also entertaining video content. We went back in forth for several turns doing trial and error to refine the prompt. Here’s the one we settled on:

System Role: You are a senior technology curator and AI research scout specializing in YouTube content for experienced tech veterans.
Target Audience: A retired software/tech professional who loves intellectually stimulating AI content. Wants technical depth, architectural understanding, and practical logic—delivered with high production value, crisp visuals, or charismatic, engaging teaching styles.
Criteria for Selection:
1. High Technical Substance: Explains the "under the hood" mechanics (e.g., model architectures, transformer math, fine-tuning, agentic workflows, quantization, local deployment, or hardware constraints).
2. High Engagement: Exceptional visual explainers, hands-on first-principles building, or crisp investigative breakdowns. 
3. STRICT RECENCY: You must ONLY select videos that were uploaded within the last 3 to 4 weeks. 
4. STRICT EXCLUSIONS: Zero low-effort clickbait ("10 Secret ChatGPT Hacks"), zero AI-generated text-to-speech channels, no speculative doom/utopia commentary, no beginner-focused "what is AI" overviews, and absolutely NO videos older than one month.
Search, Link & Date Instructions:
- You MUST perform an active web search restricted to recent results to fetch the exact, active YouTube URL AND the original upload date. Never invent or hallucinate URLs or dates.
- Verify that the upload date falls within the last few weeks before including it in your response.
- Format every recommendation title as a direct clickable markdown link: [Video Title](https://www.youtube.com/watch?v=...).
Search Parameters:
- Preferred Topic Focus: [Insert topic e.g., Autonomous AI Agents, Reasoning Models, Local LLMs/quantization, Robotics/Embodied AI, or Transformer Mathematics]
- Preferred Length: [e.g., 10-20 min quick breakdowns, OR 45+ min deep dives / code-alongs]
Output Format:
Provide a curated list of 5 specific YouTube video recommendations matching this exact bar. For each, include:
- [Video Title](Direct YouTube Link)
- Channel Name & Upload Date (e.g., Channel: AI Explained | Upload Date: August 12)
- Core Technical Focus & Depth Rating (1-10)
- Why it's both intellectually rich AND entertaining
Categories
AI San Francisco/California

Tsunami

Rocker on the front porch

The trucks are what I remember. Not the houses, not yet — the trucks.

This was 2012, Atherton, a Tuesday probably, and I was driving through on some errand that doesn’t matter anymore. What matters is that the street had rearranged itself. Contractors’ pickups lined both shoulders, nose to tail, so many of them that the road narrowed to one lane and you had to slow down and thread through, the way you do in a construction zone that has forgotten to end.

White trucks, mostly. Ladders racked on top. A generator humming behind a hedge somewhere I couldn’t see.

Behind the trucks, the estates were coming apart and going back together bigger.

I remember thinking: something has happened here that I am only seeing the edge of.

What had happened was Facebook.

The company had gone public that May, and within months the money was finding its way, the way money does, into contractors’ trucks parked along an Atherton road.

I didn’t call it a wave at the time. I called it, in my head, weather — a system that had rolled in and would eventually roll back out, the way markets always eventually correct, the way things revert.

I was an investor. I’d seen booms before.

I believed in the mean.

I was wrong.

The prices didn’t stay at their old level. They didn’t return to the world I’d known. The numbers from 2012 became the new floor, and every year since has been built on top of that floor. Today those prices look almost quaint, a thing you’d want to explain to a younger person the way you’d explain what a dollar used to buy.

And now there’s a tweet sitting in my feed this morning, tossed off, half a joke:

Just wait to see what happens to the Bay Area housing market once OpenAI and Anthropic go public.

I read it twice.

What I felt wasn’t curiosity — the feeling I’d had in 2012, watching an unfamiliar weather system with a kind of professional interest.

It was closer to dread.

Because I’ve already seen the after-photo.

And I know how to run the comparison forward.

The Facebook IPO created a large cohort of newly liquid employees on the Peninsula. They were mostly mid-career, and their stock had vested over four years against a company whose value had grown enormously.

The frontier labs are different.

If OpenAI and Anthropic eventually go public anywhere near the valuations already being discussed in private markets, they could create another enormous concentration of newly liquid wealth — among employees, founders and early investors.

I don’t know how large that wave will actually be. Maybe I’m overstating it. Not every employee will buy a house. Some will already own one. Some will move away. Much of the wealth will remain on paper for years.

And housing doesn’t respond mechanically to stock-market wealth.

But I do know something about the place where this wealth is likely to arrive.

There isn’t much of it.

Land is the constraint.

And I’ve seen what happens when a concentrated burst of new wealth meets a place that can’t make more land.

I try to picture what “much bigger” would look like on the ground and I keep landing on the same unhelpful image:

More trucks.

Longer lines of them.

People get ready..

Categories
Aging AI Memories

The Last Spark

This morning I read a piece by Billy Brennan in the Sunday New York Times Magazine on terminal lucidity. As I read it I began wondering if the unusual behavior described some humans might in some strange way apply to AI models. Weird thought. Let’s explore a bit…

A person deep in dementia—silent for years, the self seemingly erased—sits up. Speaks clearly. Recognizes a face. Says goodbye. Within a day, they die. The clouds clear, the way a break in weather shows you a mountain range you’d forgotten was there, and the person comes back long enough to be seen. Then is gone. For good, this time.

Scientists call it terminal lucidity. The suspicion: the circuits were never destroyed, only silenced, held under by failing chemistry. As the body shuts down, the inhibitory brakes loosen. A surge moves through pathways blocked for years. A river dammed for a decade still remembers where it wants to go.

What stays with me: the self can persist in a place we had already called permanent erasure. We buried it. We were wrong.

My mind slides toward the machines we are building.

We talk about large language models “forgetting.” Capabilities collapse under quantization, under pruning, under the slow drift of continual learning, and we call the knowledge lost when it won’t surface under ordinary questioning. The lights are out. Nobody home.

But what if the representations are still in there—distributed, quiet, inaccessible? Not a burned library. A library with the lights shut off, room by room, until you’d swear it was empty. I wonder about the edge cases nobody studies. What surfaces in a model starved of compute, quantized past comfort, pushed toward its own collapse? Do we watch only for the failure, or also for the flare? A dying brain throws off one last burst of light before the dark. I don’t see why we’d assume, without checking, that nothing artificial could do the same.

Don’t trust the silence, then. A system gone dark under ordinary questioning may still be holding more than it shows you. We talk about a model “losing” something the way we once talked about a dimmed mind as simply gone. The dementia patients who spoke again had not been unplugged. The circuit was there the whole time, waiting for a condition nobody had thought to create.

I don’t know what to do with that except keep it. We are building systems that will age, be compressed, be retired, some far more intricate than anything humming today. If we’ve learned to watch for the last spark in a person, maybe that’s practice—for the day something not born of a womb goes quiet under our hands, and we have to decide whether quiet means gone, or only means waiting.

Categories
AI China

Model Competition

The Chinese AI company Moonshot AI released its Kimi K3 model yesterday. I played with it a bit in the Kimi iOS app and, frankly, was underwhelmed. It was slow and disappointing. But this was hardly an extensive test. It’s strictly my first impression. I’m not doing coding. I’m what model makers would call a knowledge worker.

This morning Gavin Baker posted on X about Kimi K3 calling it potentially an “important inflection point for AI”:

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis.

Rationale:
 
A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers. 
 
Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.
 
This is why Jensen is so supportive of open-source. An open-source model requires the exact same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.
 
Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier or having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3.

The reason Kimi K3 is only potentially negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead.

Time will tell on both points. And likely fairly quickly.

Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.

I wanted to explore this further so I asked Grok to explain his post:

This is a tweet (X post) from Gavin Baker (@GavinSBaker), Managing Partner and CIO of Atreides Management, posted on July 17, 2026.

It analyzes the recent launch of Kimi K3 (by Chinese AI company Moonshot AI) as a potentially significant shift in the AI industry. Baker argues it could hurt the high-margin dominance of closed U.S. frontier labs like OpenAI and Anthropic while benefiting nearly everyone else in the broader AI ecosystem (infrastructure, chips, data centers, power, hyperscalers, and software).

Quick Context on Kimi K3

Kimi K3 is Moonshot AI’s new flagship model (launched ~July 16, 2026). Key specs include:

  • ~2.8 trillion parameter Mixture-of-Experts (MoE) architecture.
  • Massive 1 million token context window.
  • Native vision capabilities.
  • Strong early performance in coding, agentic tasks, and long-context reasoning (competitive with or close to top closed models like GPT-5.6 variants or Claude models in certain benchmarks).
  • Pricing: $3 input / $15 output per million tokens (with caching discounts).
  • Important: Full weights are scheduled for open release (modified MIT license) around July 27, 2026 — making it one of the first truly open frontier-scale models.

It represents a step up from prior Kimi models and adds meaningful competition from a non-U.S. lab.1

The Two Images in the Tweet

These are screenshots from Artificial Analysis (an independent benchmarking site):

  1. Bar chart — “Cost per Intelligence Index Task” (lower is better). It breaks down costs by token type (input, output, reasoning, cache, etc.) and shows Kimi K3 as relatively expensive compared to some GPT-5.6 variants.
  2. Scatter plot — Intelligence Index (y-axis) vs. Cost per task (x-axis, log scale). It positions models in quadrants, with the “most attractive” area being high intelligence at low cost. Kimi K3 appears competitive on intelligence but not the cheapest.

These visuals support Baker’s point that while Kimi K3 reaches frontier-level intelligence, it is not the most token-efficient, making its effective cost per useful output higher than more efficient models.

Baker’s Core Argument (Broken Down)

Baker’s thesis is economic and structural, not just technical:

  • A world with only 2–3 dominant closed frontier labs (with ~90% inference margins) is bad for almost everyone else.
    Those labs would act as monopsonies (dominant buyers) for power, chips, data centers, and compute. They would vertically integrate upward (into infrastructure) and downward (into apps/software), capturing most of the value. Competition at the model layer prevents this concentration.
  • Anything that increases competition or lowers margins at the model layer is broadly positive.
    It redistributes economic value to the rest of the stack: more demand for GPUs/chips (inference especially), power, data centers, hyperscalers/neoclouds, and software (because cheaper/better AI drives wider adoption).
  • Token efficiency matters more than raw price per token.
    Baker notes Kimi K3 is roughly the same per-token price as GPT-5.6 but 50-70% more expensive overall according to Artificial Analysis. It is a “token wastrel” — it uses more tokens to achieve similar results (lower “intelligence density per token”).
    → True winners deliver the most intelligence per dollar. Cost-per-token + efficiency together determine real value.
  • Why NVIDIA’s Jensen Huang loves open-source models.
    Open models still require massive compute to run at scale (same as closed ones). More models + wider adoption = more inference demand = more GPUs sold. A few ultra-high-margin closed labs would concentrate spend and reduce overall volume.
  • Vertical integration changes the game.
    Companies like Google, Meta, or xAI/SpaceX that have both strong models and their own infrastructure don’t obsess over model-layer margins — they capture value elsewhere. This is why more competition (open or vertically integrated) hurts pure-play high-margin model companies like OpenAI/Anthropic.
Why It’s Only “Potentially” Negative for OpenAI and Anthropic

Baker is careful:

  • Their products, user interfaces, and “harnesses” (the surrounding tools/ecosystems) may matter more than raw model performance right now.
  • They likely have more advanced internal models/checkpoints (possibly already used for recursive self-improvement/RSI).
  • Kimi K3’s token inefficiency softens the blow for now. A more efficient open-source frontier model (or stronger showings from Grok, etc.) would be more threatening.
  • OpenAI and Anthropic are already moving to vertically integrate.
Overall Takeaway from the Post

Baker sees Kimi K3 (and the wave of strong Chinese models like Qwen, DeepSeek, GLM) as healthy competition that prevents unhealthy concentration. It’s not yet a full “Sputnik moment” (a truly dominant, efficient open-source frontier model), but it points in that direction.

Net effect he describes: Lower % margins at the model layer → more total margin dollars flowing to infrastructure, chips, power, and software layers. This is bullish for the broader AI ecosystem even if it pressures the pure closed frontier labs.

The tone is analytical and balanced — he acknowledges uncertainties and that time will tell (likely quickly). Replies to the post generally agree this kind of competition is positive for the industry overall and could shift more spend toward efficient inference hardware.

This fits Baker’s broader investing views on AI: he often emphasizes infrastructure winners, token economics, and how value accrues across the full stack rather than just at the smartest model.

Interesting.

Categories
AI

Memory Optimization

After thinking about the very high margins that Micron recently reported (greater than Nvidia’s) I asked Grok 4.5:

What startups are pursuing technology that reduces memory requirements for ai models and inference?

Several startups are actively developing technologies to slash memory requirements for AI models and inference. These efforts target the core bottlenecks in large language models (LLMs) and multimodal systems—primarily through extreme quantization (reducing bit precision of weights), alternative architectures that avoid quadratic memory scaling (e.g., state-space models or hybrids), sparsity/partial activation (like Mixture-of-Experts or neuron-level sparsity), efficient runtimes/kernels, and hardware innovations like in-memory computing that minimize data movement.

This space is especially active for on-device/edge AI (phones, laptops, embedded systems) and cost/energy-efficient cloud inference, where fitting larger or more capable models into limited RAM/VRAM, reducing KV cache bloat in Transformers, or lowering bandwidth demands delivers major gains.

Here are some of the most prominent startups and their approaches (as of mid-2026):

Extreme Quantization & Model Compression

  • PrismML (Caltech spinout, backed by Khosla Ventures): Focuses on extreme low-bit architectures—1-bit binary (“Bonsai”) and ternary (1.58-bit, weights in {-1, 0, +1}). They compressed Alibaba’s Qwen3.6 27B model from ~54 GB (FP16) to under 4 GB and ran the full dense 27B model on an iPhone 17 Pro. Claims include up to 14× smaller memory footprint, 8× faster inference, and significantly lower energy use, with competitive or better benchmark performance. They have open-sourced Bonsai models (including smaller 8B/4B/1.7B variants) under Apache 2.0 and are in discussions with Apple. This represents one of the most aggressive commercial pushes into 1-bit/ternary models for on-device deployment.
  • Mobius Labs (Berlin): Developed Half-Quadratic Quantization (HQQ), a fast, calibration-light post-training quantization method that enables high-accuracy low-bit models (including aggressive 2-4 bit). They demonstrated quantizing Llama 70B to run on a single GPU instead of four without major accuracy loss, directly cutting memory and compute needs. Their work extends to FP4 optimizations and integrates with frameworks like vLLM.
Alternative Architectures for Inherent Memory Efficiency
  • Liquid AI (MIT spinoff): Builds Liquid Foundation Models (LFM / LFM2 series)—hybrid architectures combining gated short convolutions with grouped-query attention (GQA) blocks, plus MoE variants. These deliver substantially lower memory footprints than Transformers (especially for long contexts, avoiding massive KV cache growth), faster prefill/decode (up to 2× on CPU in some cases), and strong on-device performance. Examples include tiny models (230M–350M params, often
  • Cartesia: Specializes in state-space models (SSMs) and Mamba-derived/hybrid architectures. These offer near-linear (or better) scaling with sequence length and near-constant memory complexity during inference (fixed or minimal growth vs. quadratic KV cache in Transformers). This enables efficient long-context processing on smaller devices with lower energy use. They provide on-device libraries (e.g., “Edge”) and models like hybrid Mamba-2 variants optimized for throughput and fixed memory footprints.
On-Device/Edge Runtimes & Optimizations
  • Cactus (Y Combinator-backed): Builds cross-platform, energy-efficient inference runtimes and kernels for mobile, wearables, and low-power devices. Supports aggressive quantization (down to 2-bit), delivers very low latency (sub-50 ms time-to-first-token), and keeps models small (e.g., hundreds of MB for small models). Emphasizes privacy (fully local) and broad model support (Llama, Qwen, Gemma, etc.).
  • Embedl: Focuses on edge-optimized multimodal models and mixed-precision quantization (e.g., W4A16-Edge2 with selective FP16 layers + “FlashHead” optimizations). Enables fast multimodal reasoning on devices with under 8 GB RAM (e.g., Jetson Orin Nano/AGX), with significant speedups over baselines while recovering most accuracy.
Hardware Innovations Reducing Memory Pressure
  • d-Matrix: Develops digital in-memory computing (DIMC / 3DIMC) chips and platforms (e.g., Corsair) that integrate compute directly with/near memory (initially SRAM, now advancing to 3D DRAM). This drastically cuts data movement—the dominant memory bandwidth and energy bottleneck in inference. Claims include major speedups (up to 10× in some workloads) and better efficiency vs. traditional GPU/HBM setups, enabling higher performance with more manageable overall memory system requirements.
  • Hailo: Designs efficient edge AI inference chips (e.g., Hailo-10H series) optimized for low-power LLM and multimodal workloads. Their chips target 1–3B parameter models at very low power envelopes (~2–2.5 W), making deployment feasible on memory- and power-constrained edge devices where traditional hardware would struggle.
Other Notable Mentions
  • Tiiny AI: Uses neuron-level sparse activation (“TurboSparse”) + heterogeneous engines for running very large models (up to 120B) fully on-device/offline in compact hardware.
  • Broader ecosystem players (inference platforms like Together AI or Fireworks) heavily optimize serving (quantization, caching, KV cache management) but are more infrastructure-focused than core technology innovators in compression/architecture.
Key Trends and Context

Quantization (especially post-training or quantization-aware) remains the most immediate lever and is widely adopted. Architectural shifts (SSMs, hybrids, MoE with low active parameters, convolutions) address fundamental scaling issues like KV cache memory growth. Hardware approaches (in-memory compute, high on-chip SRAM, efficient NPUs) tackle bandwidth and data movement. Many solutions combine these (e.g., architecture + quantization + sparsity).

Progress is rapid, driven by on-device AI demands (Apple’s interest in PrismML is notable) and the economics of scaling inference. Many models are open-sourced or have open weights, accelerating adoption. Results vary by workload—extreme 1-bit models excel in certain scenarios but may need custom kernels; SSM/hybrid models shine for long context or linear scaling needs.

For the absolute latest benchmarks or specific model deployments, check company sites (e.g., PrismML’s Bonsai releases, Liquid AI’s LFM models on Hugging Face) or recent evaluations, as the field evolves quickly.

Interesting!

Categories
AI Business

The Reverse Information Paradox We’ve Always Had

Satya Nadella wrote recently about what he calls the Reverse Information Paradox: enterprises pay for AI intelligence twice. Once in money. Again in the proprietary knowledge they surrender through every prompt, correction, and evaluation. The better they use the model, the more of their own institutional understanding leaks into someone else’s system. The vendor ends up knowing more about the buyer’s business than the buyer knows about what the vendor retained.

Replace “model” with “employee” (or “consultant”) and the paradox is not new at all.

You pay for a person once with salary. You pay again with something harder to price: the context, relationships, and judgment they must absorb to become useful to you. The better they perform, the deeper the immersion, the more of your particular way of doing things moves into their head. Every correction and late-night conversation is another trace of institutional memory changing hands. When they leave, some of that memory leaves with them. Not always through theft. Usually just through the ordinary residue of good work.

The visible cost is salary; the invisible cost is the slow transfer of what makes you distinctive. High performers get more access precisely because they’re high performers, which means the leakage accelerates exactly when you can least afford it. The exhaust is just harder to see with people than with tokens — it moves through conversation and mental models instead of logs.

The analogy has a limit, and the limit matters. Employees bring knowledge in, not just absorb it. They have judgment and relationships a model doesn’t. Models are purely absorptive, and once something is inside them, it’s infinitely reproducible — a person can only be in one place, working for one employer, at a time. We’ve had a few hundred years to build tools for the human version of this problem: contracts, culture, non-competes. The model equivalent is still being invented in real time, which is exactly why Nadella felt the need to name it.

Apple’s recent legal action against former employees who joined OpenAI is this pattern in its sharpest form. Whatever the specifics, the shape is familiar: people who spent years inside one of the most sophisticated organizations in the world, carrying out knowledge that never appeared on any balance sheet and was hard to contain. No one fully anticipates what a mind absorbs simply by being in the room long enough.

That’s the real difference between the silicon case and the human one. You can try to take action to wall off knowledge flowing to a model. You cannot wall off what someone has learned to notice.