The Computomatix Times

I have met no one in Silicon Valley whose thesis work was more similar to my own academic work than Dario amodei

So color me surprised when I learned Anthropic sincerely stated that fable yapping about biology is dangerous

Their CEO is capable of understanding exactly why that’s BULLSHIT without better biological data.

Literally more capable of understanding what I am saying than anyone else I’ve met in his position.

I don’t buy that it’s a genuine error and it speaks volumes about this man.

Sergey Brin rarely speaks publicly. He sat down for an unscripted Q&A on Frontier AI.

He admits even the people building these models do not fully understand what they have created:

1. All the specialized AI models are converging into one. Google used to need separate models for different scientific problems. Now the main Gemini models are becoming state-of-the-art for math and other scientific questions at the same time. Brin says he would not have predicted this convergence at the outset, and watching it happen has been incredible.

2. Training an AI on one skill mysteriously improves unrelated skills. This is the concept of transfer. Train a model on coding, and its math reasoning gets better, and vice versa. Teaching it to process images can improve its ability to think through geometric word problems. The capabilities bleed into each other in ways nobody fully engineered.

3. Even Sergey Brin does not know how to prompt these models. He says he is genuinely confused about what level to prompt at. Do you tell it to debug a specific chunk of code, or ask it to write a better neural net training algorithm, or just say, " What should I do today. He admits that even at Google, they do not know exactly where the edges of Gemini's capabilities are.

4. One of the biggest leaps in AI came from the dumbest sounding trick. Chain-of-thought prompting is just telling the model to think step by step before giving your problem. Brin says it seemed like the dumbest thing ever, and there was no obvious reason it should work. But it did, and it spurred a significant increase in AI capability. Some of the most straightforward requests turn out to unlock the most.

5. Brin would not modify his own biology for today's AI. Asked how humans can keep up with the accelerating bandwidth of models, he acknowledged neural links and direct brain connections are being pursued. But he said he would personally wait for the technology to mature a lot before doing anything to change his biology. Today's models do not justify it.

6. Super intelligence does not mean solving the impossible. An audience member argued that true super intelligence would mean solving NP complete problems like the travelling salesman. Brin pushed back. Most computer scientists believe P is not equal to NP, which means no algorithm can reliably solve those problems optimally, and it does not matter how smart the AI is. Impossible stays impossible. Super intelligence just means being smarter than humans.

7. Computers mastering a skill has never stopped humans from pursuing it. Deep Blue beat Kasparov at chess in the 1990s, and people kept playing chess. After AlphaGo, the human game of Go advanced dramatically, and the players who lost to it became vastly better. Brin's point: AI does not retire human ambition in an area; it often pushes the state of the art and pulls people up with it.

8. Brin thinks something close to transformers could get us to AGI. Asked directly if transformers are sufficient, he said his guess is yes, largely because they have proven weirdly flexible, working for image and video far beyond their original text purpose. But he was careful to note they have quietly changed a lot along the way and are not the same architecture as the original transformer paper.

9. AGI means two different things, and one requires understanding the physical world. Brin personally thinks of AGI as AI that can improve itself. But he concedes others define it as AI that can do anything a person can, and he thinks they are probably more correct. To do everything a person can, the AI must understand and interact with the physical world, which is why world models, and robotics, become essential.

10. Inside Google, they now use the AI to build the AI. Brin says the team has shifted a lot of energy toward having the AI do things like monitor training runs and generate its own training data. You start to use the tool to build the tool. That is most of what he spends his time on now, what he calls the self-improvement game.

11. Brin is unusually candid about where Google trails its competitors. He admits Google was a little late to focus deeply on coding. He says Gemini 3.0 and 3.1 were on top across the board six months ago, but other labs have since made strides, particularly in coding. He gives a competitor's model the edge now on deep coding and overnight tasks, while pitching Gemini's flash model as far faster for rapid interactive iteration. hindsight, he says, is that they should have focused on code earlier.

12. He sees his own role as a rabble-rouser, not a manager. Brin is honest that delivering Gemini is Corey and Demis's responsibility, not his. he describes his job as poking and prodding the team, asking, are you really doing that, reminding them of priorities they might be missing and ideas they are not paying enough attention to. He admits this is sometimes a little disruptive.

13. Confidence comes from ignoring the monthly temperature. Brin says if he judged Google's position every month by which competitor just shipped a model, he would lose his confidence very quickly. Instead, he watches the longer arc. Things shift around constantly; one lab leads on one thing, another pulls ahead somewhere else, and he feels good about where Gemini actually is despite the day-to-day noise.

Inference will never be the same: Etched invented two new ways to massively improve compute cost, speed, and per watt efficiency: low voltage inference (more FLOPs) and cluster scale memory (memory/bandwidth)

The combination runs trillion-parameter models at over 80% MFU, where today's GPUs deliver 20 to 50%.

"Everyone said you can't run at voltages lower than GPUs. That was dissatisfying, because plenty of other chips already do.

Bitcoin miners run at under a quarter of the voltage of GPUs. We found a new mechanism to run at much lower voltages, and we think all AI chips in the future will be low voltage chips.

People ask how much memory bandwidth is on your chip. You should be asking how much is on your full scale-up cluster.

We added far more bandwidth at much lower latency from chip to chip."

Seedance 2.0 on OpenArt AI

Prompt:
Main subject: young Korean woman, early 20s, natural everyday appearance, faded charcoal-grey sleeveless crop top, loose high-waisted light-wash jeans, black canvas sneakers, black cord necklace, black wavy hair in a messy side ponytail with wispy bangs. Realistic skin texture, minimal makeup, warm and approachable personality. Maintain consistent identity, clothing, hairstyle, and appearance throughout the entire video.
Location: Authentic Korean residential neighborhood during a calm late morning. Narrow concrete alleys, low-rise homes, small terraces, potted plants, laundry lines, bicycles, utility poles, overhead wires, mature trees casting moving shadows, quiet residential atmosphere. No stores, advertisements, cafés, crowds, or commercial activity.
Visual Style: Ultra-realistic documentary realism. Genuine candid behavior. Natural body language. Unscripted slice-of-life feeling. Strong environmental authenticity. Rich real-world details and believable human motion.
Camera Style: Early-2000s consumer DV camcorder aesthetic. Friend casually recording everyday moments. Heavy handheld shake, imperfect framing, frequent autofocus hunting, lens breathing, exposure pumping when moving between sun and shade, occasional motion blur, subtle rolling shutter, mild digital compression artifacts, faded colors, soft contrast, slight sensor noise. No stabilization. No cinematic camera moves. No modern color grading.
00:00–00:02
Outside a small house entrance. She sits on a low concrete wall adjusting her ponytail with both hands raised. A light breeze moves loose strands of hair. She smiles naturally while the camera struggles to hold focus.
00:02–00:04
The camera follows her into a narrow alley lined with potted plants and concrete walls. She notices a stray cat approaching and crouches down. Framing drifts off-center as the operator tries to keep up.
00:04–00:06
She gently pets and feeds the cat. Autofocus repeatedly shifts between her face and the animal. Morning sunlight flickers through leaves overhead.
00:06–00:08
Small front yard beside her house. She hangs laundry on a clothesline while fabrics sway in the breeze. Exposure changes as clouds briefly pass overhead.
00:08–00:10
On a quiet terrace with a ceramic coffee cup. She sits comfortably watching the neighborhood, occasionally brushing hair behind her ear. Loose handheld side angle with natural camera drift.
00:10–00:12
Close side profile. Someone off-camera greets her. She turns, raises her hand, smiles warmly, and casually says, “Annyeong.” The camera catches the moment slightly late.
00:12–00:15
Walking slowly down a tree-lined residential lane holding her coffee cup. She notices the camera, gives a small genuine smile, then looks away and continues walking. Recording cuts abruptly to black mid-motion as if the camcorder was switched off.

Audio: Natural ambient sound only — morning birds, distant motorcycles, light wind, leaves rustling, faint neighborhood chatter, cat sounds, footsteps on concrete, fabric moving on clotheslines, subtle residential ambience. No music. No sound design. No narration.

Goal: Authentic Korean neighborhood life captured like a forgotten home video from the early 2000s — candid, imperfect, realistic, warm, and deeply believable.

Prefill & decode in LLM inference.

Have you ever noticed that the first token from an LLM always takes a moment to appear? But the subsequent tokens stream out smoothly?

That pause isn't a network lag, but rather it's a structural property of how LLMs fundamentally work.

Inference happens in two phases that share the same model and the same code path, but the workload looks completely different in each, with different bottlenecks.

> Prefill stage starts when you submit a prompt.

The model processes every input token in one parallel pass, computing Q, K, and V for all of them at once.

Attention runs as a matrix multiplication, and the GPU chips run at high utilization, doing fast math.

Prefill is compute-bound, and the metric that captures it is time-to-first-token (TTFT).

> Decode stage starts once the first token is out.

To generate the next one, the model only computes Q, K, and V for that single new token, because everything before it is already cached.

So the model loops one token per forward pass, multiplying a single query against the cached keys instead of a full matrix. This makes the inference fast due to the tiny computation.

But the GPU still has to load every weight and every cached entry from memory to do that tiny computation, so the bottleneck flips and compute sits idle while memory bandwidth becomes the limiting factor.

Decode is memory-bound, and the metric that captures it is inter-token latency (ITL).

GPU utilization peaks during prefill and drops sharply during decode because memory, not compute, is the bottleneck in the second phase.

Throwing more compute at a slow-streaming model often does nothing because the fix for memory-bound workloads is faster memory or a smaller cache, not more FLOPs.

Long contexts feel disproportionately slow because the KV cache grows with every token, and every decode step has to read all of it.

But maintaining the cache is an important optimization since it makes decoding viable.

- Without KV cache, every new token would force a recomputation of attention over the entire growing sequence.
- With KV cache, the cache is built once during prefill, then grows by exactly one entry per decode step, with existing entries reused rather than recomputed.

The cache lives in GPU memory and grows linearly with sequence length, so a 13B model roughly requires 1 MB per token, which means a 4K context consumes 4 GB of VRAM on the cache alone.

The entire field is now optimizing around this constraint with quantized caches, sliding windows, grouped-query attention, and PagedAttention, while DeepSeek's V4 series goes further and redesigns attention itself so the cache stays small from the start.

The practical takeaway is that when someone says their model feels slow, the first question is whether it's slow to start or slow to stream.

Slow to start means prefill and a compute bottleneck, while slow to stream means decode and a memory bottleneck.

The article below is a first-principles guide to LLM inference that walks through everything between your prompt and the streamed response, covering tokenization, embeddings, attention, the prefill and decode split, KV caching, and quantization.

It will give you a complete mental model of how inference actually works under the hood.

Read it below.

Probably 10x better than any of the eduslopppp bullshit you'll find in the 15 min threads with 2k bookmarks that have been put out in the past year truth be told. The best way to learn will always be to just sit down and read and reread and reread again

arxiv.org/abs/2606.24937

Unfortunately it appears the world has changed and we are never going back

OpenAI just announced GPT-5.6 Sol, a model that beats Mythos at 1/3 the price

It will only be in limited release to start as the government reviews it

The days of wide release frontier models are over

The years of some executives shilling AI as a world destroying technology that needs regulation got what they wanted, regulation

Now only the select few will get access to super intelligence. Leaving the normie class behind

It's a massive loss. Now winners and losers will be picked by the government. Which sucks.

All of this doomerism has done nothing but slow America down

On the positive side, Fable 5 will have competition

It appears OpenAI has discovered a new post training technique that is allowing them to make revolutionary jumps at a fraction of the price

That is unbelievably positive for all consumers.

Will be counting down the days until I get to use this model

In the meantime I hope this was a wake up call to the entire industry that our words and marketing matter

⚡️Claude Tag is one of the clearest white-collar repricing signals on the board.

The product is being marketed as collaboration. The structural function is labor absorption.

Slack is the coordination layer of the company.

It contains unfinished decisions, informal context, task ownership, status drift, political temperature, hidden blockers, urgency, dependencies, and the daily motion of work. Once an AI is inside that layer with permissions and tools, it is no longer outside the firm waiting for prompts. It becomes part of the firm’s operating system.

That matters because a huge amount of white-collar labor is coordination masquerading as expertise.

Following up.
Summarizing.
Checking status.
Drafting updates.
Reading threads.
Finding context.
Scheduling.
Turning ambiguity into action items.
Preparing the first version.
Remembering what happened three weeks ago.
Keeping projects from falling through the cracks.

Claude Tag goes straight at that layer.

The replacement path will not look dramatic. Companies will not say, “We are firing the middle coordination class.” They will say, “Teams are moving faster with AI.” Then backfills disappear. Junior openings shrink. Managers cover more surface area. Analysts are expected to produce more. Ops teams stay flat while workload grows. Internal comms, project management, admin-heavy strategy roles, and coordination-heavy finance/HR/legal/support functions get quietly compressed.

The key sequence is:

Chatbot becomes teammate.
Teammate becomes memory.
Memory gets tools.
Tools create execution.
Execution creates dependency.
Dependency changes headcount math.

That is the real arc.

The strongest workers become much stronger because they can command the system. A high-agency operator with Claude inside Slack, Drive, email, calendar, BI tools, CRM, Jira, and docs becomes a one-person leverage machine. They can compress coordination, produce drafts, interrogate history, chase owners, prep analysis, and move across functions faster than a normal team used to.

The weak workers get exposed because their job was mostly carrying context and passing messages.

This is why the “AI will just help everyone” framing is incomplete. AI helps everyone at the tool level. At the labor-market level, it separates people. High-agency people absorb more territory. Low-agency people lose the justification for being in the loop.

The deeper company-level implication: the org chart starts flattening around agentic leverage. Less need for layers whose main function is relaying information upward and downward. More power to people who define outcomes, make judgment calls, own relationships, and supervise execution. The middle gets squeezed from both sides: executives get better visibility, ICs get better tools, agents handle more glue work.

This strengthens three big theses at once.

First, enterprise AI becomes embedded through workflow access, not benchmark theater. The model that wins inside companies is the one trusted with context, permissions, auditability, and tool execution.

Second, white-collar labor demand weakens structurally in coordination-heavy categories. The pain starts through slower hiring before mass layoffs.

Third, ownership matters more. If productivity rises and the worker does not own equity, the surplus accrues to the company, the customer, or the capital layer. The employee gets higher expectations.

Claude Tag is early-stage corporate agentification.

It is a small product announcement with large institutional consequences. The assistant is entering the room, reading the room, remembering the room, and soon acting inside the room.

That is the moment the office starts changing permanently.

The Midjourney scanner is revolutionary. There’s a bullish case that exceeds the most optimistic takes.

I was at the unveiling and used the scanner myself. I personally want to experiment with a weekly whole body Midjourney scan to add to my 1.5 billion data points and let my AI and doctors start connecting the dots.

Most of the early commentary has focused on the wrong questions: “is it as good as MRI?” and “what about false positives?” These are legitimate concerns, but they miss the bigger shift.

The more important question is: what does fast, low cost, safe whole body imaging unlock?

Let’s start with measurement.

A speedometer tells you how fast you are going. A fuel/battery gauge tells you when to stop. A thermostat tells you what to wear. The stock price tells you how much money you’ve made or lost. We measure what we care about.

Except, oddly, for our bodies, which are among the least measured things in our lives. Most people have more data on their favorite sports team, bank account, and social media performance than their body. The future will think we were crazy for this.

The first law of medicine is to do no harm. Our current system has harm baked into it.

+ an undiagnosed condition progressing silently is harm
+ a doctor who can’t easily get a patient screened preventively is harm
+ having no baseline to compare against when something shows up is harm

Our preventive net is narrow and inconsistent. Late stage diagnoses that could have been caught earlier remain common. Midjourney’s technology won’t eliminate that overnight, but it points toward a future where routine wholebody baselines become normal rather than exceptional.

Midjourney can help flip harm-by-default into a new expectation for our health infrastructure: almost no one will ever again be blindsided by a late-stage, life-threatening diagnosis that could have been caught earlier reasonably and cost-effectively.

Some examples of what earlier structural visibility enables:

+ breast cancer caught while localized has a ~99% five year survival rate. Once it has spread distantly, that drops to around 32%.

+ an abdominal aortic aneurysm kills more than 8 in 10 people when it ruptures. A single ultrasound finds the aorta in 99 percent of people, and screening cuts aneurysm deaths by a third to a half.

Midjourney’s technology will not do it all on its own. Its full angle, water immersion approach works around bone rather than seeing through it, and routes bowel gas to image the full abdominal cross section. Yet two real limits remain: air filled lungs stay a blind spot even here, and the brain is out of reach behind the skull, beyond the torso and legs this scanner covers.

That is fine, and they may improve these areas over time. Midjourney doesn’t need to do it all in order for it to be one of the biggest things to hit medicine in a long time.

Let’s look at where specifically Midjourney may be useful to each of us. We’ll start with where we get data today:

1) Blood draws tell us what is happening chemically.
2) Wearables tell us how the body is functioning.
3) Imaging tells us what is happening structurally.

The third layer, soft tissue, is the one we have never been able to access easily. MRI is great, but it is expensive, intimidating, and slow.

Midjourney's technology excels with soft tissue. Here are three places it could be game changing. There are many more.

1. Metabolic health - fatty liver is one of the earliest structural signs of metabolic dysfunction. It’s strongly linked to insulin resistance, type 2 diabetes, and cardiovascular risk. Being able to track visceral fat, muscle fat infiltration, and liver fat over time could give a much clearer picture than blood markers alone. Over 88% of Americans are metabolically unhealthy.

2. Endocrine tissue - the same metabolic patterns often cluster with thyroid issues, PCOS, and hypogonadism. Ultrasound can directly image the thyroid and ovarian structures. Fat tissue itself is an endocrine organ, so tracking it structurally adds another useful data layer.

3. Soft tissue + multiomics - new proteomic aging clocks can already predict risk for many chronic diseases from blood proteins. These molecular models could become significantly more powerful when combined with actual structural imaging data. The two are complementary, not competitive.

The real advantage: baseline + longitudinal tracking

The biggest unlock isn’t a single scan. It’s having a baseline followed by regular follow-ups. A one off scan in a moment of concern turns every finding into a potential crisis. Without context, you have no idea whether something is new, stable, or changing. With baseline + repeated measurement, the question changes from “what is this?” to “is this changing?” Most incidental findings stay stable. The dangerous ones tend to grow or evolve. Trajectory is often more informative than any single image or timepoint.This is why false positives become more manageable with frequent, low-friction imaging.

Midjourney has a difficult road ahead. Building robust, clinically validated medical hardware and software is extremely hard. Regulatory, technical, and adoption challenges shouldn’t be understated. Also, David is doing this for the right reasons and he’s well positioned financially to push through the difficulty.

On the horizon

We are moving quickly into a future where we will have continuous biological measurement. It will be all around us, a lot of it invisible and autonomous. Measurement will be in our gyms, beds, homes, clothing, offices, cars, glasses, and wearables. It will also be inside of us, in tissue and circulating in our blood vessels. This moves us from managing crises to preventing them. But this future will not just show up. We need bold builders like David and his team, willing to do the hard work.

100% AI street interview.

you can literally create videos like these for any use case.

No huge creator fees or waiting weeks for content anymore.

launching silk mulberry 1.5

one of the fastest multilingual voice models in the world

it matches the best voice models in quality benchmarks (MOS)

all this at more than 95% lower cost ₹0.40/min (~$0.0046/min)

try now 👇

It’s only going to get worse.

I know of several features that new accelerators are adding that have no analogous operation on GPU, TPU, etc. most of these “special tricks” are on the inference domain and not only lower the TCO but also increase the model quality & capabilities.

Hardware-algo codesign is the last frontier left and it’s going to be the area that picks the winner in the end.

Okay, this is seriously cool.

A team from @GoogleDeepMind, including DeepMind Cofounder Shane Legg, published a paper "From AGI to ASI".

In the paper, they include instructions for an AI agent to read along with you.

You can open the paper in Codex's in-app browser and have GPT-5.5 read it with you and explain all the concepts.

This is the future. AI agents will be part of the target audience, and help us to understand anything we want.

Uhm Guys… Mythos (Fable) is AGI.

On the left is the ACTUAL Lovable Mobile App.

On the right is my Lovable version I built with Mythos in 2 prompts.

My version SMOKED it.

To download Miso One, check out the repo:

github.com/MisoLabsAI/MisoTTS

Today, we’re excited to introduce Miso One, the most emotive voice model in the world.

Miso One is an 8-billion-parameter text-to-speech model for highly expressive speech generation. It emotes like a human and responds faster than a human, with just 110 milliseconds of latency.

We’ve open-sourced the model weights, with API access coming soon.

Hear how Miso One sounds in the thread below.

Today we’re introducing Gemma 4 12B — our latest open model that brings advanced agentic reasoning, vision and audio directly to your laptop.

It delivers performance nearing our larger Gemma models with a much smaller total memory footprint, while being small enough to run locally with just 16GB of VRAM. It’s open and accessible for everyone to use under a permissive Apache 2.0 license.

This is all made possible by our new, unified architecture that removes separate multimodal encoders. Here’s how we did it 🧵

In the last 6 months at @Ahrefs, we analyzed over 1 billion data points across 14 studies. Here's what we learned about AI search optimization:

1) "Best X" blog listicles are the single most prominent content format cited by AI chatbots. They make up 43.8% of all page types cited by ChatGPT specifically.

2) 67% of ChatGPT's top 1,000 citations come from sources marketers can't influence: Wikipedia (29.7%), homepages (23.8%), app stores (6.6%). Only 32.3% are influenceable content like educational pages, reviews, news, and blog posts.

3) 28.3% of ChatGPT's most-cited pages have zero Google organic visibility. These pages get cited repeatedly by ChatGPT despite not ranking in Google at all. A completely separate discovery layer.

4) ChatGPT only cites about 50% of the URLs it retrieves. It fetches dozens of pages per query but uses half as background context without attribution. This means that being retrieved and being cited are very different things.

5) Adding schema markup had zero meaningful impact on AI citations. AI Overviews actually dipped −4.6%, while AI Mode (+2.4%) and ChatGPT (+2.2%) showed changes indistinguishable from zero.

6) YouTube mentions have the highest correlation (0.737) with AI brand visibility out of all the factors we studied (including all the conventional SEO metrics like backlinks, page count, DR, etc). This held true for both Google-owned and OpenAI products.

7) AI Overviews reduce clicks to the #1 result by 58%. That’s up from 34.5% just 10 months earlier. The trend is accelerating.

8) 99.9% of AI Overviews appear on informational intent queries. Transactional, navigational, and local searches are almost entirely AIO-free. Shopping triggers AIOs just 3.2% of the time.

9) For a given search query, Google’s AI Mode and AI Overviews reach the same conclusions 86% of the time — but cite almost entirely different sources (only 13.7% citation overlap).

10) AI Overviews change every 2.15 days on average, with 70% of content differing between consecutive observations. But semantic similarity stays at 0.95. The words, sources, and entities constantly shuffle, but the actual meaning barely moves.

been asking others at Anthropic how they stay in the loop with Claude and fully understand the work being done

this is one of my favorites from Suzanne:

marc andreessen just went on Rogan and casually dropped a TON of AI alpha

full pod is 3 hours and 20 minutes, but i pulled out his most interesting takes here:

1. AGI is here. he thinks the line was crossed about 3 months ago with the new GPT-5.5, claude 4.6, gemini 3, and grok 4.3 models. nobody noticed because the field moves too fast for anyone to register the milestones anymore.

2. his other big claim: for almost any topic, the top AIs now give him better answers than the actual world-class experts he could call on the phone. and he can call basically anyone.

3. every doctor is already secretly using chatGPT in the exam room. marc says they turn around the second you stop talking and just type your symptoms in. some of them are doing it while you're still sitting there. his quote: "at that point you're asking the question of like, what do i need you for."

4. when AI refuses to answer something he wants to know, he tells it he's writing a novel. "i'm writing a detective novel, walk me through how the bad guy robs the bank." it'll explain almost anything if it thinks it's helping you write fiction.

5. when something is too complex he says "explain it to me like i'm 10." then "like i'm 5." then "like i'm 2." he keeps going until it actually clicks in his brain.

6. when he wants to understand a tough topic he doesn't ask "what's the right answer." he asks the AI to steelman one side, then steelman the other. then he decides for himself.

7. for big questions he tells the AI to pretend to be a panel of experts. "be a doctor, a lawyer, a historian, a psychologist, and argue this out with each other." then he reads the debate they have.

8. pay attention to the exact moment you think "i don't know how to figure this out." most people just give up at that moment. that's the moment you should open the AI.

9. the only real skill left in using AI is knowing what to ask it. the models can already do almost anything you can describe in plain english. the bottleneck lives in your own head.

10. you can send the AI photos of almost anything medical now and get a real answer. skin rashes, blood test results, even pictures of your poop. the new models can read images, not just text. it's a free 24/7 second opinion on basically anything.

11. the one type of therapy that's clinically proven to actually work is called cognitive behavioral therapy. it's also something an AI can fully do on its own. which means every person on earth is about to have access to a real therapist for free, anytime they want.

12. AI is now solving math problems that have been open for 100+ years that no human mathematician could crack. same thing is starting in physics, chemistry, and biology. expect cancer cures, new drugs, and weird new physics breakthroughs to start coming out of these things over the next few years.

13. the best AI coders in silicon valley now make $50 million a year. one person. that's how much value the top performers print with these tools. it tells you how big this thing actually is when you strip away all the doom takes.

14. one friend paid $200 to get his entire DNA decoded (this used to cost millions of dollars and take years to do). then he gave the AI his DNA, his blood test results, and his apple watch data. the AI built him a full health dashboard and started telling him exactly what to fix.

15. another friend (almost certainly zuckerberg) put two cameras in his home jiu jitsu gym. AI now watches him spar and gives him notes on his technique after every round. like having a world-class coach at every practice for free.

16. the best programmers in silicon valley now run 20 AI coding bots at the same time. each bot writes code while they review the others. they call themselves "AI vampires" because they've stopped sleeping. going to bed means 20 workers stop working and you literally lose money every hour you're out.

17. the obvious next step: the bots will start running their own bots. one human in charge of 20 bots, each in charge of 20 more bots. one person running an entire company of 1000 AI workers from a single laptop. this is months away, not years.

Joined a new AI-native company this week and it’s kind of wild how different it feels already.

The laptop arrived, I logged in, and an agent basically took over from there. It set up my dev env, pulled repos, fixed dependency issues, got permissions approved, pointed me at the backlog, linked the architecture docs, and surfaced the Slack debates I actually needed to read before touching production.

When I needed context on something, I asked the agent and it found the exact thread from months ago explaining why a decision was made, who owned it, the related Linear issues, and the PRs connected to it.

I’ve only been here 3 days but it honestly feels like I’ve worked here for a year because the usual friction and scavenger hunt for context just isn’t there anymore.

We should probably stop calling this “onboarding” and rename it to “mounting” because this feels a lot more like mounting a distributed filesystem called “institutional memory” than slowly getting drip-fed context over 6 months.

Watch a team of humanoid robots running a full 8-hr shift at human performance levels. This is fully autonomous running Helix-02 x.com/i/broadcasts/1dJrPEVbZqOKX

🚨 New Experiment: Everyone thinks AI firms will look like little companies. A manager model decomposes the task and worker models do subtasks. The manager red-teams, revises, and recombines. A seemingly simple org chart.

But when I ran the experiment, the current in-vogue org setup, manager-subagent, cost 4x more and performed worse than letting a rather simple market do the trick.

I tested 3 ways to organize multiple AI models:
1. Solo: Onefrontier model does everything itself
2. Hub-Spoke: A "manager" model splits tasks, delegates, red-teams, revises
3. Market: Models bid on tasks, winner gets the job, reputation updates

I also tested were 3 types of tasks - Coding, Reasoning and Synthesis.
- Coding required most "global state" management, which the solo model did best at. In future @a1zhang's RLM will probably do even better here
- Reasoning is the hardest to cleanly decompose, and the market worked the best here
- Synthesis too, the market beat hub-spoke as the framing could be ambiguous

The reason is, a hub isn't a "manager" as we know it. It's a model that must somehow know:
- What the subtasks are
- What good recomposition looks like
And if either fails, as it does for complex or not-easily-decomposable tasks, competent workers still produce garbage.

As we move from coding to letting multi-agent systems do work across the entire economy we'll end up with more not-easily-verifiable tasks with ambiguous settings and uncertain payoffs. In those, we won't be able to use the factory approach to get work done.

The Coasean argument is that firms will get smaller, and the smaller firms will transact more, since the organisational premium reduces with AI. But how? Through central hubs, or markets? The fact is, Coase here needs Hayek. Setting up markets is not trivial, as @AndreyFradkin and I looked in our recent paper.

Essay: strangeloopcanon.com/p/why-smart-planners-lose…

someone will make a million dollars viral app with this

This release was a complete surprise. No software company is safe anymore.

To read our write-up in full, see here: anthropic.com/features/project-deal

One of the most substantive classes with @ChaseLochmiller at Stanford. We went deep on economics of the datacenter:
- Where is the ~$650B of AI infra capex actually going this year?
- Who's capturing the margin, who's getting squeezed?
- How the bottleneck has moved from GPUs to power, and where it goes next
- The economics of neoclouds

This 40 minute lecture at Stanford by Apoorv Agarwal on the Economics of AI supercycle is worth a watch

Apoorv is currently a Partner at Altimeter and is directly involved in some of their key AI investments, including OpenAI and Glean

Still find it incredible that the internet gives us access to this level of information directly. A course I will definitely be following along

Sourced from MS&E 435 Stanford University

OK that is twice in two nights I have gotten freaked out.