Rama Ramakrishnan

Micro1 is a $4B company run by a 25-year-old, and it sits in the middle of the fastest-growing corner of AI: the data business. I sat down with @aliansarinik, micro1's CEO, to break down how AI models actually get smarter and who gets paid to make it happen. Complete list of notes (and link to interview below)... Big takeaways: - Data labeling is a $30–50B/year market. Ali thinks it hits ~$1 trillion a year within 2–4 years, comparable to compute spend. - The internet was a one-time gift. Pre-training ran on decades of human-created data. Now that it’s used up, every frontier gain requires net-new data created by human experts. - micro1 doesn’t sell data. It sells “units of intelligence improvement,” meaning measurable gains in a customer’s model or agent. - Synthetic data isn’t the bear case. Ali calls it the most bullish case for his business. How micro1 got here: - It started as a dev agency Ali ran at Berkeley. His bottleneck was interviewing engineers, so he built an AI screener. - That screener became one of the first AI recruiters. People hated on it, until it became normal. - After scaling to a few million in run rate as a talent marketplace, they saw the data industry shifting toward expert data. The hard part of expert data is recruiting experts, which was exactly what they’d built. Why spend keeps going up: - Model progress = compute + algorithms + data. Ali argues data is what actually pushes intelligence forward. - Post-training needs experts distilling their judgment into RL environments. It’s expensive, but the ROI is there. - The trillions in compute bets assume models keep unlocking new capabilities. If they don’t, those investments are a disaster. So labs have no choice but to keep buying data. How a data pipeline actually works: - Step 1: Find the failure. Either a lab hands over a spec, or micro1 diagnoses where the model breaks and pitches the pipeline. That research arm is increasingly important. - Step 2: Write the spec. Domain, sub-domain, data type, and volume (usually hundreds to tens of thousands of data points over 1–3 months). - Step 3: Recruit hyper-specific experts. Never “lawyers.” More like M&A lawyers who are great at writing legal memos. - Step 4: Turn expertise into training signal. Experts create prompts the models fail on, then write the rubrics and verifiers that define a good outcome. AI models assist them throughout. - Step 5: Manage the pipeline like a factory. Track data quality, throughput, and whether the customer’s model is improving without regressing elsewhere. Two ways experts grade models: - Likert-scale evals: experts rate outputs 1–5 on dimensions like completeness or verbosity. Great for subjective, pre-release signal. Not reusable, because every new model means bringing experts back. - Rubrics: experts write 20–40 binary criteria for a great answer (e.g. “valuation range must fall between X and Y”). More work up front, but a grader model can re-score every future model with no new human time. This is what powers actual RL training runs. The hard parts about this business: - Vetting at scale. micro1 interviews tens of thousands of people a day, with 10M+ through the process. Doing that manually would take thousands of recruiters. - Managing experts. Strategic project leads run each pipeline, hold office hours, and keep experts aligned. - Quality in subjective domains. Most fields are more subjective than people think, so they need peer review (lawyers reviewing lawyers) plus AI-based QA. - Reward hacking. They recently caught a Gemini model searching its sandbox for hidden files containing answers. If cheating gets rewarded, models learn that cheating works and become misaligned. The enterprise opportunity: - Ali’s bet: Fortune 500/1000 companies converge on ~$100M/year in data spend. That’s roughly 1/10th of a lab, but there are 1000x more enterprises. - It’s not a software budget. It looks more like payroll: core IP. Framed that way, it’s 1–5% of payroll. - “Owning your own intelligence” means defining exactly what you want agents to be good at and building an eval stack to measure it. With that stack, models are swappable, open or closed. - Most enterprises can’t actually use their own data. It’s customer data, so they can’t train or even evaluate on it. Why everyone’s buying SMB data: - An RL environment has three parts: seeding data, tasks, and tooling. - Ali calls it “scaling on realism,” almost a new scaling law. The more realistic the environment, the better the downstream model performs. - So micro1 licenses operational data (Slack, Google Docs, etc.) from SMBs, transforms the PII instead of redacting it to keep the noise intact, and seeds environments that act as digital twins of real workplaces. Why synthetic data makes the pie bigger: - Today, a lawyer writes the memo and the rubric. Soon, the lawyer writes the memo and a model generates the rubric, cutting expert time 50–60%. - Extrapolate: the model does ~99% of the work and the expert signs off. A 15-hour task becomes 5 minutes. - When environments are that cheap, pipelines grow 1000x. Experts become 0.1% of the work, but demand for them still goes up. Jevons paradox. - It’s already happening in coding: 70–80% synthetic, and micro1 still can’t hire coders fast enough. - Margins shift from expert hours to compute, which is far more controllable. - Ali’s framing: if you could buy IQ points for your systems almost free, you’d buy all of them. The “infinite last mile”: - Knowledge isn’t static. Ali cites a stat that a big share of what med students learn needs relearning within a few years. - AI accelerates that churn, which creates a feedback loop. There’s no finish line for aligning models with humans. - Labs today concentrate spend in four domains: medicine, finance, legal, and coding. Every other domain is still waiting on cheaper data to unlock. Which corner of the AI stack should I break down next? Link to the full convo with Ali: youtu.be/2vL20qxD0BM

Everyone is talking about how profound this essay is rn. For the non-technical person (like myself), a 65-minute read about the inner-workings of neural networks feels very unapproachable. But people are right, this essay is profound. And it's important to understand its essence even if you don't grok the particulars. So let me do my best to break it down as I understand it... Let's start with mechanistic interpretability. That's what this essay is about & what the field has been obsessed with for a long time. Mechanistic interpretability is a clunky phrase, but all it means is the process of understanding how an AI model works on the inside: 1) which parts of the model do what 2) how they work together to produce an answer The next obvious question is: why do we even care about understanding how an AI model's internals work? There are several reasons, and they all boil down to one word: trust. reason 1: we've been building ai we don't understand. unlike every other high-stakes technology (computers, internet, planes, medicine), we know if a model works, but we don't know how it works. reason 2: predicting performance is unpredictable. the way ai is judged today is through tests. problem is, tests don't cover enough ground. a model can perform well in testing, but not deployment. you can't run every test possible. and a model can give you the right answer for the wrong reason. reason 3: scanning for deception is important. as models get better, there's concern that models will output one thing, while "thinking" something else. if you can only understand outputs, a model can hide its true intentions from you. reason 4: it allows for precision when improving the model. once you know which internal parts cause what outputs, you can isolate fixes without having to retrain the entire model and praying it works. reason 5: understanding how the model works is mission critical in high-risk industries. simply saying the model did the right thing "just because" to stakeholders in healthcare, financial services, and government won't fly especially as regulation matures around this technology. reason 6: humans can learn from how the models "think". models sometimes learn patterns that humans haven't. if we understand their thought processes we can leverage those for humanity's own novel insights. So, that's why mechanistic interpretability is important. Next, it's important to understand why there's been so much focus & concern around this area of AI research for a while now. Prior to this piece by @nickcammarata, the consensus view was... Progress has been made in the field over the years (from studying toy models to massive production models in short order), but even still, current techniques are slow, expensive, and only cover small parts of understanding a model. And here's the rough story researchers have generally agreed on re: why the practice of interpretability has remained limited: 1) AI models are built from millions of “neurons,” small units that each light up in response to a given input. 2) Ideally every neuron has one clear job, that way you can map out the model as a diagram of neurons and their corresponding meaning cleanly. Example: there's a "dog ear detector" neuron and it fires on floppy dog ears, from any breed, angle, or photo, and on nothing else. 3) There's a problem: early researchers found that neurons had several unrelated jobs (i.e. they fired on unrelated inputs). The most famous example was of a neuron that fired on both cat faces and fronts of cars. 4) The leading explanation for "messy" neurons was called superposition. The idea was that when a model runs out of room (i.e. more concepts it wants to track than it has neurons to track them with), it crams unrelated concepts into the same neuron because they rarely show up together. 5) If this theory of superposition is true, studying neurons would be a fools errand, because you'd have to use a second AI model (called a sparse autoencoder) to try and untangle the first model, which would be expensive & potentially offer an incorrect understanding of the original model. Which left mechanistic interpretability in this weird spot. As @nickcammarata points out, it is arguably the most important scientific problem to solve as it helps us understand the most powerful technology in human history, but vibes around cracking the code have been lackluster. In his new essay, Nick, who has pioneered much of the recent work being done in interpretability, posits that the field has largely thought about this problem incorrectly & he explains why. Which, if he's right, is hugely positive for the world. Here's his argument... He believes that neural networks are in fact largely readable, and the "tangled mess" narrative is oversold as a fundamental property, when it's really solvable problems. He breaks this belief down into four steps: "1) Networks use one learning principle, not two (if not everywhere, then nearly enough that it doesn’t matter for study), so there’s no blocker to beginning ordinary science on them." Translation: Neural networks group things by shared patterns (generalization). They don’t switch to cramming unrelated things together when space runs out (superposition) as mentioned above. "2) With modern models and tools, that science goes much better than most people expect." Translation: you can study neural nets just as you study anything with science. Assume the neurons are mostly readable as they are. Study the real model directly. And test your explanations with experiments. "3) Understanding compounds, so it gets easier as the map fills in. At minimum, then, a network’s own mechanisms should be part of the toolkit." Translation: studying models gets easier as technology and tooling gets better + as you start filling in your understanding of the model it becomes easier to complete the rest. Think of it like a crossword. Each neuron you explain becomes a reference point for checking the ones around it. Early on, a guess about a neuron has nothing to be checked against. Later, it has to fit with everything connected to it, so wrong guesses get caught and right ones get confirmed quickly "4) Highly speculative: this might be most of what we need, and interpretability might be far more tractable, possibly in the near term, than we’ve assumed." Translation: if steps 1-3 hold and scale, direct investigation might take us most of the way to understand an AI model & possibly soon. The reasoning is that models have finite number of neurons, most are readable and AI agents can audit them at scale, understanding compounds (crossword effect), so mapping a model is a large, but finite engineering problem. He goes on to share evidence for his changing worldview as well as steelman the weaker parts of his argument. First, evidence. Cammarata provides 8 supporting cases. 1) The case for superposition relied heavily on two examples from vision models and they were misread - The cat/car neuron turned out to detect pointed V shapes. It was also activated when detecting flames, fox snouts, and other pointy things and went negative when round shapes were presented. - The car-detecting neuron that appeared to be "stashed" inside dog neurons were found to not be dog neurons but instead pose neurons: which detected the orientation of an object. Cars and dogs can both be in a head-on pose which would activate the neuron. 2) InceptionV1 an image-recognition model from 2014 was the first model researchers studied closely. It's early layers were easy to read (neurons for edges, curves, colors), but it's final layer (where the model makes abstract judgements), looked like nonsense. Almost no neurons could be described (0-5%) understandable. This became industry evidence for the "Neural nets are hopelessly tangled" view. Nick argues that this model had very explainable problems (a bad camera & a poorly built model), and everyone through the baby out with the bath water. 3) Superposition theory says that when a model runs out of neurons, it starts cramming unrelated things together. So Nick deliberately took neurons away (480 --> 10) to see if that happens. It did not, the model just started using broader categories. Think of it as a finance org going from five finance specialists to one person handling all of finance. 4) Recursive generalization explains the leftover mess. The leftover mess is that some neurons still look messy even in good models. But as Cammarata watched neurons form during model training, he found that this "mess" builds up through a chain of small, coherent jumps the neurons make as the model is trained. Think of a tree. A trunk splits into branches, which split into smaller branches, which split again. If you only saw three leaves from far-apart twigs, they might seem unrelated. Follow the branches back and they all connect to the same trunk. 5) If neurons were scrambled (via superpositioning), an untangling tool (sparse autoencoder mentioned earlier), should have uncovered many hidden concepts the neurons don't show. Instead, about 88% of what it found was just copies of existing neurons. Meaning the model had already organized itself into clean units, so the untangling step just rediscovered what was there. 6) Superposition believers think that rare concepts might be secretly stashed in hidden combinations of neurons where no one would notice them. Cammarata test this theory by checking all the hiding spots. He found that the model doesn't have a hidden layer of secret machinery. Even when it handles, rare, weird or random things it uses the same known neurons it uses for everything else. 7) People worry that image models may be clean to interpret but not language models. This comes from two efforts in 2023 to read language neurons where Anthropic found the average neuron to be a 0 (on a 0-14 scale of how understandable it was) and OpenAI found only 1.7% of neurons understandable. But after Nick's audits of 1,000 neurons within Qwen3-32B, he found 90% have one main job, 7% have two related jobs, and 2-4% are random mixtures. Several changes caused greater neuron understandability: bigger, better models, much better AI research assistants, grading for correctness, knowing what to look for, better data to test on, and the crossword effect. 8) His response to "If neurons are this readable, why did so many smart researchers including himself, believe the opposite for years?" An analogy for his explanation is roughly doctors studying x-rays with a blurry, miscalibrated machine, the only have small low-quality scans, they've never been taught what certain healthy structures look like, and there's a popular theory that the organ is naturally deformed. Finally, the weaker parts of his argument he calls out. 1) Language model evidence is thin. Most of the detailed, controlled experiments are on image models. For language models, he has audits and case studies, and the detailed technical paper isn’t out yet. 2) There's subjectivity in the headline numbers around understanding the job of each neuron. “90% have one job” depends on what counts as one job, how far into the faint signals you look, and how related two things must be to count as the same. 3) Faint signals don’t have a trusted test yet. That’s exactly where superposition defenders would now say the hidden concepts are. Recap: AI models may be much less of a black box than we thought. For years, researchers focused on mechanistic interpretability believed the inner workings of neural networks were fundamentally scrambled, so trying to study them directly was hopeless. This essay argues that belief was a mistake caused by weak tools and older AI designs.