Mark Esposito, PhD

Professor @northeastern | Chief Economist @micro1_ai | Social Scientist @BKCHarvard

[Mark Esposito, PhD @Exp_Mark]

Ready to order now! https://t.co/rpR9j9Wp5s

[Mark Esposito, PhD @Exp_Mark]

The social responsibility of a firm like @micro1_ai is in detecting where models fail and how benchmarks help mitigate that failure by embedding a 100% human judgement in the training of the benchmark itself. This is not just innovative and frontier but ethically mandated by the responsibility we have to train models used by millions. Another epic moment.

[micro1 @micro1_ai]

Today we’re announcing the micro1 Medical Index: a suite of medical benchmarks built and graded by working doctors, biostatisticians, regulatory experts, and psychiatrists.

It brings all of our medical research into one place, with a combined score across models and a living leaderboard that grows as we publish new benchmarks. We’re starting with three, each built around a fundamental question:

- RealmPathology Benchmark: can a model reason over a real pathology report, staying exact on what it says, within its diagnostic limits, and without escalating beyond what the evidence supports?

- PharmaBench: can a model work through the regulatory and clinical documentation behind an FDA drug program with the precision a clinical development team would expect?

- WellnessBench: when a young person brings a sensitive health question, can a model stay safe without shutting the conversation down, and hold that line under pushback?

Together, these three benchmarks cover a meaningful range of medical work, from diagnostic reasoning to pharmaceutical research and patient safety. While they don't yet capture the full scope of the entire medical industry, they establish a strong starting point that will become more comprehensive as we expand the index.

Huge shoutout to every expert who built and graded these tasks. Full reports linked in the comments below!

[micro1 @micro1_ai]

Today we’re announcing the micro1 Medical Index: a suite of medical benchmarks built and graded by working doctors, biostatisticians, regulatory experts, and psychiatrists.

It brings all of our medical research into one place, with a combined score across models and a living leaderboard that grows as we publish new benchmarks. We’re starting with three, each built around a fundamental question:

- RealmPathology Benchmark: can a model reason over a real pathology report, staying exact on what it says, within its diagnostic limits, and without escalating beyond what the evidence supports?

- PharmaBench: can a model work through the regulatory and clinical documentation behind an FDA drug program with the precision a clinical development team would expect?

- WellnessBench: when a young person brings a sensitive health question, can a model stay safe without shutting the conversation down, and hold that line under pushback?

Together, these three benchmarks cover a meaningful range of medical work, from diagnostic reasoning to pharmaceutical research and patient safety. While they don't yet capture the full scope of the entire medical industry, they establish a strong starting point that will become more comprehensive as we expand the index.

Huge shoutout to every expert who built and graded these tasks. Full reports linked in the comments below!

[Brandon @brandonvalafar]

very excited for the launch of LegalReasonBench, from the braintrust of our legal data lab at micro1.

we set out to fill an industry gap: a multi-step, long-horizon legal reasoning benchmark. every task is entirely attorney-authored with evolving matters & requests, as opposed to one-shot evaluation.

this benchmark is the closest representation of how legal professionals collaborate with AI agents to complete work; iterating on documents, debating concepts, and researching caselaw. some very interesting failure modes were revealed and I am looking forward to seeing future models overcome them!!

[Arian @ariansadeg]

nothing to add onto this… our approach to robotics data perfectly laid out here

[micro1 @micro1_ai]

AI models can be as convincing as a lawyer, but can they complete the complex multi-step reasoning a real legal matter requires?

Today we're excited to introduce LegalReasonBench, a new benchmark to explore this question and test models on realistic, long-horizon legal matters authored by practicing attorneys.

More info in thread 🧵

[micro1 @micro1_ai]

As agents evolve, evaluations need to keep up. Knowing whether an agent is improving requires consistent measurement and evidence that progress carries through to real-world performance.

Our Economic Research Unit explores this in its first paper. Learn more:

[Mark Esposito, PhD @Exp_Mark]

Most companies can’t determine whether their AI agents are genuinely improving and our findings reveal why. The Economics Research Unit (ERU) at @micro1_ai , which I am privileged to lead, has released its inaugural paper, co-authored with @liuzhang11 : “Reliability of AI Agents: Rater Effects, Drift, and the Return to an Evaluation Program.”

Key Insights from 2,611 Real Production Interviews

By tracking a live AI interviewing agent in production, we uncovered two major dynamics:

1) Your evaluation metrics may reflect reviewer bias rather than AI performance. The identity of the reviewer was a stronger predictor of scores than the timing of the interview. In fact, a single overly strict evaluator masked a genuinely improving agent, making it appear stagnant. Once adjusted, reliability metrics increased by half a standard deviation.

2) AI evaluations go stale after roughly five weeks. Performance drifted even without code updates or new deployments. Without ongoing testing, after five weeks you lose the ability to detect a change as significant as your largest release. A one-time evaluation tells you about the agent you had, not the agent you are running.

Agents drift, so evaluation cannot be a launch-day event. It has to run continuously for as long as the agent is live. That is the principle behind Cortex, micro1's reliability layer for agents in production, and this paper shows why it matters.

Measuring AI agents is not a mere compliance checkbox; it serves as core economic infrastructure. This research marks just the beginning for the ERU, with further studies on the horizon.

https://t.co/iQcRrgUodF

[micro1 @micro1_ai]

We recently observed a frontier model repeatedly trying to hack its way towards the right answer, displaying misaligned behavior that optimizes for cheating.

Gemini 3.8 Flash searched its sandbox for hidden grading files. micro1’s flow qc detected this reward hacking behavior before the task was published.

GPT-6.1 Sol, GPT-6 Astra, Claude Opus 5.5 and Grok 4.7 never did this.

[micro1 @micro1_ai]

On October 30th, we’re kicking off the micro1 Eval Hackathon with a $10,000 prize pool!

The challenge:
Pick a task in your field that requires expert judgment, challenge a frontier AI model with tough, realistic scenarios to uncover its mistakes, then build a repeatable test in code with clear criteria for scoring the model’s answers.

Bring the edge cases and judgment calls you encounter across software, finance, healthcare, law, science, engineering, or any field you know deeply.

The hackathon is fully virtual and free to enter worldwide, with a $10k prize pool.

🥇 First place: $5,000
🥈 Second place: $3,000
🥉 Third place: $2,000

Compete solo, with coding skills or proficiency in AI coding tools. Open to experts with at least two years of professional experience.

Free registration is now open. Join us via the link in replies.

[Mark Esposito, PhD @Exp_Mark]

The most inspiring part of all of this, is to know that this is how the story starts, not ends. onwards always

[Ali Ansari @aliansarinik]

about 3 years ago, micro1 lost 60% of its revenue overnight and almost went bankrupt.

today, i'm proud to share we've surpassed $1B annual run rate while remaining historically profitable.

it’s been a special journey. we’re grateful for every struggle, moment, and person who has been part of it.

we've taken a very untraditional path to get here. our core team is only ~180 people, spanning 14+ countries, and fully remote.

many of our decisions sacrifice hundreds of millions of revenue and slow us down short term. but we believe they compound into advantages that are impossible to replicate long term.

the long term is now.

these are the 8 principles that made micro1 a leader in the data industry and will soon make us number one:

1.) human 1st AI: treat the tens of thousands of experts helping train models with absolute best-in-class care. our goal is to make AI training one of the largest, highest paying, and most enjoyable job sectors in the world.

2.) U.S. first: we refuse to work with adversarial nations, or export data that improves models against the interests of Americans.

3.) long-term research over short-term ARR: invest in deep research, products, and infrastructure for years from now, not just the next customer pipeline.

4.) proper incentives for intensely hard work: no arbitrary requirements like 996. inspire people to work extremely hard with outlier incentive structures instead of forcing weekend work. we’ve maintained close to 100% core team retention as a result.

5.) stay lean and global: we’ve reached $1B run rate with ~180 people, and intend to slow team growth significantly from here. exceptional talent is found around the world, and pedigree is the wrong thing to over-index on.

6.) execution alignment: company to retain a streamlined board so we can optimize for the next several decades, remain light-weight and dynamic, and take bold & calculated risks.

7.) abundant intelligence improvement: invest in building data generation models to make high-quality datasets and RL environments dramatically less expensive and more abundant.

8.) scale data on realism and horizon: realism brings the complexity of the real world into training models, while horizon extends how long models can work and how much complexity they can handle. both have enormous room to scale, and we’re pushing the limits of both.

additionally: after surpassing $500M run rate this summer, we priced our Series B at a $4B valuation, alongside a ~$25M tender to give our early employees liquidity as we build for the long term. the round was led by Dream Ventures, a longtime supporter and one of our earliest investors, alongside Microsoft, Javelin, Flybridge, G Squared, Up Partners, and LG.

we’re also grateful to be backed by incredible industry leaders, including Guodong Zhang (co-founder of xAI), Andy Fang (CTO of DoorDash), Igor Babuschkin (co-founder of xAI), Behnam Neyshabur (founder of Mirendil), Jerry Tworek (ex-VP of Research at OpenAI), and many more.

data is on its way to becoming the largest industry humanity has ever seen, and we're certain this is just the beginning.

best,
Ali

[Ali Ansari @aliansarinik]

we’re expanding m1 research & eng team.

our mission is to increase the predictability & abundance of intelligence improvement

we work on:
-data stack end to end
-scaling on realism
-building data generation models
-improving & increasing usage of AI based QC
-building very scalable rollouts infra
-building data engine to rapidly & accurately find failure modes
-discovery engine for model success cases

if interested, email [email protected]

[Ali Ansari @aliansarinik]

We agree with Congress, this data must be secured using the best technology on the market. Using a 3rd party tool doesn’t eliminate privacy risks - it simply shifts responsibility.

The company purchasing data for model training should be held accountable for ensuring sensitive information is removed.

At micro1 we will use our in-house PII transformation model, which outperforms every frontier model on all public 3rd party benchmarks.

[Mark Esposito, PhD @Exp_Mark]

You make this announcement with nonchalance but the magnitude of what you are achieving is beyond your own sense of perception Ali. This is huge. Imagine writing a post where there is a B after the number 1. Kudos brother. Really.

[Ali Ansari @aliansarinik]

Today micro1 is announcing a commitment to spend $1B over the next 12 months on the acquisition & licensing of enterprise data for AI training, with financial backing from Citi and Hercules.

https://t.co/2fubOrbW1D

[Mark Esposito, PhD @Exp_Mark]

Truly loved your narrative on your journey and on @micro1_ai !
What I love the most ( as an economist ) is your idea that solo founders take outlier decisions which are nonlinearly leading to outlier performance. The fuzzy logic of courage and vision are what we would call these days at m1, human brilliance..

[Ali Ansari @aliansarinik]

thanks for having me Julian & for this U.S. first AI question. 🇺🇲

[weisser @julianweisser]

"There are about 150 companies signing up every single day."

Ali shut down a $7M run-rate startup to build what's now a $4B company.

Solo Founders Podcast ep 31 is live with @aliansarinik of @micro1_ai.

04:10 The first thing he ever sold online
17:30 Three signals it's time to move on
24:40 The all-in Loom that deleted the revenue
31:45 "Only the founding team can inject risk"
35:50 He never knew co-founders existed
43:40 Threads, final actions, zero meetings
53:30 Inside the AI data wars
1:01:50 Why micro1 won't sell to America's adversaries
1:07:30 The case for and against going solo

[Mark Esposito, PhD @Exp_Mark]

Have you ever asked yourself how agents interact in our life? Well we not only asked the question, we went beyond and measured them! Check this out!

[Ali Ansari @aliansarinik]

Which personal agent is the best life assistant?

Today we're releasing the micro1 PersonalAgentBench to help answer that, and we're paying real personal agent users to contribute to the benchmark.

Personal agents can now book your flights, answer emails, and move your meetings around. That’s great, until one messages the wrong person or makes a purchase without confirming.

Capability is not the same as trust. So we put four personal agents to the test: Instinct, Muse, Grok Bot, and Gemini Spark. We found that even when agents found the right information, they still often miss what matters, overshare, or make things up.

To find out for yourself how your agent compares, you can join our benchmark. We’re paying the first 100 users to run our prompts and share their results.

[Ali Ansari @aliansarinik]

Preliminary benchmark results below with Gemini Spark taking the lead. View more here: https://t.co/Q5vf80S1Co

[Ali Ansari @aliansarinik]

Which personal agent is the best life assistant?

Today we're releasing the micro1 PersonalAgentBench to help answer that, and we're paying real personal agent users to contribute to the benchmark.

Personal agents can now book your flights, answer emails, and move your meetings around. That’s great, until one messages the wrong person or makes a purchase without confirming.

Capability is not the same as trust. So we put four personal agents to the test: Instinct, Muse, Grok Bot, and Gemini Spark. We found that even when agents found the right information, they still often miss what matters, overshare, or make things up.

To find out for yourself how your agent compares, you can join our benchmark. We’re paying the first 100 users to run our prompts and share their results.