Rumi Elias Calles

mts @micro1_ai | rl envs

[Rumi Elias Calles @RumiCalles]

deep dive into our in-house qc system, flow qc 🌊

[micro1 @micro1_ai]

We’re sharing more information about flow qc, our system for reviewing the tasks used to train and evaluate frontier models across domains, with multimodal support including audio and video.

A task can ask for the right work and still reward the wrong behavior. Grading rules can reject valid answers, miss real mistakes, or leave room for reward hacking. flow qc red teams tasks, grading rules, and model rollouts to investigate these issues before human review, providing experts with evidence for each finding and a proposed fix.

Our latest article shares how we built this process and what we’ve learned from running it in production.

Read more below.

https://t.co/3levdfJyL4

[Rumi Elias Calles @RumiCalles]

@micro1_ai flow vibes 🌊

[Rumi Elias Calles @RumiCalles]

@Exp_Mark @micro1_ai @liuzhang11 great reading. evals evals evals

[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.

[Rumi Elias Calles @RumiCalles]

awesome challenge for any engs out there

[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.

[William Almeida @will_venas]

$1B ARR. So proud of this team 🚀

[Rumi Elias Calles @RumiCalles]

@aliansarinik 🚀🚀🚀🚀🚀🚀

[Rumi Elias Calles @RumiCalles]

insane growth; truly a unique company. would not be possible without the incredible team!

[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]

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 have two additions to PersonalAgentBench: @hark_labs and @OpenAI's Dots!

Dots has taken the lead, with Hark close behind while Gemini Spark moves to third, followed by Instinct, Grok Bot, and Muse.

We also doubled the number of tasks this week, adding research, image understanding, editing, and everyday communication. The wider set changed the picture. Gemini Spark was strongest on research but weaker on image and editing tasks. Hark and Gemini tied on tasks involving user authority. Dots was the most consistent across task types, although it was weaker when it had to infer personal context. No assistant led every category; the overall ranking rewarded consistent performance across the full set.

This is still an early snapshot, and we’re excited to see how results evolve as we add more agents, expand the tasks, and re-evaluate after product updates.

If you’re using any of these six personal agents, follow along by visiting the link in the original post.

[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.

[Rumi Elias Calles @RumiCalles]

LEGEND

[Bruce Buffer @brucebuffer]

I’m proud to announce I’m joining @micro1_ai as MTS 🗣️🎙️👊

[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

[Rumi Elias Calles @RumiCalles]

@aliansarinik HUGE

[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

[Rumi Elias Calles @RumiCalles]

@kobe0938 NICE

[Rumi Elias Calles @RumiCalles]

own your intelligence == own your evals

[Ali Ansari @aliansarinik]

high chance that fortune 1,000 companies will spend ~$100M+/year on average on their evaluation stack over the long run. this is the main component of an enterprise owning their own intelligence.

includes measuring capabilities they precisely care about, and hill climbing on those measurements. it also includes building verifiers on top of their internal data to extract training signals for post training.

it's not going to be a “software” like budget line item. it's fundamental IP being created where $100m/year+ in spend will, in many cases, be a no brainer.

today, the best fortune 1000s are already spending this and more.

[Ali Ansari @aliansarinik]

in Q3 we had over 50k expert onboardings across our data pipelines.

totally possible that 10%+ of the economy converges toward AI training roles, which will become the highest paying and most enjoyable jobs humans can have.

this is the very early signs of that happening.

[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.