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Join us for a masterclass on how AI is shaping the medical field. We’ll explore today’s agent landscape in healthcare and the biggest opportunities for AI agents in medicine, walking through real case studies along the way. We’ll also dive into how micro1 partners with healthcare organizations to evaluate and strengthen their AI agents, using expert-generated data, structured workflow testing, and continuous monitoring to help systems stay accurate, reliable, and compliant as they move from demo to production. Featuring: - Paola Rodríguez - MD, Eng, MSc. (Director of Medical Research, micro1) - Dylan Cahill - MD (Strategic Project Lead, micro1) This session features our core team to explore how AI is transforming the medical field, from research to real-world application. Join us on 7/16, 10:30am PT: micro1.ai/forum/medical-…

@aliansarinik @SpaceXAI 👀

Grok 4.5 takes #2 spot on our medical Pathology benchmark 🔥 Impressive results on clinical interpretation, seems that @SpaceXAI is getting serious about medical reasoning capabilities.

For years, the conversation around AI has been about humans being replaced. But at micro1 we’re seeing the opposite. As models become more capable, human expertise becomes more valuable, not less. Coding is already proving this, and we’re seeing the same pattern across dozens of other domains where human judgment is critical. Human brilliance is, in fact, needed more than ever.

RL environments are used to train AI models to operate in the real world. To build them, we use data that captures how companies across different industries make decisions, collaborate, and get work done. Through the micro1 Company Data Partnership Program, we're partnering with companies and paying out $100k - 2M+ for anonymized operational data that helps frontier AI models understand real workflows. Know a company that could be a fit? Qualified referrals are eligible for rewards of up to $50,000.

@reductoai View the full report: micro1.ai/benchmark/long…

Today we're publishing LongExtractBench, a benchmark commissioned by @reductoai and independently validated by micro1. We evaluated seven production document extraction systems across the same 225 complex enterprise documents. The benchmark was intentionally difficult: documents averaged 358 pages and contained roughly 88,700 ground-truth fields each. Every system was evaluated using the configuration documented in the benchmark methodology. Key findings: • Reducto Deep Extract was the only system to successfully complete all 225 documents. • Direct frontier LLM baselines achieved substantially lower completion rates on long, complex documents. • In this benchmark, dedicated extraction platforms achieved higher completion rates than the direct frontier LLM baselines. • Recall was the clearest differentiator. Precision remained high across systems, but recall ranged from 33.8% to 99.6%, highlighting which systems consistently captured the information contained in long, complex documents. The full report includes the benchmark methodology, limitations, and reproducibility resources. Check out the report and results in the comments below.

My conversation with @aliansarinik the CEO of @micro1_ai, the human data engine helping AI labs train foundational models and enterprises build better AI agents. We talk about why AI still needs human experts, never stop learning, the talent war for researchers, and why the model is not the product. 01:32 — Building Micro1 in the Center of the AI Boom 06:57 — A Once-in-a-Lifetime AI Opportunity 08:44 — How Micro1 Helps Train Smarter AI Models 10:43 — Why AI Models Never Stop Learning 14:56 — Why AI Still Needs Human Experts 17:49 — The Model Is Not the Product 21:23 — Who Wins and Loses in the AI Era 27:25 — When AI Agents Become Team Members 29:19 — The Talent War for AI Researchers 33:38 — Why Your Company Data Is Training AI 38:35 — From Iran to the American Dream 44:33 — Why Taking a Unique Path Can Win 48:04 — Embrace the Chaos 49:50 — Why AI Will Create New Jobs 53:02 — How Ali Measures His Life #AI #ArtificialIntelligence #FutureOfWork #AIData #AIAgents #MachineLearning #Startups #DivotPodcast

@VictorJuma8k @cartesia jobs.micro1.ai/post/21caf82a-…

@micro1_ai @cartesia I can't see the link

micro1 x @cartesia: The Human Speech Data Project Every language, accent, and speaking style has its own rhythm, texture, and feeling. Building great voice AI means learning from the people who know those details best. That is why micro1 and Cartesia are launching The Human Speech Data Project, an initiative focused on improving the next generation of voice AI through high-quality speech data and human evaluation. The project brings together multilingual speakers, linguists, transcriptionists, native speakers, and voice actors to help: - Capture and evaluate natural conversational speech - Transcribe and annotate audio with a sharp ear for accuracy - Review AI-generated speech for naturalness, correctness, and edge cases - Bring expert judgment into the model improvement process Visit the link in the comments if you’d like to learn more about how you can contribute.

@cartesia jobs.micro1.ai/post/21caf82a-…

We're hosting a forum to discuss micro1's Company Data Partnership Program this Thursday at 9am PT. micro1 Founder & CEO, @aliansarinik , and Strategic AI lead, Soliman Aniss, will share insights on how we're partnering with 50 companies, paying $100K–$2M+, for their real-world business workflows that will help train the next generation of AI models. Join us to learn how the program works, common questions companies have, and what participation looks like in practice. Register here: us06web.zoom.us/webinar/regist…

View the full report: micro1.ai/benchmark/real…

Two AI models can land the same pathology benchmark score for completely different reasons. Today we're introducing micro1's pathology-reasoning benchmark, the latest addition to our Realm Medical-Reasoning benchmark series. Built with practicing pathologists, it spans the range of cases a working service actually sees, from hematopathology and bone marrow workups to breast, thyroid, gastrointestinal, genitourinary, dermatopathology, and biomarker studies. Rather than testing medical knowledge in isolation, it measures something narrower and harder: whether a model can extract report facts exactly, preserve diagnostic limits, and avoid escalating to conclusions the specimen doesn't support. We scored three frontier models across the dataset: Claude Opus 4.8 - 82.6% GPT-5.5 - 76.3% Gemini 3.5 Flash - 75.7% However, the ranking was the least interesting part. The models were similarly strong at extracting facts and running calculations. Where they separated was judgment: knowing where the report ends. The most common way points were lost wasn't getting facts wrong. It was saying more than the report supports, resolving uncertainty the report left open, or naming a stage, biomarker, or treatment the specimen can't establish. We found that two models can land nearly the same score for completely opposite reasons. The aggregate numbers look close, but the trajectories tell a different story, and those differences get more interesting as the cases get harder. Our takeaway: what matters most isn't how often a model is right, but how it reasons when a report leaves room for doubt. Full report linked in the comments.

@ryanjdaniels 🤝

Building RedlineBench with @micro1_ai was fascinating. We asked a bunch of senior lawyers to negotiate against each other. At first they did similar things, but then things got weird. As the deals dragged on, each lawyer went rogue, relying on instinct to get things closed. How can you possibly grade a model against that!? This is where @aliansarinik's team came in -- design an eval framework, review structures, pipeline design. As always, loved discussing this with @TBPN @jordihays & @johncoogan

Today we're excited to introduce the micro1·Crosby Contract Redlining Benchmark. In collaboration with @crosbylegal, we built a benchmark on real contract redlining to identify how frontier AI models perform at contract negotiation. Practicing attorneys negotiated multi-round SaaS agreements, and we tested how models handled the actual back-and-forth of a deal. Results: GPT-5.5: 50.5% Claude Fable 5: 47.3%* Gemini 3.5 Flash: 45.1% Claude Opus 4.8: 44.4% The biggest weakness showed up early in negotiations, meaning models performed much better once there was already context and prior redlines to react to, but struggled more when they had to make the first move. Our takeaway: today’s models are useful in legal workflows and can support live deals, but are not ready to negotiate one. Check out the full report at Crosby Intelligence.

Earn up to $25k per successful company data partnership referral to micro1 Link in comments to get started

@aliansarinik Earn up to $25k per successful referral: micro1.ai/data

Today, we’re committing $5,000,000 to launch the micro1 Company Data Partnerships Referral Program. For every company you refer, you can earn up to $25,000. Simply introduce a company, have them identify you as the referrer during onboarding, and once they enter into a paid data partnership with micro1, you’ll receive your referral payout. If you know a company that wants to turn its operational data into a recurring revenue stream while accelerating its adoption of AI through micro1's Data Partnership Program, we’d love an introduction. visit /data to get started