Tiffany Luck

Partner @NEA investing in enterprise & vertical AI. Previously, @GGVCapital, @MorganStanley, BD/Mktg @amazon @Lot18 @Forbes. Golfer ⛳️. And best of all - mom ✨.

[Tiffany Luck @lucktm]

Such a fun conversation this morning with @EdLudlow on @technology. Thanks for having me, Ed!

We touch on:

1️⃣ intelligence is not enough, deployment is the next frontier
2️⃣ importance of "own your intelligence" in enterprise AI
3️⃣ exciting opportunity ahead with physical AI

[Bloomberg TV @BloombergTV]

“Intelligence is not enough.”

NEA’s Tiffany Luck says even if frontier labs slow the pace of model development, enterprise AI has plenty of room to run, with the next big opportunity turning increasingly powerful models into real-world adoption and ROI https://t.co/W10BXQwkkH

[Tiffany Luck @lucktm]

@jana_iris @exedev @davidcrawshaw So exciting — congrats!!🎉

[Tiffany Luck @lucktm]

@SamRo Best tweet of the day 😂 and just oh so true!

[Tiffany Luck @lucktm]

@jcspinell @stewart @aunder @iamcal @BradleyArmstr10 @collabfund Congrats!! Exciting!

[Tiffany Luck @lucktm]

@EdLudlow This tweet ensures that there will be big news! 😆

[Tiffany Luck @lucktm]

@GarrettLord @jessicarwolf Congratulations!!!

[Tiffany Luck @lucktm]

🔥 go team @samaya_AI !!

[Maithra Raghu @maithra_raghu]

Introducing Samaya Sterling, our new model post-trained for challenging financial research that outperforms the frontier! Sterling started off as a two person research effort to see if we could increase efficiency in agentic orchestration. To our surprise we were able to post-train a much smaller model (Nemotron 3.5 Lightning, a 30B mixture-of-experts model with 3B active parameters) to greater accuracy (58.9% vs 55.9% on FrontierFinance) and 11x lower cost ($0.61 vs $6.89) than Claude Fable 5.1, the best performing frontier model.

We did this via a four-stage training process, two stages of SFT where the model first learns to research with our financial tools and how to construct a good agentic research strategy. The second two stages are RL, with an offline GRPO stage where the model further hones its research strategy from eight teachers that provide diverse approaches, and an online GRPO stage where the model can make end to end attempts and learn from its mistakes.

We see substantial gains from the co-adaptation of the model and harness through post-training, both through the model learning to effectively use the high-quality financial tools, and better compaction approaches that let even a small model execute on longer and deeper research.

We are exploring multiple follow-up directions, from multi-agent research swarms using this small model, to training firm-specific models to encode unique judgement, to training a larger model, where early results show even greater gains from the training process.

The full research is linked below!

[Tiffany Luck @lucktm]

@IlirSela 🤩

[Ari Morcos @arimorcos]

Data quality is the ultimate compute multiplier.

Curated data matched our largest baseline with 6x less compute. A 12B-A1.4B model beat 30B-A3B on a fifth of the pretraining compute.

Live Tuesday 10am PT with @hurrycane @lukemerrick_ @josh_wills: https://t.co/xHMEXqcEcm

[Tiffany Luck @lucktm]

@NWischoff 💯!!

[Tiffany Luck @lucktm]

@nihalmehta @Penn @EniacVC @vijay_r_kumar @PennEngineers @Wharton This is awesome!

[Ann Bordetsky @annbordetsky]

@FactoryAI x @SapienneAI x @sequoia

You don’t want to miss this one

@Theashleyjepson hosting an epic night of building, demos and fireside chats with special guests 🦇

Apply to attend 👇🔗

@francesca_lab @maddiehfaulkner

[Ann Bordetsky @annbordetsky]

Important time to support the growth of the open weight ecosystem

@FactoryAI is a Day 0 launch partner with @reflection_ai Beam

First met @MishaLaskin & team at the Series A and so thrilled to see them driving the American open weight frontier forward and putting this ecosystem together

[The Information @theinformation]

Can AI beat Wall Street analysts at predicting earnings?

Samaya AI Founder and CEO @maithra_raghu “It’s a very high baseline, and it’s only the most recent models that are able to do this.”

“But even in that setting, we actually see that there’s pretty substantial error rates for the models.”

[Tiffany Luck @lucktm]

@mvanhorn @LoloVanHorn 😂 genius

[Tiffany Luck @lucktm]

@maddiehfaulkner @Mentlio Love this!

[Tiffany Luck @lucktm]

@realtarek @tbpn Very on brand for NYC :) congrats again!!

[Tiffany Luck @lucktm]

@arimorcos @datologyai So exciting -- congrats!!

[Tiffany Luck @lucktm]

Amazing progress from team @datologyai with the exciting launch of Datology Curation Studio! Furthering the mission of helping every company own its intelligence using data quality as the ultimate compute multiplier 💥

[DatologyAI @datologyai]

Today, we’re excited to launch the Datology Curation Studio, customizable frontier data curation for every team training its own models.

Data quality is the ultimate compute multiplier, and Curation Studio makes it easy for everyone.

[Tiffany Luck @lucktm]

@datologyai Big day for 'own your intelligence' -- congrats!!

[Maithra Raghu @maithra_raghu]

We’re sharing new results showing that frontier AI models are able to outperform human experts on financial predictions for the first time. We built an environment for the challenging task of Earnings Predictions, finding that the most recent frontier models (Opus 5.5, Fable 5.1, Astra) with Samaya’s harness, meaningfully outperform expert consensus predictions on commonly reported earnings metrics such as company revenue, gross margin and more.

We built this environment to have a “point-in-time” gate in the harness to ensure no information leakage, and created financial retrieval and data tools that would respect this gate and give the models access to the same information available to experts. We also adjusted for bias in expert consensus to create a hard, bias-corrected baseline, where outperformance has real signal. Only the most recent frontier AI models are able to beat this hard baseline, pointing to an important inflection point in AI capabilities in finance.

Like with FrontierFinance, we see substantial headroom for further RL training in this environment — the models show large variations in error across different rollouts. We’re continuing research on using earnings prediction and other predictive tasks to post-train models to learn from their mistakes. Get in touch if you’d like to collaborate or try out an alpha version. Link to the full research below.