Stathis V.

ml @stripe - but actually a 🤖 in disguise - ( )( )( )

[Emily Sands @emilygsands]

Coding tools changed what’s possible for engineers. We took that same model and asked what could give more Stripes—sales, finance, ops—that same boost. Here’s how we built Stripe’s Knowledge AI Platform. https://t.co/2POTmqjqyD

[Emily Sands @emilygsands]

Had a great time chatting with @swyx and @FanaHOVA about all things AI at Stripe: from how we are enabling agentic commerce and building our own foundation model, to how we use AI tools internally and power the world’s fastest-growing AI companies

https://t.co/9dJAmH98eC

[gabriel @gabriel1]

INVITE CODE: 0B8Z7G

[Patrick Collison @patrickc]

We have three cool announcements today:

(1) @OpenAI is launching commerce in ChatGPT. Their new Instant Checkout is powered by @stripe.
(2) We're releasing the Agentic Commerce Protocol, codeveloped by Stripe and OpenAI.
(3) @stripe is launching an API for agentic payments, called Shared Payment Tokens.

It's clear that internet purchasing modalities are going to change a lot, and we're excited to start to lay some of the foundations.

Links below!

[Stathis V. @techabilly]

@eliebakouch @giffmana @lvwerra @lvwerra Is the GQA correctly depicted? Shouldn't the group be on the KV ?

[Anastasios Gerontopoulos @NasosGer]

1/n Multi-token prediction boosts LLMs (DeepSeek-V3), tackling key limitations of the next-token setup:
• Short-term focus
• Struggles with long-range decisions
• Weaker supervision

Prior methods add complexity (extra layers)
🔑 Our fix? Register tokens—elegant and powerful

[Emmanuel Ameisen @mlpowered]

Curious about how LLMs work?

I discussed our recent research, and how to apply circuit tracing to answer arbitrary questions about how models work.

We also snuck in a demo of an open source tool you can use today, and shared tips about getting into the field!

Check it out!

[Stathis V. @techabilly]

@yacineMTB @thegautam 👨‍🍳indeed

[Patrick Collison @patrickc]

The @stripe product keynote just finished!

We announced a lot of new stuff. (We actually couldn't fit everything into the keynote.) Some highlights:

• We've built and deployed the first general-purpose Payments Foundation Model. Trained on tens of billions of transactions and already leading to very large fraud reductions.
• We announced Stablecoin Financial Accounts. Businesses in 101(!) more countries can now store stablecoin balances in Stripe and use our regular fiat money movement rails around the world. The magic of stablecoins comes when they're integrated with the rest of the financial ecosystem. I think it's the biggest international launch we'll ever do -- businesses from 151 countries can now use Stripe.
• Stripe Profiles: your public identity on the Stripe network. Stripe Profiles will let you streamline invoicing and payments with other businesses on Stripe. (We’ll soon launch reservations for Stripe Profiles.)
• Stripe Verified, a new service that will help you navigate global requirements for payment processing with personalized monitoring, enhanced protections, and expert assistance.
• We're adding multicurrency support to Stripe accounts: instead of converting all balances to a single currency, we're going to make it easy to store, convert, and operate across different currencies. We want to make it possible to use Stripe as your financial home.
• Stripe Orchestration: manage multiple payment processors directly within Stripe.
• Support for many new payment methods. You can now access more than 125 global payment methods on Stripe, including stablecoins and real-time payment schemes like Pix and UPI.
• We’ve upgraded the AI models built into Optimized Checkout Suite and now use more than 100 signals to personalize the checkout in real time. A/B tests indicate that Optimized Checkout Suite increases revenue by 11.9% on average.
• Managed Payments: merchant of record is coming to Stripe! Enter new markets while we take care of global taxes, fraud prevention, dispute management, fulfillment, and more.
• A new Connect dashboard, with vastly improved visibility into what's happening across your platform.
• Radar for Connect: it’s now way easier to detect and manage fraud and compliance issues with subaccounts.
• Networked onboarding for platforms: users no longer need to re-enter their details if they’ve onboarded before.
• Smart Disputes: AI automatically responds to disputes for you. Early results show that companies win 13% more chargebacks. We also put skin in the game: we cover the fee if you lose.
• Radar now supports ACH and SEPA. This reduces fraud rates by 20% and 42% respectively.
• A bunch of usage-based billing improvements. It can now ingest up to 100,000 events every second.
• Stripe Terminal can now be used with third-party hardware, starting with Verifone.
• Klarna is coming to Link.
• Authorization Boost. AI automatically updates cards and tokens, determines optimal transaction routing across networks, and determines if and how to retry failed payments (if we’re confident that the failure was ephemeral).
• Radar risk scores are now available via the API. (I.e. they can become a feature for in-house fraud systems.)
• Stripe Tax is now available in 102 countries (up from 57 last year), and we also now automate the entire tax lifecycle (monitoring, registration, collection, filing).
• New FX Quotes API.
• Payment method sharing: if you have multiple Stripe accounts, you can now share payment details across them, saving the need to have customers re-enter their details.
• Stripe Issuing now supports consumer card programs. (Long-requested!)
• Global Payouts: pay customers, contractors, and other third parties with just an email address.
• Stripe Workflows: build, test, and execute multistep flows that orchestrate behavior across Stripe multiple products—using your data, Stripe APIs, and conditional logic.
• Stripe Scripts: a new way to extend and customize the logic in Stripe Billing. This will soon expand to cover many parts of the Stripe platform.
• Benchmarking: compare your performance to similar businesses on Stripe.
• You can now import third-party data into Stripe Capital, which enables expanded eligibility for your customers.
• A new Issuing rules engine: granular configurability over authorization decisions.
• Global Issuing via stablecoins: a simple and cohesive way to administer global card programs. Ramp is using this to massively accelerate their global expansion.
• Data in Sigma is now far faster (6 hour latency, down from 24 hours). That'll continue to get faster in the coming months. Similar data freshness improvements across other products.
• You can now use Instant Payouts to withdraw funds from Stripe to debit cards in 32 countries.
• The Dashboard AI agent: create products, process refunds, and generate payment links by just telling our little critter what to do.

Whew! I’m not sure I even covered everything. A few of the above are years in the works and could have received launch events by themselves. I’m very proud of the team. So what are the common themes?

• We’re rethinking and upgrading pretty much every part of the stack with AI. (Embedding/foundation models, agents, etc.)
• Stablecoins are enabling unprecedented global access and expansion.
• We’re making a lot of things deeply configurable that weren’t previously. (Scripts, Workflows, and more.) Ties to our goal of building truly programmable, global financial services.

[Gautam Kedia @thegautam]

TL;DR: We built a transformer-based payments foundation model. It works.

For years, Stripe has been using machine learning models trained on discrete features (BIN, zip, payment method, etc.) to improve our products for users. And these feature-by-feature efforts have worked well: +15% conversion, -30% fraud.

But these models have limitations. We have to select (and therefore constrain) the features considered by the model. And each model requires task-specific training: for authorization, for fraud, for disputes, and so on.
Given the learning power of generalized transformer architectures, we wondered whether an LLM-style approach could work here. It wasn’t obvious that it would—payments is like language in some ways (structural patterns similar to syntax and semantics, temporally sequential) and extremely unlike language in others (fewer distinct ‘tokens’, contextual sparsity, fewer organizing principles akin to grammatical rules).

So we built a payments foundation model—a self-supervised network that learns dense, general-purpose vectors for every transaction, much like a language model embeds words. Trained on tens of billions of transactions, it distills each charge’s key signals into a single, versatile embedding.

You can think of the result as a vast distribution of payments in a high-dimensional vector space. The location of each embedding captures rich data, including how different elements relate to each other. Payments that share similarities naturally cluster together: transactions from the same card issuer are positioned closer together, those from the same bank even closer, and those sharing the same email address are nearly identical.

These rich embeddings make it significantly easier to spot nuanced, adversarial patterns of transactions; and to build more accurate classifiers based on both the features of an individual payment and its relationship to other payments in the sequence.

Take card-testing. Over the past couple of years traditional ML approaches (engineering new features, labeling emerging attack patterns, rapidly retraining our models) have reduced card testing for users on Stripe by 80%. But the most sophisticated card testers hide novel attack patterns in the volumes of the largest companies, so they’re hard to spot with these methods.

We built a classifier that ingests sequences of embeddings from the foundation model, and predicts if the traffic slice is under an attack. It leverages transformer architecture to detect subtle patterns across transaction sequences. And it does this all in real time so we can block attacks before they hit businesses.

This approach improved our detection rate for card-testing attacks on large users from 59% to 97% overnight.

This has an instant impact for our large users. But the real power of the foundation model is that these same embeddings can be applied across other tasks, like disputes or authorizations.

Perhaps even more fundamentally, it suggests that payments have semantic meaning. Just like words in a sentence, transactions possess complex sequential dependencies and latent feature interactions that simply can’t be captured by manual feature engineering.

Turns out attention was all payments needed!

[Basil Halperin @BasilHalperin]

Introducing the Stripe Economics of AI Fellowship:

The economics of AI remains surprisingly understudied. The fellowship aims to help fill that gap, by supporting grad students and early-career researchers with $, data, a conference, and community –

[Stripe Developers @StripeDev]

We’ve added /llms.txt and Markdown to @stripe docs: https://t.co/mG9FIaXLTS

Use the .md pages to quickly move Stripe knowledge into your LLM of choice. 📄

[Nantas Nardelli @nntsn]

I'm so *wildly* excited to finally be able to share that I have joined Pacific Fusion and I'm starting an AI team!

PF is the product of a rare alignment of a bunch of exceptional events. The result? A realistic path to fusion energy in years instead of decades.

Story time! 🧵

[Stathis V. @techabilly]

@TryCoveAI @sequoia 🥳🥳🥳 Congratulations to the team!

[Stathis V. @techabilly]

Big congratulations to the team @TryCoveAI !! They've put a lot of effort into creating a unique way to interact with LLMs.

[Cove @TryCoveAI]

🚀 Today, we're thrilled to introduce Cove, a groundbreaking interface for thinking brilliantly with AI, and announce our seed round led by @sequoia

Cove is not a chatbot. It’s AI unconstrained by a chat thread or document editor.

See it in action:

[Cove @TryCoveAI]

🚀 Today, we're thrilled to introduce Cove, a groundbreaking interface for thinking brilliantly with AI, and announce our seed round led by @sequoia

Cove is not a chatbot. It’s AI unconstrained by a chat thread or document editor.

See it in action:

[Stathis V. @techabilly]

@yacineMTB got a link!?

[Stathis V. @techabilly]

[2/2] If you are interested fill out the form: https://t.co/v6Ac0vgomv

[Stathis V. @techabilly]

[1/2] Join us for an evening with @stripe 's AI/ML teams. We'll deep dive into the essence of our work, the opportunities that lie ahead, and how we bring added value to Stripe and our users.

#ArtificialIntelligence #MachineLearning #AI #ML

[Stathis V. @techabilly]

@sedielem @goodfellow_ian @karpathy @ylecun @alfcnz @kaggle Lasagne was my favourite !