But Stripe actually benchmarked the current generation AI leaders against previous generation SaaS leaders, and empirically, they are growing much faster—in terms of real revenue from real companies/customers—than SaaS was in some very good years.
@patio11
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Stripe observes explosive growth in AI company adoption
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We also discuss Stripe's front row seat to emerging AI companies. A commanding majority of household names, and some which aren't as well-known yet, use Stripe for payments, billing, etc. Many people with an informed POV on growth rates have been surprised how fast AI growing.
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Financial Industry Failures and Systemic Customer Impact
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… abandons the transaction wondering why the entire financial industry cannot seem to get its #%$*(#(% together. And thus, a small amount of getting stuff together, with extremely leveraged consequences over the customer base.
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AI Prevents Payment Declines Generating Billions in Revenue
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This ends up creating literally billions of dollars of extra revenue for customers, which might otherwise have been lost as e.g. a card is declined, the user calls the bank, the bank CS rep gives the true and common "We have no idea why that was declined" answer, and customer…
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Stripe’s Scale Advantage: Probing Global Financial System
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Using the incredible scale advantage, Stripe is able to hold back a small portion of global retries (i.e. a very large number of retries) to perturb to probe the global financial system, trying e.g. different routes or different semantically equivalent ways to phrase messages.
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Banking System Vetoes: Legacy Code and Hidden Decision Logic
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No team anywhere has total visibility into *why* those vetoes happen. At one bank, it might be a legacy software issue that no programmer has actually opened since 1986. At another, it might be a poorly coded heuristic. There might be literally misaligned wires somewhere else.
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Stripe’s Trillion-Dollar Data Scale Enables AI Decision-Making
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The scale of data involved is staggering, too: Stripe processed about $1.4 trillion (yes, with a T) in 2024, which implies many billions of transactions, each of them with a lifecycle at which one could make several interesting decisions to improve outcomes.
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Machine Learning in Payment Processing: High-Velocity Decision Testing
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Payment processing is an unusually fruitful testbed for machine learning, because one is making very high-velocity decisions and being graded on them objectively very quickly: microseconds later by e.g. issuing banks, days to weeks later in the case of waiting for a fraud report.
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Stripe’s 15 Years AI/ML Experience and AI Economy Insights
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This week on Complex Systems I talked with Emily Sands about Stripe's 15 year experience with AI/ML and current observations on the AI economy. I think you'll really enjoy the conversation.
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Essays as Perpetual Value Investments in AI Era
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There are very few essays I’ve ever written that would not be worth $10/yr in perpetuity, particularly on a portfolio basis.