“Why do I have to tell your chatbot to do something? Just do it.”

In this episode, Jeff Seibert – founder of Digits (AI-native accounting platform), former Twitter Head of Product, and the engineer behind Crashlytics (now on 6 billion devices) – reveals what it actually takes to build AI-native companies from scratch. We explore why most companies are getting AI wrong by bolting chatbots onto old products, how to structure teams for extreme velocity, and why the accounting industry is about to experience its HP-35 calculator moment. Jeff’s bold prediction: the entire month-end close process will be automated within 12 months.

What You’ll Discover:

[02:45] Why Accounting Data Quality is Decades Behind Product Analytics → The genesis story of Digits: when Twitter’s 100-person finance team couldn’t answer a simple budget question in under three weeks

[08:28] Building Companies for AI From Day One → How ML-native architecture differs from traditional databases and why this matters more than the AI hype suggests

[10:31] The 65-Person Company That Runs All-Hands Every 48 Hours → Jeff’s radical approach to velocity: weekly sprints, fractal team structures, and why they’ll never hire “lone eagle” engineers

[15:20] Keeping Teams Intentionally Small at Scale → How to eliminate the “empire building” problem by dissociating engineering coaches from project staffing

[19:59] What CEOs Actually Do That AI Can’t Replace (Yet) → The 10%/90% leadership philosophy and why Sundar Pichai’s “AI will replace CEOs” take misses the point

[23:30] Disrupting QuickBooks: Technology vs. Distribution → Why accounting is uniquely suited for AI disruption and how startups can outpace 800-pound gorillas

[26:14] Why AI Isn’t Just Another Ajax Moment → The fundamental shift from “talk to our chatbot” to “the AI should just do it” – and what that means for software architecture

[30:47] The Architectural Wall Ahead for Large Language Models → Why current LLM architecture won’t reach AGI: the context window problem, lack of memory, and inability to backtrack during inference

[32:05] The Great Work Displacement: Data Entry is Dead by 2026 → Jeff’s evolved prediction on AI’s economic impact and why the “lump of labor fallacy” applies to automation fears

Key Takeaways:

• AI-native means redesigning your data architecture from scratch, not adding a chatbot interface to legacy systems
• Run your company on the shortest planning horizon you can see – for Digits, that’s 4-5 week “horizons”
• Hire senior people who are “chill” with strong opinions, loosely held – and actively filter out solo operators
• The most powerful AI products won’t ask users what to do – they’ll understand the goal and just execute
• Accounting’s month-end close will be automated by end of 2025, marking one of AI’s first complete workflow eliminations

About Jeff Seibert:

Jeff is the founder and CEO of Digits, the AI-native accounting platform. Previously, he served as Twitter’s Head of Consumer Product (launching the algorithmic timeline), co-founded Crashlytics (acquired by Twitter, now runs on 6 billion smartphones), and was featured in Netflix’s Emmy-winning documentary “The Social Dilemma.” He’s backed 100+ startups as an angel investor and has been building software since releasing his first app at age 12.

Related Links:

• Digits