“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

“We couldn’t start inkjet again if we had it in our hands because we’re not meeting the rules.” That stark admission from former HP Fellow Will Allen reveals why even the most innovative companies struggle to recreate their own breakthroughs—and what it takes to actually scale disruptive technology.

In this episode, Will Allen, holder of 102 US patents and the first HP Fellow promoted within HP’s Global Print Business, takes us inside three decades of Silicon Valley innovation from logic analyzers to consumer inkjet printing to his current role as CTO at Kaspix, where he’s pioneering ultra-low-power AI inference using analog circuits. We explore why research should be treated as investment portfolio management (not an expense to cut), how “showing beats telling” when getting buy-in for radical ideas, and why getting AI to the “true edge” – directly at sensors and actuators – will fundamentally change computing economics.

What You’ll Discover:

[00:00] Why Research and Development Are Two Different Things

→ The fatal mistake of treating R&D as a single expense line when research is actually an investment with portfolio-level returns

[06:03] Has Silicon Valley Run Out of Ideas?

→ Why scaling success creates the very constraints that prevent future innovation, and whether we’re less innovative than decades past

[10:12] The Scaling Trap That Kills Success

→ Real HP story: how field-fixing problems scaled so badly that engineers couldn’t design problems out, and what this means for any growing business

[16:23] Getting Past the “$100 Million Question”

→ How to navigate corporate demand for predictable returns when developing something the market has never seen before

[18:03] “A Functioning Proto Is Worth a Thousand Pictures”

→ The clownfish story: how a weekend demo got low-drop-volume printing approved after months of rejection, and the art of communicating on stakeholders’ terms

[21:16] Signal Spotting and Fundamental vs Killer Apps

→ Will’s framework for distinguishing noise from transformational trends—and why asking “what’s the killer app?” might be the wrong question

[24:47] Kaspix and the True Edge

→ Why analog circuits for AI inference could be as transformative as the mouse, enabling intelligence directly at transducers without memory-compute bottlenecks

[29:56] Where AI Is Actually Heading

→ Beyond the hype: specialized AIs, “AI middle management,” and why rapid societal change from deterministic technology creates uncomfortable transitions

[36:04] The Advice Will Would Give His Younger Self

→ Why leaders who invested years in education suddenly think quarterly, and how to reclaim the long-term thinking that got you there

About Will Allen:

Will Allen is CTO at Kaspix, pioneering ultra-low-power AI inference through analog circuit design. Previously, he spent 30 years at HP, becoming the first HP Fellow promoted within HP’s Global Print Business. He designed the color imaging pipeline used in HP’s first 4 million color consumer inkjet printers, led IP production in HP Labs’ AI and Emerging Compute Lab, and holds 102 issued US patents across printing, displays, robotics, and digital imaging.

Related Links:

• Will’s LinkedIn Profile

• Will’s Professional Homepage
• Will’s YouTube Channel
• Kaspix
• Kaspix Founder Pablo Zegers interviewed on The Innovators Podcast by John Biggs

What if the leadership frameworks we’ve relied on for decades are fundamentally broken for today’s world? In this thought-provoking conversation, Pascal sits down with Jeffrey Rogers—his collaborator of nearly a decade—to explore how leaders can navigate sustained uncertainty and systematic disruption.

Jeffrey, an expert in organizational learning and futures thinking, challenges the “Good to Great” era of one-size-fits-all leadership models. Instead, he advocates for meta-learning: the ability to learn how to learn, adapt frameworks contextually, and build organizations that can transform repeatedly. They dive deep into the tension between efficiency and experimentation, why the “middle horizon” (5-10 years) is so hard to envision, and how generative AI fits into organizational learning—spoiler: it’s not the efficiency tool you think it is.

Key Topics Covered:

• Why best practices are dead and what replaces them in high-uncertainty environments
• The efficiency vs. learning paradox: how to balance execution with experimentation
• Generative AI’s real value: rapid prototyping, not scaling (and why that matters)
• The futures cone and how to think about multiple possible futures
• Practical advice for middle managers when leadership won’t listen
• Building learning systems that connect across your organization