The AI Why with Liam Lawson

In this episode, Jason Li, CTO of Laurel, reveals how the company is turning timesheets into the AI playbook for the entire knowledge-work economy. Jason breaks down why $2,000/hour lawyers still spend Saturdays manually filling out time in six-minute increments, how Laurel's AI platform automatically captures every click, email, and meeting, and why data (not code) is the only real moat left in the age of the SaaSpocalypse. Jason shares how Ernst & Young is using Laurel to identify high-leverage work, why Laurel deliberately integrates with "decades-old" software like Classic Outlook that most startups ignore, and the counter-intuitive reason your best rainmakers should never be forced into cookie-cutter roles again. He also explains why Laurel doesn't train its own LLM, how they run AI feedback loops that self-iterate prompts, and the frameworks leaders can use to actually measure AI ROI instead of just surveying "did it help?" Key Topics Covered: Why "what gets measured gets managed" is the most important rule in AI adoption The Moneyball insight that changed how Jason thinks about metrics How Laurel auto-generates timesheets for lawyers and accountants Why Ernst & Young chose Laurel for their tax group The hidden cost of manual timesheets for $2K/hour professionals How Laurel maps knowledge work to a company's "work ontology" Why decades-old software (Classic Outlook) is a competitive moat, not a liability The SaaSpocalypse: what survives when AI eats applications How to measure if an AI tool actually delivers ROI Why data, not models, is the real defensible asset in AI Episode Timestamps: 00:00 - Intro 00:25 - The Peter Drucker quote that shaped Jason's career 02:49 - A Moneyball analogy for AI adoption 03:25 - What Laurel actually does: the AI platform that maps time to outcomes 07:19 - Why every business (not just law firms) needs time visibility 09:17 - Inside the Ernst & Young deployment 12:27 - Jason's journey to becoming CTO at Laurel 14:21 - Live product demo: Laurel's work ontology engine 17:49 - How AI shifts the line between high and low leverage work 21:15 - What onboarding a 2,000-person firm actually looks like 23:06 - The technical architecture behind Laurel's desktop client 28:35 - Why Laurel doesn't train its own LLM 29:39 - How Laurel handles AI models "getting worse" overnight 33:35 - Capturing time for work that doesn't happen on a computer 37:17 - AI adoption meets employee behavior change 41:54 - The SaaSpocalypse and why Laurel's moat is data, not software 48:00 - Why Jason left Ironclad to join Laurel 51:16 - Jason's answer to The AI Why's signature closing question Jason Li's Socials: LinkedIn: https://www.linkedin.com/in/jasonhli/ Laurel: https://www.laurel.ai Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: www.theaireport.ai/leaders-launch-guide Learn more about your ad choices. Visit megaphone.fm/adchoices

Monday.com has 225,000 customers and over 60,000 seats at some of its largest accounts. So when its co-founder says the company completely changed its vision, that is worth paying attention to. In this episode, Liam Lawson sits down with Roy Mann, co-founder of Monday.com, to talk about the three waves of AI, why managing work is no longer the goal, and how Monday is now betting everything on agents actually doing the work instead. Roy also breaks down Agent Talent, the marketplace where companies can hire AI agents like employees, why Monday opened its platform to agents as first class citizens, and what adaptability really means when technology is moving this fast. Stories Covered This Week: The three waves of AI and why wave three changes everything Why Monday changed its core vision from managing work to doing the work Agent Talent: hiring AI agents like employees with real job postings and qualifications Selling to agents, not just humans, and what that marketing looks like The SaaS apocalypse and whether the per seat model is actually dead OpenClaw, open source AI, and why Roy thinks this is a democratic technology The future of work, abundance vs scarcity, and why adaptability is the only skill that matters Timestamps: 00:00 Intro 00:45 The three waves of AI 03:42 Customer reception to agents 07:16 The developer who went from terrified to empowered 10:33 What personality traits succeed in the agent economy 12:16 Monday's internal AI infrastructure 19:44 Agent Talent and selling to agents 24:50 How to sell to an agent 27:37 Testing and qualifying agents with Sensei 30:22 Why open source matters 32:22 The future of work 36:44 Is the per seat model dead? 39:51 OpenClaw and local models 44:04 Paperclip and multi-agent orchestration 52:09 Roy's ideal future of work 56:42 Betting everything on agents with Monday stock down 76% 01:00:00 Where Monday's adaptability comes from 01:01:43 Why do you do what you do? Partner Links Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Free AI Tool Stack: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Join the community: www.theaireport.ai/leaders-launch-guide Learn more about your ad choices. Visit megaphone.fm/adchoices