AI 2030

In AI vision, the bottleneck is no longer the model. High-quality models are everywhere now, and the real work has moved to orchestration, stitching lightweight detectors and heavy visual language models together without burning compute on every frame.Brad Chisum, CEO of EyePop.ai, shows Keith how mid-market teams can run computer vision on cameras they already own, crop to the pixels that matter, and decide up front what data is actually worth extracting.Topics discussedHow the bottleneck moved from model to orchestrationRunning lightweight detectors before heavy visual language modelsWhere image enhancement stops and guesswork beginsTurning existing security cameras into operational dataDeciding up front what data is worth extractingTargeting by customer profile instead of industry verticalCapturing make and model to sidestep plate privacyMoving AI vision processing on premise by 2030

Laura Stapleton joined Foley as Chief People Officer with a mandate she had never been handed: lead an AI transformation, not just run HR. Her answer is stewardship, assigning each agent to whoever owns that domain, regardless of title, and writing it into job descriptions.She walks host Keith Jensen through Foley's Access, Usage, Impact maturity curve, why a shared ontology decides whether agents work, and how AI drafted performance reviews in Lattice that people did not hate. Her through-line: jobs change, headcount logic breaks, accountability stays human.Topics discussed:Writing agent stewardship into formal job descriptionsPushing past AI access and usage to real impactCreating a context-enablement role that PMs AI contextBuilding a shared ontology so humans and agents alignDrafting performance reviews in Lattice without the usual dreadRunning a Value Creation Office for in-work AI supportSetting the hiring gates humans, not agents, decideProtecting domain expertise as org chart layers collapse

Devi Parikh ran generative AI as a senior director at Meta for eight years before leaving to build Yutori, where she's making AI agents proactive rather than reactive. The design choice that stands out: the first product, Scouts, asks for zero access to your context and only reads the public web. Trust gets earned before an agent touches your money, your logins, or your calendar.Devi takes Keith through Yutori's "trust staircase" and why read-only was the deliberate starting point, not a limitation. She details the near-term shift from notifying you to completing tasks, where you sandbox specific sites behind auth walls one at a time instead of handing over a browser extension with access to every logged-in state. For the long tail of the web with no API or MCP server, Scouts spin up a remote browser and use a browser-use model Yutori trained in-house to click through and complete the task.Topics discussed:Yutori's "trust staircase" for earning permission to actRead-only as a deliberate trust-building wedge, not a constraintPer-site sandboxed auth access versus browser-extension exposureIn-house browser-use model for the API-less long tail of the web"Inspect work" transparency to build trust in agent actionsProsumer chief-of-staff spanning personal and work contextSelling to the employer to reach work context, individual as the userWhy connected email, Slack, and Notion unlock proactivity without always-on listening

Most AI apps treat ads as a tax on the product. Nic Baird, co-founder of Koah Labs, is betting the opposite: that at $2-3 per thousand queries, an AI app that refuses ads either paywalls its best features or dies. He tells Keith why Koah only serves an ad when it's unit-economic positive against token cost, and why relevance is the only defensible moat in this category.Nic goes into the mechanics: how Koah generates ad copy from product data (with static images coming from the advertiser when brand control matters), why standard click attribution breaks when nobody transacts inside a chat interface, and the brand-lift and topic-lift proxies he's testing instead. He also lays out where he thinks websites go next, and why a personal agent handing off context between sites is the protocol someone needs to build.Topics discussed:The $2-3 per thousand queries cost floor forcing AI apps to paywall featuresServing ads only when unit-economic positive against token cost99% approval when a publisher polled users about dropping $5 subscriptions for adsCharacter AI's interstitial video ad backlash as the anti-patternGenerating ad copy from product data, with static images from the advertiser when brand control mattersMobile CTR at 3-5%, spiking to 14-15% in early testsExtracting intent from emotional context, not stated purchase signalsBrand lift and topic lift as substitutes for direct response attributionThe rabbit hole ad format that enriches first-party retargeting cohortsSubsidizing marketplace liquidity Uber-style with the $20M raisePersonal agents handing off user context as the missing web protocol

What does it take to build an AI consultancy that's tracking for 3x revenue growth in year two, with a founder who thinks he's got a ten-year head start before anyone can catch up? Chad Lohrli, Co-Founder and Chief AI Officer at Cadre AI, walks Keith Jensen through the specific operating mechanics behind that growth: a goal-tracking system built around paper on the wall with plans to add a public Hall of Shame for missed goals, the 8,000-row prompt it took to make an internal chatbot actually reliable, and the exact point in the AI workflow where Chad says things start to go sideways.Topics discussed:Printing goals on paper walls and planning a public Hall of ShameA 200% jump in hours tracked from visibility aloneAn 8,000-row JSON prompt that made an HR chatbot accurateWhy Cadre treats every prompt as an SOP, not a shortcutA code knowledge base keeping AI-built client products consistentWhy reviewing AI output is the real bottleneck nowThe ten-year model-freeze math behind Cadre's growth betThe contract mistake that taught Cadre where AI shouldn't write