AI 2030

Cadre AI
  • 5.0 (5)
  • Business
  • Biweekly

Conversations about the future of AI, with the builders building it. By CadreAI.com

  1. 1d ago

    How Will Businesses Put AI Vision to Work? [Ft Brad Chisum, EyePop.ai]

    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 discussed How the bottleneck moved from model to orchestration Running lightweight detectors before heavy visual language models Where image enhancement stops and guesswork begins Turning existing security cameras into operational data Deciding up front what data is worth extracting Targeting by customer profile instead of industry vertical Capturing make and model to sidestep plate privacy Moving AI vision processing on premise by 2030

    How Will Businesses Put AI Vision to Work? [Ft Brad Chisum, EyePop.ai]

  2. Oct 15

    Who Owns Your AI Agents? [Ft Laura Stapleton, Foley]

    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 descriptions Pushing past AI access and usage to real impact Creating a context-enablement role that PMs AI context Building a shared ontology so humans and agents align Drafting performance reviews in Lattice without the usual dread Running a Value Creation Office for in-work AI support Setting the hiring gates humans, not agents, decide Protecting domain expertise as org chart layers collapse

    Who Owns Your AI Agents? [Ft Laura Stapleton, Foley]

  3. Sep 17

    How Do AI Agents Earn the Trust to Act on Your Behalf? [Ft. Devi Parikh, Yutori]

    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 act Read-only as a deliberate trust-building wedge, not a constraint Per-site sandboxed auth access versus browser-extension exposure In-house browser-use model for the API-less long tail of the web "Inspect work" transparency to build trust in agent actions Prosumer chief-of-staff spanning personal and work context Selling to the employer to reach work context, individual as the user Why connected email, Slack, and Notion unlock proactivity without always-on listening

    How Do AI Agents Earn the Trust to Act on Your Behalf? [Ft. Devi Parikh, Yutori]

  4. Aug 20

    How Will AI Ads Reshape the Internet? [Ft. Nic Baird, Koah Labs]

    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 features Serving ads only when unit-economic positive against token cost 99% approval when a publisher polled users about dropping $5 subscriptions for ads Character AI's interstitial video ad backlash as the anti-pattern Generating ad copy from product data, with static images from the advertiser when brand control matters Mobile CTR at 3-5%, spiking to 14-15% in early tests Extracting intent from emotional context, not stated purchase signals Brand lift and topic lift as substitutes for direct response attribution The rabbit hole ad format that enriches first-party retargeting cohorts Subsidizing marketplace liquidity Uber-style with the $20M raise Personal agents handing off user context as the missing web protocol

    How Will AI Ads Reshape the Internet? [Ft. Nic Baird, Koah Labs]

  5. Aug 6

    Where Will AI Consulting Be in 2030? [Ft Chad Lohrli, Co-Founder & Chief AI Officer]

    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 Shame A 200% jump in hours tracked from visibility alone An 8,000-row JSON prompt that made an HR chatbot accurate Why Cadre treats every prompt as an SOP, not a shortcut A code knowledge base keeping AI-built client products consistent Why reviewing AI output is the real bottleneck now The ten-year model-freeze math behind Cadre's growth bet The contract mistake that taught Cadre where AI shouldn't write

    Where Will AI Consulting Be in 2030? [Ft Chad Lohrli, Co-Founder & Chief AI Officer]

  6. Jul 30

    How Will AI Change Marketing Analytics? [Ft. Alex Lirtsman, CorralData]

    Most attribution setups reward the click and the form fill. Alex Lirtsman, founder and CEO of CorralData, built his platform to reward the person who actually shows up and pays, then feed that signal back to the ad platforms so bidding optimizes toward revenue instead of cheap leads. He and Keith get specific: how Corral sits between a company's data and its LLM of choice as a governance and semantic layer, so permissions set once carry into Claude or ChatGPT, and every metric answers to the company's own definition. Now serving 120 brands, Alex lays out why he thinks the software moat disappears by 2030, leaving proprietary data and service quality as the only defensible advantages. Topics discussed: Why automated bidding rewards clickers, not buyers Feeding offline conversions back to optimize on revenue Matching ad click IDs to EMR records for closed-loop attribution Setting permissions once, inheriting them into any LLM Encoding company-specific definitions of LTV and rebooking rate AI interrogating every feature ticket before writing the PR Running ad campaigns through Google, Meta, and LinkedIn agents Why proprietary data and service become the only 2030 moat

    How Will AI Change Marketing Analytics? [Ft. Alex Lirtsman, CorralData]

  7. Jul 9

    Why AI Isn't a Technology, It's a New Form of Labor [Ft Rohit Sharma, True Ventures]

    Rohit Sharma spent his first decade in Silicon Valley building a company from two employees to 750 before it got acquired, then landed at True Ventures, where he's stayed for almost fourteen years without ever planning the next move. He tells Chad and Dhruv why he calls AI a new form of labor rather than a technology, why LLMs are Shannon's machines and not magic ones, and why founder vision and distribution, not technical execution, are the only things still scarce. Topics discussed: Reframing AI as a new form of labor, not a technology Envisioning 150 customers each getting a critically different product Evolving forward deployed engineers into armies of specialized agents Explaining why LLMs are Shannon's machines, not magic machines Arguing AGI rests on the wrong standard entirely Calling the GPU-transformer fit a historical accident, not design Predicting the same fate for tokens as transistors and servers Locating scarcity in founder vision and distribution, not execution

    Why AI Isn't a Technology, It's a New Form of Labor [Ft Rohit Sharma, True Ventures]

  8. Jun 23

    Is Outbound the Future of Hiring? [Ft. Ishan Gupta, Juicebox]

    Inbound stopped being a signal the moment LLMs let candidates fire the same resume at a thousand companies at once. Ishan Gupta, CTO and Co-founder of Juicebox, argues the fix isn't better filtering of that flood but a shift back to outbound, where his agents read what someone has actually done to infer adjacent skills, tech stacks, and likely employers rather than matching keywords. He's just as clear about what Juicebox won't do: it replaces your sourcing software, not your ATS, never emails a candidate's work address, and stops the moment a recruiter takes over the relationship.  With over 4,000 customers and a fresh $80M round at an $850M valuation, the roadmap is narrow on purpose, going deep on search and a memory layer that learns who actually succeeds at your company instead of building a broad platform. Topics discussed: How inbound volume became noise, pushing hiring outbound Inferring adjacent skills and tech stacks over keyword matching Bolt-on positioning: replacing sourcing software, not the ATS The contact-data waterfall and why work emails stay off-limits Protecting sender-domain reputation through narrow targeting Internal talent rediscovery: mining your team's existing network A company-specific memory layer built on who actually succeeds The outbound-first 2030 view of talent acquisition

    Is Outbound the Future of Hiring? [Ft. Ishan Gupta, Juicebox]
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Conversations about the future of AI, with the builders building it. By CadreAI.com

Information

  • Creator
    Cadre AI
  • Years Active
    2026
  • Episodes
    21
  • Rating
    Explicit
  • Copyright
    © Cadre AI
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    AI 2030