AI 2030

Cadre AI

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

  1. 5d ago

    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]

  2. 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]

  3. 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]

  4. 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]

  5. Jun 9

    The Hidden Reason Most Enterprise AI Deployments Break Down [Ft. Binny Gill, Kognitos]

    Most enterprise AI projects don't fail because the AI can't perform. They fail because nobody documented what the AI is actually supposed to do, or how it should behave when things get ambiguous. Binny Gill, former CTO of Nutanix and now CEO of Kognitos, spent five years solving that by making English the programming language for business process automation, with a proprietary interpreter that extracts precision from ambiguity rather than demanding it upfront, the same way a parent figures out what a toddler wants. His architecture deliberately avoids using LLMs during execution. For deterministic workflows like order-to-cash or vendor onboarding, injecting a creative model at runtime is a liability. The LLM earns its place at design time. He also makes a pointed argument about what AI governance actually requires that most companies are completely unprepared for: with AI, every model is a blank slate, which means the tacit knowledge that's always lived in people's heads has to be explicitly captured or it won't exist. And his 2030 prediction reframes the whole conversation away from technology capability and toward organizational architecture. Topics discussed: Why the English interpreter was always the problem, not the language itself Neuro-symbolic architecture: separating creative AI at design time from deterministic execution at runtime Why the toddler model of ambiguity resolution beats forcing precise inputs upfront Why zero behavioral assumptions with AI models demand a new documentation standard How AI autonomously authors tribal knowledge rather than relying on humans to write it down The three enterprise buyer personas (CFO, CIO, Chief AI Officer) and what each one is actually evaluating Tailor shop vs. assembly line as the framework for assessing organizational AI readiness by 2030 Why Binny predicts legacy company architecture, not technology, is what fails in the transition

    The Hidden Reason Most Enterprise AI Deployments Break Down [Ft. Binny Gill, Kognitos]

  6. Jun 2

    Is AI Actually Disrupting Venture Capital? [Ft. Will Quist, Slow Ventures]

    Most VCs are chasing AI deals right now. Will Quist, Partner at Slow Ventures, thinks most of them are chasing the wrong thing. With somewhere around $1B deployed across pre-seed and seed, Will makes a direct case that the shift from client-server to SaaS was more economically disruptive than anything AI is doing to software today, and that building a defensible company in this environment requires the same fundamentals most founders skip. Will walks through the logic-chain framework Slow Ventures uses to decide whether anything is actually venture-backable: is the hypothesis novel, falsifiable, and objectively valuable if true? He applies that same test to the current AI wave, separating structural arbitrage from deals that are just riding a spread until it closes. He closes with a point almost nobody in the room is making: why selling software is sometimes the worst financial decision a software company can make. Topics discussed: Novel, falsifiable hypothesis framework for venture-backable ideas Why AI SaaS is incremental, not transformational disruption Structural vs. spotted arbitrage and why only one compounds into enterprise value "Leverage is leverage" mental model for evaluating startup valuations Valuation gravity: pricing rounds so 24-month milestones are actually achievable Fast boil vs. slow boil deals and where early-stage returns actually come from Why complex customer relationships and proprietary data sets may outlast pure AI plays Growth buyouts and vertical integration as higher free cash flow alternatives to selling software

    Is AI Actually Disrupting Venture Capital? [Ft. Will Quist, Slow Ventures]

  7. May 19

    What It Actually Takes to Build an AI Company [Ft. Francois Chaubard, Y Combinator]

    Francois Chaubard co-created CS224D at Stanford with Richard Socher and others, the course that replaced the entire NLP curriculum two years after launch and still carries his name. He then spent nine years as a founder before closing a $60M enterprise deal at Focal. He is now a Visiting Partner at Y Combinator and a PhD researcher working on alternatives to backpropagation. He is not a generalist with opinions. In this conversation with Chad, Francois opens with a provocation: in terms of fluid intelligence, humans still dominate. The ARC-AGI 3 benchmark makes this concrete. The best LLMs are 50x less efficient than humans on novel tasks. Nucleus sampling structurally prevents LLMs from ever being funny, not as a solvable limitation but as a mathematical consequence of how the word "pretend" sits at 2-in-1,000 token probability. Stanford's entire compute cluster for 4,000 CS students is 250 H100s, what a single OpenAI new hire gets on day one. Each of these points has an implication, and Francois spells them out. Topics discussed: The Stanford vs. MIT founder model and why novelty is structurally a startup liability YC's "agency and taste" funding criteria and what it looks like in a three-week window Why vibe coding and Claude Code collapse activation energy for international founders GPU-per-student as a research moat metric and the compute gap between academia and industry Why in-context learning does not monotonically improve and what that means for agents The batch size one problem: why humans learn stably from a single example and models cannot The token probability argument for why nucleus sampling structurally prevents LLM humor Cartridges: learning a compressed KV cache from random initialization using SGD, with the model frozen Auto Research with good ideas: a priority queue over ideas.md ranked by expected value, with a Gemini Pro reflection loop EVO's DNA base-pair prediction model: capped at 80B parameters due to funding, and what scaling to 800B might mean

    What It Actually Takes to Build an AI Company [Ft. Francois Chaubard, Y Combinator]

  8. May 12

    What Happens When AI Agents Replace Knowledge Work? [Ft. Jay Hack, ClickUp]

    Jay Hack was building async coding agents before the term "agentic AI" existed in the mainstream, back when GitHub Copilot was the ceiling of ambition and everyone else was focused on IDE autocomplete. As Head of AI at ClickUp, he's now running one of the most ambitious agent bets in enterprise software, a platform that treats agents not as a feature, but as the primary way work gets done. Jay tells Keith why context, not model capability, is the actual ceiling on what agents can accomplish, how ClickUp's "super agent" is already running background workflows that used to require a human, and why first-party data integration will make externally-built agents structurally obsolete for most companies. He also shares where he thinks the next real step-function in productivity comes from, and it's not a better LLM. Topics discussed: Why context, not model capability, is the hard ceiling on agent performance Training coding ability to improve model performance across every other domain How ClickUp's super agent handles async workflows without human initiation First-party data integration as a structural moat against external agent tools Why the no-code failure doesn't doom generative UI VibeUp: dynamically generated interfaces connected directly to live workspace data The multi-agent architecture: personal AI clients running alongside company-specific async agents Brain-machine interfaces as the next real step-function in human productivity

    What Happens When AI Agents Replace Knowledge Work? [Ft. Jay Hack, ClickUp]
See All (17)

Ratings & Reviews

5
out of 5
5 Ratings

About

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

Information

  • Creator
    Cadre AI
  • Years Active
    2026
  • Episodes
    17
  • Rating
    Explicit
  • Copyright
    © Cadre AI
  • Show Website
    AI 2030