Podcast
AI with Alec. Get smarter on AI. The easy way.
Hosted by Alec Coughlin · EN
Conversations with leading technical minds in Artificial Intelligence - from CTOs of pioneering AI startups to AI architects at Fortune 500 companies. We explore their strategies, implementations, and innovations to help you better understand and deploy AI in the real world. Hear enterprise AI insights and practical perspectives you won't find anywhere else. If you're a technical leader, business executive, AI practitioner, or innovation strategist, this is for you.
11episodes
Episodes
Newest firstAll episodes
Are Your AI Agents Reasoning From a Snapshot That Never Existed? | Tacnode Founder + CEO
4w ago00:32:49Tap to summarizeCould Tacnode be the next Databricks? Yes. Imagine the world before writing. Before books. People had to learn everything from scratch.Thanks to writing, thanks to books, knowledge compounded. Everyone could learn from each other.Today's AI agents don't yet have their version of writing or books. They operate in isolation, without the shared understanding or context of what previous agents and humans have experienced. They have to start over. Learn everything from scratch.This is the context gap.Tacnode exists to solve the context gap. I interviewed Xiaowei Jiang, Tacnode founder + CEO on AI with Alec E31. What follows are four of his arguments that have stuck with me, worth weighing in your own context. 1: The primary user of enterprise software is shifting from humans to agents.Databricks. $5.4B revenue run-rate. 65% YoY growth. $134B valuation. 60%+ of the Fortune 500. The most valuable private enterprise software company in the world.That's the bar.In Xiaowei's view, lakehouse architecture became the standard because humans were the primary consumer of data. Databricks and Snowflake built extraordinary capabilities for the analytical workloads they were designed to serve.But humans are slow. A handful of decisions a day with long gaps. Pipelines had time to catch up. Caches had time to refresh.Agents collapse those assumptions. Thousands of decisions a second. No human in the loop. Zero tolerance for conflicting signals.This isn't about replacing analytical platforms. Tacnode sits alongside the lakehouse, purpose-built for real-time agent decisions. 2: Most "AI failures" are not model failures. They are context failures.Xiaowei broke it into three patterns. AI invents things when context is missing. AI contradicts itself when sources conflict. AI commits confidently to information that is no longer true.The model is rarely the bottleneck. The pipeline behind it is.When an agent reads account balance from one system, transaction velocity from another, and behavior signals from a third, each with its own lag, the model is reasoning from "a snapshot that never existed in the world."In fraud detection and credit underwriting, that fictional snapshot shows up on the P&L. 3: The design starts with what must be true at decision time. The intuitive approach is to wire together best-in-class components. A great stream processor. A great feature store. A great search engine. Each one correct in isolation. The composite system is not.Guarantees that hold inside one system erode the moment you cross to another.Xiaowei's team inverted the design. Start with what must be true at decision time. Build everything else on top of that contract.This is what first principles looks like below the waterline.4: Shared context compounds. Isolation does not."If a database gives you application shared state, context lake is going to give agents shared memory in a compounding system."Read that twice.Every decision an agent makes generates a signal worth keeping. A fraud pattern. A predictive signal. A route cause. In an isolated stack, that learning evaporates with the session. In a Context Lake, it becomes every other agent's capability instantly.The early movers don't just deploy infrastructure. They accumulate institutional knowledge inside it."The cost of waiting is not linear. Every month you wait, the gap grows."Early movers compound. Late movers start at zero.Humans + Machines. Never Humans vs. Machines.
Transcribe →"We Don't Do Tokenized Assets" Is the New "We Don't Do the Internet" | Jason Dobbs
Apr 1700:34:06Tap to summarizeThe promise of deploying autonomous AI agents at scale hinges on infrastructure and architectural truths. AI isn’t magic or a silver bullet. It most definitely isn’t one size fits all.What did it accomplish? What steps were taken to get there? What was the general knowledge vs more specialized, subject matter expertise required?What logic was introduced and how did it reason along the way?Jason Dobbs, Founder and CEO of ICTI, is building AI-enabled tokenized issuance for sovereign debt. He joined me on AI with Alec E30 and shared his truths using the fascinating vocabulary of a space I’m intrigued by. I was all ears.Two of Jason’s quotes are stuck in my head:“‘We don’t do tokenized assets.’ That’s the same as saying, ‘We don’t do the internet.’”“That’s a real big sledgehammer approach, when sometimes you just need a tiny little jewelry hammer going at something.”Below the waterline. Architecture and infrastructure is where it’s at.
Transcribe →The AI Questions CEOs Are Running Out of Time to Ask | Henrik Werdelin
Apr 900:44:02Tap to summarizeThere's a short list of people who shaped my AI journey before I even knew it was a journey. Henrik Werdelin is at the top of that list.We met in NYC in 2011. A few months before he launched BARK. I was the furthest thing from technical. He had a gazillion things going on. But he made time, answered every question and never made me feel like I was behind. Those conversations in NYC accelerated my learning curve in ways I'm still drawing from today. Bark now ships millions of dog toys a month. Henrik has since built Audos.com, an AI-native incubator that can launch a business in 15 minutes. He hasn't slowed down once. My hope with this episode is simple. What Henrik did for me back then, this conversation does for all of you now. A few things he said that I can't stop thinking about: 1️⃣ Most enterprise leaders are about to hit a wall they don't have language for yet. 2️⃣ AI agents are going to free up enormous human energy inside your organization. The question nobody is asking soon enough: what does your organizational design do with it? 3️⃣ The unlock isn't a better AI strategy. It's a harder question: what is your company's strategy in an age of AI? Humans + Machines. Never Humans vs. Machines.
Transcribe →75% of Your Creative Team's Week Is Wasted. Shane Hegde Built a Salesforce for Creatives to Fix It.
Mar 2700:38:56Tap to summarizeCreativity is magic. 75% of your creative team’s week is logistics, 25% is actually creating. What happens when you invert that?Shane Hegde has spent eight years fixing that problem and the implications for modern CMOs are staggering.Most CMOs have no visibility into what content their org is creating, where it’s going or how it performs. Shane drew the parallel to a CRO with no pipeline view, something unimaginable at this point.What Salesforce is to CROs, Air is to CMOs.Air is an operating system for your visual data. Images, videos, raw files, ad campaigns + brand assets. Organized, enriched, searchable and connected to performance data so your CMO knows what content exists, where it lives and what it’s doing.The 75/25 split? Air flips it.Air backs it up with six months of forward deployed engineers on their dime, not yours.My man Shane Hegde put his actual cell phone out there, multiple times, on my podcast.That’s what 15 years of friendship and 8 years of obsession looks like.He’s expecting your call. I guarantee he will get back to you. Quickly.Humans + Machines. Never Humans vs Machines.s/o Tyler Strand, Ping Ma, Ariel Rubin
Transcribe →Polsia: The AI That Doesn't Just Help You Build a Business. It Runs it. | Ben Cera
Mar 1500:42:22Tap to summarizeWould you believe me if I told you there’s an autonomous AI agent platform that enables anyone with a business idea to outsource to a swarm of AI agents to handle everything except what the founder wants to focus on?Would you believe me if I told you this company has seen its ARR run rate grow from $0 to $100K to $1.5M in a few weeks, with a trajectory that looks less like a hockey stick and more like an elevator shaft?Allow me to introduce you to Polsia and the founder + CEO, Ben Cera.There are 3 reasons Polsia and Ben are the focus of #10.1: Polsia epitomizes how AI is unleashing a sonic boom of entrepreneurial and human potential by dialing up our ability to “focus on making the beer taste better” instead of all the other important but tedious workEntrepreneurs spend 26-50% of their work week on administrative tasks. First time founders have an 18% success rate and the leading reasons startups fail are no market need (42%), running out of funding (29%) and the wrong team (23%) (link). There are 28.5 million solopreneurs in the US, 81% of all small businesses are solo, non-employer firms, yet less than 4% ever break $1M in revenue (link).Ben’s framing is simple and surgical: AI handles 80% of the operational grind. Humans focus on creativity, taste and direction. What used to be inaccessible infrastructure for a bootstrapped one-person company is now table stakes.2: Solo founder + AI stack is a new start-up archetypeOne person. No employees. No engineering team. $0 to $1.5M ARR. Ben never looked at the code. Polsia was built by AI and works in production.Team size as a proxy for ambition is becoming a thing of the past.We’ve moved from AI enabled promises to reality. The future arrived yesterday. 3: What’s the difference between the democratized AI-enabled infrastructure an Entrepreneur vs Intrapreneur has access to? Hint: nothingPolsia is enabling Entrepreneurs to capitalize on the AI Technical Overhang. No different than the way Intrapreneurs in established companies can lead tiger teams, departments or even entire companies by harnessing the technology.Remember the one word (“Goose”) Jack Dorsey left out of his memo (AIWA “The One Thing” #08)? Remember Anthropic’s AI labor market research describing “what AI is theoretically capable of doing versus what’s actually happening in the workplace” aka The Gap is the Game (AIWA “The One Thing” #09)?You know what I mean?Less strategy, “more hands in the dirt” doing.The 18-year-old with a great idea who couldn’t afford a team? They can now build like a funded company. That’s not disruption. That’s democratization.Never run from it. Run at it.
Transcribe →Greg Boone: AI Isn't an Intern, It's an Everything Machine | AI with Alec E26
Feb 400:43:24Tap to summarizeAI with Alec E26 with Greg Boone. 5 reasons to watch?1: Greg says the quiet part out loud2: The era of the lone “idea guy or gal” is over, now everyone can build3: You can’t mandate your way through AI transformation, never underestimate psychological safety4: AI isn’t an intern, it’s an everything machine, an “IDE for business people”5: AI native interns crush month long projects in days, are you enabling them aka “feeding the beast?” BIG s/o to Tracey Franklin at Moderna, leading the integration of technology + human + digital labor.
Transcribe →2 Weeks to 2 Days: How Calm's UX Team Tests 10X More Concepts with AI Prototyping | Chase Clark
Dec 1900:45:15Tap to summarizeThe way Chase Clark, Lead UX Researcher at Calm, demonstrates how AI is augmenting his workflows with real, tangible examples is outstanding. Personification of “hands in the dirt” use of AI that produces real, measurable results. The kind that all leaders want to generate.1: Time Compression at Scale: 2 hours → 5 minutes, 2 weeks → 2 daysNot just time saved, it’s all about accelerated learning.2: AI Prototyping: Previously impossible research is now possibleWhat happens when teams test 5-10 concepts in the time it took to test one? Innovation cycles compress. Risks get de-risked before production.3: AI Augmentation: Calibrating collaboration between Humans + Machines Never underestimate the importance of human taste. AI processes data. Humans are the strategic and creative layer.While a lot of people are debating AI strategy, Chase and his team are shipping real results.s / o Lovable, Supabase
Transcribe →Good Pirates Ship Fast: L'Oreal's AI-First in 90 Days (May '24), Accelerating Since | Thomas Menard
Dec 900:47:11Tap to summarizeWant to see AI-first at scale? Check out Thomas Menard and the rockstar team at L’Oreal showing us how it’s done.Embracing AI to drive “idea to impact” in less than 90 days for a 100,000 person enterprise is impressive.To do it in May 2024 is outrageous. My man Thomas Menard is a “good pirate” who personifies the “ship fast, learn, iterate” approach. No analysis paralysis. No tech for tech’s sake. No science projects. Just a ruthless commitment to augmenting people to create business value.Intelligent systems that abstract complexity so everybody, engineers and non-engineers, can create value without becoming AI experts.What’s next?From Gen-AI-as-a-Service to a proper Agentic AI platform. Realizing the dual benefits of AI infrastructure while balancing the buy vs build equation accordingly.Think context engineering, AI-first architecture, platform design and continuous improvement.1% better every day. This is the way.s/o Harrison Chase, LangChain + GCP
Transcribe →Banks Have 2-3 Years Before AI Leaders Pull Away, Forever. AIWA E23 w/Co-CEO of Evident.
Nov 2600:46:09Tap to summarizeIf you know who Evident is, you understand why I was so stoked to interview co-founder / co-CEO Alexandra Mousavizadeh. If you don’t know Evident, let’s fix that.Evident is all about data and benchmarking the embrace of AI across Financial Services. And you know what they say about data driven conversations? They’re super objective aka the best.1: AI doesn’t remove thinking, AI allows you to “think about dimensions you’ve never been able to draw in before”, it raises the floor and ceiling of your work2: Banks leaning hardest into AI are the ones hiring while those avoiding it are doing the opposite3: Moving away from point solutions and “getting to platform” is the race you want to run4: Banks all in on operationalizing AI are “moving at triple, quadruple speed” and we’re “two to three years out” from a tipping point where “growth is exponential”5: CEO and leadership conviction that this is “absolutely existential” and acting on it is consistent across companies leading the packWatch this interview and I guarantee my enthusiasm for Evident will be transferred to you. Not the most “objective” statement but you know, I love data…Want to know which banks are winning with AI, backed by data, not hype? Link to the “Evident 2025 AI Banking Index”: https://evidentinsights.com/ai-index/A must read.
Transcribe →Bret Greenstein: 'If You Can't Imagine It, You Can't Make It'-Science Projects to AI Transformation
Oct 1000:33:09Tap to summarizeIs the failure rate for enterprise AI POCs super high because they’re “science projects” solving a problem that’s already been solved?Guest Bret Greenstein, Chief AI Officer of West Monroe, dropping all sorts of knowledge in AI with Alec podcast E22:“We already know that AI can do this work. Proving value is actually sort of a cop-out.”“It's much more phases than POCs.”“A lot of POCs were done by technical people who proved it could be done with no line of sight to the implementation and adoption.”“And the people who actually know how work is done are not sitting in an IT center.” “They're near where the work is.”“So the best projects, and I've seen hundreds of them, are when the business is involved with technology and the leadership understands what they're trying to accomplish.” “Otherwise it's just a science project.”“And there's a lot of science projects or people pushing for a license for a thing that's going to magically change your life. It's like people selling a pill to make you live longer.”If that didn't convince you to watch the episode, here are 5 more:1️⃣ Humans in the loop or on top of the loop. They’re your decision maker. Always have been and always will be.2️⃣ Operationalizing AI-first ambitions? “You can't have leaders who don't understand it. Now that being said, I don't think they have to be data scientists, but they do have to understand the nature of it.”3️⃣ “If you can’t imagine it, you can’t make it.”4️⃣ Evolution from Prompt engineering to Context engineering because “knowing which data matters is really useful” and “providing more data that’s relevant” is critical.5️⃣ In the enterprise AI era, buy vs build vs partner? “Look for no regret moves” as the “world is literally forming around us” aka make investments that become more valuable not less valuable as things change.Chapters + Timestamps:00:00 - Introduction00:26 - 100K Employees Doing 300K Work: The AI-Native Vision02:58 - Internal Audit Case Study: 50% Workload Reduction06:02 - From Fear to AI Whisperer: Building Soft AI Skills08:24 - Why Your Data Doesn't Need to Be Clean (Yet)13:04 - The Death of Application-Centric Architecture16:27 - Chat Interface vs Beautiful UI: What Users Actually Want19:01 - The No Regrets Move Strategy for AI Investment23:30 - Forward Deployed Engineers vs Business Transformation25:56 - The POC Trap: Why "Science Projects" Fail27:35 - Leaders Don't Need to Be Data Scientists (But...)30:02 - Electricity Metaphor: Reimagining Your Business30:50 - "If You Can't Imagine It, You Can't Make It"33:05 - Closing
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