Built This Week

This week we sat down with Jim Olsen, co-founder and CTO of ModelOp, to explore what it takes to get AI into production inside Fortune 500 companies. Building a model is only part of the job. Knowing where it runs, what it costs, who owns it, and whether it meets company policies is where things get complicated.

Jim walks us through a live demo of ModelOp, showing how it discovers unregistered enterprise AI, brings it into a central inventory, and uses agents to fill out documentation and flag compliance gaps. His team is turning work that can take months into a process that takes minutes, with humans still in the loop.

We also covered why AI spending needs to be measured by business use case, whether an AI-powered pet feeder solves a real problem, and how Jim runs engineering from an off-grid home at 10,200 feet in the Colorado mountains.

We cover:
• What ModelOp does and why all its clients are Fortune 500 companies
• Why enterprise AI projects get stuck between prototypes and production
• Tracking token usage and costs by business use case
• Shadow AI beyond employees using chatbots
• A live demo of discovering and registering enterprise AI
• Using AI agents to complete forms and identify compliance gaps
• Why model inventories matter when providers change or models retire
• The real-world problems that led Jim to co-found ModelOp
• Petlibro’s AI-powered feeder and whether everything needs AI
• Running an engineering team with solar power and Starlink from an off-grid home

If you are building with AI, managing enterprise AI deployments, or trying to understand how governance can accelerate delivery, this episode is for you.

Find Jim and ModelOp:
Website: https://www.modelop.com

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS
[00:00] Intro
[00:41] Meet Jim Olsen, co-founder and CTO of ModelOp
[01:49] What ModelOp does: enterprise AI governance explained
[03:15] Tracking AI costs by business use case
[04:50] Demo: token usage and cost dashboards
[05:41] Automating governance and compliance workflows
[06:08] Discovering shadow AI across enterprise platforms
[08:49] Bringing a healthcare chatbot into the inventory
[09:56] AI agents automate documentation and forms
[11:20] Checking policies and flagging compliance gaps
[12:05] From months of work to minutes
[12:27] The real-world problems behind ModelOp
[14:04] Tracking model dependencies and managing changes
[15:23] Where to find ModelOp
[15:43] News: Petlibro’s AI-powered pet feeder
[16:11] Does everything really need AI?
[17:20] Automatic feeders, robots, and everyday chores
[18:23] Jim’s off-grid setup in the Colorado mountains
[19:09] Solar panels, batteries, and working remotely
[20:12] Wrap-up

#AI #ModelOp #AIGovernance #EnterpriseAI #ShadowAI #AIAgents #Fortune500 #BuildWithAI #BuiltThisWeek

This week we sat down with Rob Williams, co-founder and CTO of Read AI, to dig into what 5 million monthly active users actually looks like at the product and infrastructure level. Most people know Read AI as a meeting note taker. Rob winces every time he hears that. What it really does is capture every conversation across your organization and turn it into intelligence — daily briefings, smart routing, and organizational context that moves itself to where it needs to be.We also covered AI companion apps and the unit economics behind them, the AI adoption divide inside enterprise companies, and Opus 5.5 dropping this week.

We cover:

• What Read AI actually is beyond the meeting note taker label
• The daily briefing feature — hot and hydrated context before every meeting
• How smart routing sends meeting data automatically to the right team
• Why some companies set data retention to 7 days and why that makes sense
• Navigating the AI adoption divide inside enterprise organizations
• Rob's vision for information moving itself in three years
• Why meetings are the biggest investment most companies make and nobody measures
• Hot Girl Hotline — an AI relationship advice app and why the unit economics work
• AI as a sounding board vs a solver and why both have value
• Opus 5.5 dropping and what cheaper intelligence means for builders
• Token economics of running 5 million monthly active users

If you are building with AI, managing a team, or want to understand where enterprise AI is actually heading, this episode is for you.

Find Rob and Read AI:

Website: readai.com

Email: [email protected]

LinkedIn: [LINKEDIN LINK — to be added]

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:06] Meet Rob — co-founder and CTO of Read AI

[01:42] What Read AI actually is beyond meeting notes

[02:12] Meetings as the biggest investment companies make

[02:33] The daily briefing feature — live demo

[03:47] Smart routing — data flows where it needs to go

[04:57] Unusual use cases and the AI adoption divide inside companies

[06:19] 7-day data retention and what it says about fast-moving companies

[06:40] 5 million monthly active users and how they got there

[07:00] Token economics at scale

[10:00] The future — information that moves itself

[15:34] What supersedes old context — the data freshness problem

[15:55] News — Hot Girl Hotline, AI relationship advice

[17:09] Is AI as a companion dangerous or valuable

[18:14] AI memory and the sounding board use case

[19:37] Unit economics of AI companion products

[20:13] Sam uses AI for big personal decisions

[21:32] Where to find Rob

[21:50] Wrap up

#AI #ReadAI #Meetings #Enterprise #FutureOfWork #BuildWithAI #BuiltThisWeek #Productivity #Anthropic #AICompanion

He Ran a Rocket Launch Company. Now He Is Building Autocomplete for Hardware Design.

This week we sat down with Sohrab Haghighat, CEO and co-founder of Hestus, to demo the AI layer that sits on top of your existing CAD software and autocompletes your next design move. Rob spent years as an aerospace engineer, ran a rocket launch company, and built Hestus to eliminate the manual clicks that slow hardware engineers down and introduce errors into designs.We also covered Anthropic 5.5, GPT-6, and a new Grok model all dropping in the same week — and why intelligence getting cheaper is good news for everyone building right now.

We cover:

• What Hestus is and how AI autocomplete works inside CAD software
• Live demo — AI suggesting the next design step before you click
• Why Hestus plugs into your existing CAD tool instead of replacing it
• How Hestus learns your preferences within a design session
• Who the target customer is — mechanical, aerospace, and robotics engineers
• The gap between a flashy 3D model and one that is actually ready for manufacturing
• Rob's long term vision — doing for hardware what Claude Code did for software
• Anthropic 5.5, GPT-6, and Grok all dropping the same week — intelligence is getting cheaper
• Why LLM improvements do not directly translate to improvements in hardware AI

If you are building hardware, working in engineering, or want to understand where AI is going beyond software, this episode is for you.

Find Rob and Hestus:

Website: hestus.co

LinkedIn: linkedin.com/in/sohrabhaghighat

Twitter: x.com/s_haghigh

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:12] Meet Rob — CEO and co-founder of Hestus

[01:33] Aerospace engineer, rocket launch company, and the pain that led to Hestus

[02:29] Live demo — AI autocomplete inside CAD software

[03:50] How Hestus sits on top of your existing CAD tool

[04:16] Meeting engineers where they are — no new workflow

[05:02] How Hestus learns your preferences within a session

[06:32] Available on Autodesk Fusion now, SolidWorks coming soon

[07:20] Who is using Hestus — mechanical, aerospace, robotics engineers

[08:30] Bottom-up approach — sketch to 3D to manufacturing readiness

[10:25] Long term vision — Claude Code for hardware design

[11:15] The gap between a flashy model and one ready for manufacturing

[12:00] News — Anthropic 5.5, GPT-6, and Grok released same week

[13:18] Jordan tests the new Opus — cheaper and competitive with Fable

[14:41] Intelligence is getting cheaper — what that means for builders

[15:06] Why LLM improvements do not directly improve Hestus

[16:08] Where to find Rob and Hestus

[16:31] Wrap up

#AI #CAD #HardwareDesign #Engineering #Hestus #BuildWithAI #BuiltThisWeek #Anthropic #OpenAI #Aerospace

This week we sat down with Jennifer Dulski, CEO and founder of Rising Team, to demo the AI leadership platform that acts as a personal trainer for your effectiveness at work. Jen spent 10 years at Yahoo, then Google, then Facebook, ran Change.org for five years, and sold her first AI company to Google in 2009. That company now powers the review insights inside Google Maps. She also teaches at Stanford GSB.Rising Team's AI coach RT does not give generic leadership advice. It knows your team. It knows that Tom likes direct feedback and Debbie needs encouragement first. And it helps you practice the conversation before you have it.We also got into why Apple Intelligence took too long, why AI memory still feels creepy to some people, and why the best AI features are the ones you never notice.

We cover:

• What Rising Team is and how the AI coach RT works
• Why 70 percent of AI transformations fail because of people not technology
• How RT learns your team individually and personalizes every conversation
• Why every executive gets a coach and every frontline manager gets nothing
• Why the engagement survey is dying and what is replacing it in real time
• The privacy guardrails built into RT and EU AI Act compliance
• Jen's first AI company in 2009 — DealMap, sold to Google, now inside Google Maps
• Why Apple Intelligence took so long and whether it matters anymore
• The AI memory debate — helpful or creepy
• Why the best AI features are the ones you never have to turn on

If you are building with AI, managing a team, or want to understand where workplace AI is heading, this episode is for you.

Find Jen and Rising Team:

Website: risingteam.com

LinkedIn: linkedin.com/in/jdulski

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:11] Meet Jen — CEO and founder of Rising Team

[01:44] From high school teacher to Yahoo, Google, Facebook, and Stanford GSB

[02:27] What Rising Team is at the highest level

[02:47] Why coaching never scaled past the executive level

[03:40] How Rising Team started in 2020

[04:03] DealMap — her first AI company, sold to Google, now inside Google Maps

[05:18] How Rising Team evolved from team building software to AI coach

[05:55] How RT knows your team individually

[06:31] Live demo — RT in action

[07:00] Why 70 percent of AI transformations fail because of people

[07:24] The science of high performing teams has not changed

[08:12] What a session with RT actually looks like

[10:00] Real-time anonymized insights for executives

[14:21] Why the engagement survey is dying

[14:48] Real insights from enterprise customers

[15:36] Privacy guardrails and EU AI Act compliance

[16:43] News — Apple Intelligence, Siri, and why it took too long

[18:28] The AI memory debate

[20:06] The best AI features are the ones you never notice

[21:00] Where to find Jen and Rising Team

[21:42] Wrap up

#AI #Leadership #RisingTeam #FutureOfWork #BuildWithAI #BuiltThisWeek #TeamManagement #AICoach #Apple #HR

This week we sat down with David Petrou, CEO of Continua, to demo Wheely — an agent development operating system built for developers running multiple AI coding agents who are hitting the wall of mental overhead. David spent 18 years at Google, was on the founding teams of Google Glass and Google Goggles, and left when ChatGPT launched. We also covered the foldable iPhone, Android vs Apple for AI agents, Fable 5.1, Anthropic vs OpenAI on developer experience, and whether new programming languages designed for agents are coming.

We cover:

• What Wheely is and why David calls it an agent development operating system
• 18 years at Google, Google Glass, Google Goggles, and why he left to start a company
• The pivot from social AI to building the OS layer for multi-agent coding
• Why token costs dropping does not solve the human attention problem
• How Wheely decomposes problems, allocates agents, and knows when to interrupt you
• Why Git and GitHub were designed for humans and need to be rethought for agents
• How agent coding can modernize legacy code and rebuild bloated React frontends
• Who Wheely is actually built for and why the middle space is the opportunity
• Why new programming languages designed specifically for agents could unlock major efficiency gains
• Apple vs Android for AI — best hardware vs best OS for agent use cases
• How Android can disintermediate apps by running them simultaneously off screen
• Fable 5.1, Gemini 3.8 Flash, and the honest developer take on Anthropic vs OpenAI
• The coevolution of models, harnesses, and programming languages
• Whether the foldable iPhone matters if agent coding eliminates the need for extra screen real estate

If you are building with AI agents, working in dev tools, or want to understand where the agent coding stack is heading, this episode is for you.

Find David and Wheely:

Website: wheelie.dev

Twitter: x.com/dpetrou

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:07] Meet David — CEO of Continua and creator of Wheely

[01:56] Google Glass, Google Goggles, 18 years at Google

[02:40] Why he left Big Tech when ChatGPT launched

[05:18] The previous product — social AI for group chats

[06:36] When coding agents changed everything

[08:03] How building for agents internally became the real opportunity

[08:56] Who Wheely is built for

[09:46] What happens when you run 4 agents at once

[10:17] Human attention is the scarcest resource in agent coding

[11:16] How Wheely works — decompose, allocate, optimize, interrupt only when needed

[13:05] The space between Claude Code and Lovable

[14:05] Modernizing legacy code and greenfield agent projects

[15:18] Why React is bloated and agents can rebuild frontends better

[16:11] News — Apple foldable iPhone coming Q3

[17:29] Apple unified memory vs Android open OS for AI agents

[18:27] Android disintermediating apps with off-screen agent execution

[20:24] Will the foldable iPhone matter if agents take over development

[20:48] Fable 5.1 launch — honest developer take

[22:10] Anthropic vs OpenAI on developer experience and harness access

[23:17] New programming languages designed for agents

[25:19] The coevolution of models, harnesses, and programming languages

[25:40] Where to find David and Wheely

[25:55] Wrap up

#AI #AIAgents #Wheely #DevTools #GoogleGlass #BuildWithAI #BuiltThisWeek #Anthropic #Fable #Apple #iPhone

This week we sat down with Hardik Kabaria, co-founder and CEO of Vinci, to demo the AI platform making physics simulation accessible to any engineer building hardware. A chip tape-out used to cost millions of dollars and take months. Thermal analysis that should take seconds was waiting days for a specialist with a PhD. Vinci is compressing all of that into minutes — and making it accessible to anyone on the design team, not just the simulation expert.We also got into why AI image and video generation still has not produced a Netflix movie, what has to change before it does, and how AI getting into hardware design raises the stakes around IP and watermarking in ways nobody is talking about yet.

We cover:

What Vinci does — making physics simulation accessible to anyone building hardware

Why a chip tape-out costs millions and takes months and how AI is compressing that cycle

How Vinci lets engineers get thermal and physics answers in seconds instead of days

The existing workflow — designers, performance evaluators, and manufacturers rarely even work in the same team

How simulation software went from requiring PhDs to something any engineer can access

The digital twin concept — running physics experiments in software before touching physical prototypes

Why the semiconductor and data center cooling market is the starting point

News — AI image and video generation still has not crossed the quality schism

Why there are no AI-generated movies on Netflix yet even though the models exist

How AI getting into hardware design changes the stakes around IP and watermarking

The parallel between language models democratizing reasoning and physics models democratizing simulation

If you are building hardware, working in semiconductors, or want to understand where AI is going beyond software, this episode is for you.

Find Hardik:

LinkedIn: linkedin.com/in/hardikkabaria

Twitter: x.com/hardikk13

Find Vinci:

Website: getvinci.ai

LinkedIn: linkedin.com/company/vinciphysics

Company Twitter: x.com/VinciPhysics

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:14] Meet Hardik — co-founder and CEO of Vinci

[02:06] What Vinci does — making physics accessible for hardware builders

[03:24] How simulation has worked in hardware for decades

[04:33] The digital and physical world of experiments

[05:47] Getting physics answers in seconds instead of days

[06:28] How Vinci fits into the existing hardware design workflow

[08:10] The tape-out problem — millions of dollars and months per iteration

[10:00] Live demo — thermal simulation for semiconductor design

[13:00] Who is using Vinci and what problems they are solving

[15:30] The language model analogy — democratizing physics reasoning

[17:00] News — AI image generation and the quality schism

[19:30] Why there are no AI movies on Netflix yet

[21:00] AI getting into hardware and the IP watermarking problem

[23:50] The regulations and guardrails that will follow

[26:05] Where to find Hardik and Vinci

[26:43] Wrap up

#AI #Physics #Semiconductors #HardwareDesign #Vinci #ChipDesign #BuildWithAI #BuiltThisWeek #AIHardware #Simulation

This week we sit down with Zach Rattner, CTO and co-founder of Yembo, to demo the computer vision platform that turns a ten minute phone walkthrough into a full home inventory, 3D model, and floor plan. Deployed in 35 countries, running thousands of inspections every day for moving companies and property insurers — and the end user never even knows AI is involved. We also dig into Claude Fable 5 being pulled without coding access, the 25,000 Chinese accounts exploiting the model, and whether AI has finally entered its regulatory adolescence.

We cover:

• What Yembo does — computer vision that deeply understands the interior of homes
• How a ten minute phone video replaces a 60 to 90 minute in-home inspection
• The visual inventory — color coded, numbered, time stamped photos of every item
• Why the real AI opportunity is doing what was previously impossible, not just faster
• How Yembo embedded AI in a workflow where the end user never knows it exists
• The insurance use case — 3D model and floor plan from a single video walkthrough
• Why 30 percent of their patents are on guiding the camera capture without any training
• Claude Fable 5 pulled and reinstated without coding access — what it means for developers
• AI reaching regulatory adolescence and whether the framework arrives in time
• 25,000 Chinese accounts exploiting the model and the KYC failure behind it

If you are building with AI, working in insurance or logistics, or want an honest take on what happens when regulators start touching frontier models, this episode is for you.

Find Zach and Yembo:

Website: yembo.ai

LinkedIn: search Zach Rattner

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:15] Meet Zach — CTO and co-founder of Yembo

[02:05] What Yembo does — computer vision for home interiors

[03:30] How a ten minute phone video replaces 90 minutes of in-home inspection

[05:10] Live demo — the visual inventory report

[07:00] The real AI opportunity — doing what was previously impossible

[09:30] Embedding AI so the end user never knows it exists

[11:00] Insurance use case — 3D model and floor plan from video

[13:00] 30 percent of patents on guiding camera capture without a tutorial

[14:30] News — Claude Fable 5 pulled and reinstated without coding

[16:20] AI adolescence — the regulation debate

[18:30] 25,000 Chinese accounts and the KYC problem

[20:30] Where to find Zach and Yembo

[21:00] Wrap up

#AI #ComputerVision #Yembo #Insurance #MovingIndustry #ClaudeFable #BuildWithAI #BuiltThisWeek #PropTech #AIRegulation

This week we sit down with Ryan Aytay, President and COO of Code Metal, to dig into one of the most underestimated problems in tech — the legacy code running mission critical infrastructure across aerospace, defense, automotive, and medical devices. Code Metal translates that code to any hardware with mathematical proof it will behave identically. Provably correct. We also dig into Meta glasses, Jony Ive at OpenAI, and where AI hardware interfaces are really heading.

We cover:

• What Code Metal does and why provably correct code matters
• The legacy code problem running mission critical infrastructure right now
• Why testing is not enough and what formal methods actually prove
• How AI finally made formal verification scalable after 50 years
• The Tesla over-the-air update analogy for code-to-hardware deployment
• Which industries Code Metal is built for and why
• Why vibe coding works for apps but terrifies Ryan for aerospace and defense
• What brought Ryan from running Tableau at Salesforce to a code verification startup
• Meta Ray-Ban glasses — price drop, Kylie Jenner, and the veteran use case
• Jony Ive at OpenAI and what the mystery hardware device might be
• Why every AI interface is converging on the same form factor

If you build software, work in hardware, or want to understand the infrastructure layer AI is about to touch, this episode is for you.

Find Ryan and Code Metal:

Website: codemetal.ai

LinkedIn: linkedin.com/in/ryanaytay

Company LinkedIn: linkedin.com/company/code-metal

Twitter: x.com/Code_Metal_AI

TIMESTAMPS

[00:00] Intro

[01:04] Meet Ryan — President and COO of Code Metal

[02:00] What Code Metal does in one sentence

[03:30] The legacy code problem in mission critical systems

[05:32] What provably correct means and why testing is not enough

[07:10] Formal methods — mathematical proof not just testing

[08:45] How AI made formal verification scalable

[10:20] Tesla over-the-air updates analogy

[11:40] ICP — aerospace, defense, automotive, medical

[13:05] Why vibe coding terrifies Ryan for mission critical systems

[15:10] From Salesforce and Tableau to Code Metal — the origin story

[17:30] News — Meta Ray-Ban glasses price drop and Kylie Jenner

[19:45] Meta glasses given to veterans — the real use case

[20:50] Jony Ive at OpenAI and the mystery hardware device

[22:30] Why all AI interfaces are converging on glasses

[23:05] Where to find Ryan and Code Metal

[23:45] Wrap up

New episodes every Friday at BuiltThisWeek.com

#AI #CodeMetal #FormalMethods #LegacyCode #Engineering #Defense #Aerospace #BuildWithAI #BuiltThisWeek #AIHardware

This week on Built This Week we sit down with Jackson, co-founder of Realm AI, to demo the platform replacing months of manual due diligence with AI agents that find, compile, and cite every piece of data on any commercial property in the US in minutes. We also go deep on the SpaceX IPO, what the Cursor acquisition means for the Elon portfolio, and whether the finance textbooks are just wrong.

We cover:

• What Realm AI is and how it works for commercial real estate due diligence
• A live demo of the self-serve platform — any property in the US in minutes
• How dozens of AI agents communicate and delegate to find property data
• Why Realm deliberately refuses to make investment recommendations
• The before and after — printed spreadsheets and button calculators vs AI agents
• How small firms with three analysts are beating 100-analyst firms to deals
• Who is actually using Realm AI and what surprised them about the ICP
• SpaceX IPO — is the valuation frothy or are the finance textbooks just wrong
• 167 launches in 2025, one every 2.2 days, and no real competition
• The Cursor acquisition confirmed and what it means for the SpaceX portfolio
• How Tesla, SpaceX, xAI, and Cursor all connect into one bigger vision
• What it actually feels like to watch a SpaceX launch in person

If you are in real estate, building with AI agents, or want a fresh take on the SpaceX IPO from people who actually use the product, this episode is for you.

New episodes every Friday at BuiltThisWeek.com

TIMESTAMPS

[00:00] Intro

[01:04] Meet Jackson — co-founder of Realm AI

[02:03] What Realm AI does and the problem it solves

[03:19] Live demo — researching any US property in minutes

[05:10] How the waterfall of data agents works

[07:20] Why Realm refuses to make property recommendations

[09:08] Before vs after — the printed spreadsheet era

[11:27] Who is getting the most value — small firms vs big funds

[14:43] ICP and asset type diversity

[15:40] News — SpaceX IPO valuation above Amazon

[16:41] Jackson's take — throw away the finance textbooks

[17:40] The SpaceX monopoly on launches — 167 trips in 2025

[19:55] Jordan's bull case for the full Elon portfolio

[21:12] Cursor acquisition confirmed

[22:11] Watching a SpaceX launch in person

[23:15] Where to find Jackson and Realm AI

[23:46] Wrap up

#AI #RealEstate #PropTech #AIAgents #SpaceX #Cursor #BuildWithAI #BuiltThisWeek #CommercialRealEstate

This week on Built This Week we sit down with Emanuele Melis, who leads Field Engineering at AI One, to demo how enterprises are deploying AI agents right now without needing a massive data transformation first. We also go deep on Claude 4 Fable — first impressions, token spend chaos, the '100x the design' prompt, and what the Jevons paradox means for AI consumption.

We cover:

• Emmanuel builds a physics puzzle game from memory using Claude in 10 minutes
• Jordan's first 36 hours with Claude 4 Fable — slow, expensive, and world class
• What happens when you type '100x the design' into Fable
• Why nobody knows how many tokens they are burning and why that needs to change
• What AI One and Context One actually do for enterprise
• The autonomous ladder — how to deploy agents safely without giving them full control
• The sweet spot for AI agents: high complexity, low judgment work
• How a financial firm cut a week-long investigation down to hours using agents
• Why 150,000 brittle rules are being replaced by a single agent
• Making custom coloring books for kids with AI every day
• Jevons paradox — cheaper tokens, more consumption, no ceiling
• A PM had Claude Code listen to a live engineering meeting and build in real time
• SpaceX IPO — the bull case, the wait and see case, and who is right

If you are building with AI, deploying agents in enterprise, or just want an honest take on Claude 4 Fable, this episode is for you.

Find Emanuele and AI One:

Website: ai.one

LinkedIn: linkedin.com/in/emanuele-melis

Company LinkedIn: linkedin.com/company/ai-one-1

TIMESTAMPS

[00:00] Intro

[01:33] Emmanuel builds a physics game with Claude in 10 minutes

[03:30] Jordan's first take on Claude 4 Fable

[04:35] Token spend confusion and the slider nobody understands

[05:17] '100x the design' — what happened

[06:37] Fable in five — Jordan's verdict

[07:32] What AI One actually does — the context control layer

[09:03] The autonomous ladder — starting agents at zero

[10:12] High complexity low judgment — where agents win

[11:40] Financial investigation use case — from one week to hours

[13:25] 150,000 rules replaced by one agent

[14:03] Coloring books for kids — the fun use case

[15:07] News — Claude 4 Fable launch and what it means

[15:55] Jevons paradox — cheaper tokens, more consumption

[16:42] How fast AI has evolved in 18 months

[17:17] Claude Code listening to live engineering meetings

[18:49] SpaceX IPO — bull case vs wait and see

[22:17] Where to find Emmanuel and AI One

[22:50] Wrap up

New episodes every Friday at BuiltThisWeek.com

#Claude #Fable #AI #BuildWithAI #Enterprise #AIAgents #SpaceX #BuiltThisWeek #AIOne #ContextOne