The AI Cookbook Show by Malcolm Werchota

#123 - Prompt Engineering 2.0 — Why 90% of Your AI Bill Is Garbage

Remember your first roaming bill shock? Two weeks in Dubai, you come home, and suddenly you're staring at a 1,000-euro phone bill instead of the usual 30. Same phone. Same behavior. Completely different billing model. That's exactly what's happening to every company in the world right now. Your CTOs are sitting at the kitchen table thinking: "We pay 30 dollars a month for Copilot licenses." And then someone quietly opens the API invoice. It's not 30 dollars. It's 1,500. Per employee. Per month. Andrej Karpathy — OpenAI co-founder, ex-Tesla AI chief — just put it bluntly in a recent post:  "90% of your AI bill is for context you never actually need."  Imagine you're building a house for 100,000 dollars. The contractor says: "Malcolm, that'll be 1 million." — "Why 10× more?" — "Well, the context..." That's what your company is doing with every single AI query. 📚 How we got here * 2022-2023: Prompt Engineering. Salaries 200,000-500,000 dollars. "Please and thank you," "think step by step," Chain of Thought. Some of it still works today. * 2024: The "Prompt Engineer" job title disappears. Karpathy introduces Context Engineering — the delicate art of giving the AI the right information in the right context window. * 2026: We now need Prompt Engineering 2.0 — not for better answers, but for answers that are 10× cheaper. 🔧 Eight measurable token levers nobody in mid-market uses * Chunking — split large documents into semantic chunks instead of burning 100 PDFs in one query * Grab-before-Fetch — tell the AI exactly which book to pull from the library instead of letting it read 100 * Prompt Caching — with stable prefix instructions, you pay only 10% (Anthropic). First cache write costs 90%, every reuse 10%. On a 17-page compliance brief = massive lever. * Skill.MD / Agent.MD — work instructions for the AI. Karpathy did the math: without Skill.MD = 4 dollars per session. With Skill.MD = 30 cents. Factor 13. * Compaction — manually compact long sessions yourself, don't wait for the AI to do it. Works in Claude Code, Codex, etc. * Model Routing — Haiku $5/1M tokens (classification, formatting), Sonnet $15 (code review), Opus $25+ (architecture). Don't drive the Bugatti to the grocery store. * Change your default model — your devs have the most expensive model set as default. Sonnet is enough in 85% of cases. * Auto-Context-Loading + Prompt-Audits by a second AI = automatic context-bloat killer 🚦 The electricity-bill analogy for your board Private life: 20-dollar lightbulb. If you leave it on 24 hours, it doesn't matter. Electric bill 800 or 850 — who cares. Now scale it up: factory floor. 50,000 lights. Three-shift operation. Plus machines, server room. Suddenly 5 million dollars in electricity. That's your AI bill in 2026. You spent two years buying AI without installing the meter. If I walk in as a consultant and say "1-million-dollar project to optimize your prompts" — and you go from 5 million to 500,000? That's factor 10. From 4 million in savings, I'd happily take 1 million. 📟 Cloud-Meter — the physical electricity meter for your AI Someone built a small cube with a touchscreen that displays in real time how much money he's burning on tokens. Sits on the desk next to the laptop. GitHub repo, viral on TikTok. A human built a literal power meter for AI because he can't grasp how much he's spending in the abstract. 🎯 Three Monday actions * 1. Subscription Audit: Claude Code + Codex + Cursor + Lovable Pro + ChatGPT Plus + Gemini all running in parallel? Have an AI list every duplicate spend. At werchota.ai we save thousands monthly by subscribing fast and canceling fast. * 2. Build Skill.MDs: The moment you do a process twice, write a Skill.MD. We have a GitHub Skill Repository at werchota — every skill = better quality + 13× fewer tokens. * 3. Change the default model: Open Claude / Codex / Cursor, switch the default model to Sonnet (or smaller). You'll hit "max out" less often — and you can work much longer per session. 💬 The question every board needs to answer "How much does one token cost us?" Your CFO knows the electricity bill. Knows the gold price. Knows the price of gasoline. Knows the price of milk at the supermarket. They don't know the token price. And they don't yet know they should know it. That's the new language we have to learn. AI-language. First mover wins. ⏱️ Timestamps * 00:00 — Cold open: The 1,000-dollar Dubai roaming bill * 03:30 — Two worlds: private flat-rate vs. enterprise API * 06:00 — Karpathy: 90% of your AI bill is wasted context * 08:30 — Retro: Prompt Engineering 2022 → Context Engineering 2024 → Prompt Engineering 2.0 * 13:00 — Chunking + Grab-before-Fetch * 16:00 — Prompt Caching: 10% instead of 100% * 19:00 — Skill.MD / Agent.MD — Factor 13 * 22:00 — Compaction * 25:00 — Electricity bill analogy: 5M in token costs with no meter * 28:00 — Cloud-Meter — the physical token meter * 30:00 — Model Routing: Haiku / Sonnet / Opus — Skoda, Ferrari, Bugatti * 33:00 — Three Monday actions: Subscription Audit, Skill.MDs, Default Model * 37:00 — The question for every board: "How much does one token cost us?" 🎙️ About the Host Malcolm Werchota runs AI adoption programs for companies across Europe. After 15+ years at Novartis and Schlumberger, today's focus: AI without the bullshit. Lecturer at ESADE and HSLU. Studied in Leoben. 🚀 Resources for Executives * 📚 Chief AI Academy — AI for Decision Makers [https://www.werchota.ai/chief-ai-academy] * 👥 AI Leadership Community [https://chief.werchota.ai/getting-started] * 🌐 werchota.ai [https://www.werchota.ai] 📬 Contact * LinkedIn: linkedin.com/in/malcolmwerchota [https://linkedin.com/in/malcolmwerchota] * Email: malcolm@werchota.ai [malcolm@werchota.ai] 📰 Sources * Andrej Karpathy — recent X/Twitter post on Context Engineering & Skill.MD factor 13 * Anthropic — Prompt Caching Pricing (10/90 split) * Anthropic — Model pricing Haiku / Sonnet 4.6 / Opus 4.7 * GitHub — Cloud-Meter open-source project (viral on TikTok) * Werchota.ai — internal Skill Repository & Subscription Audit workflow Tags: #PromptEngineering #ContextEngineering #Karpathy #Anthropic #Claude #ClaudeCode #Codex #Tokens #AICost #PromptCaching #SkillMD #ModelRouting #CFO #CTO #werchota #ChiefAIAcademy #TheAICookbookShow

#122 - AI Drama — Sierra, Brett Taylor, and the Biggest Conflict of Interest in AI

Welcome to AI Drama. A story in two cities, with one villain, a 16-billion-dollar valuation, and one of the biggest conflicts of interest in the entire AI world. Manila, May 5, 2026. A man named Ivan — a "quality analyst" at one of the world's largest BPO companies — sits across from reporters and says one sentence that should make all of us shiver: "I actually helped improve the work of an AI, and now AI replaced my job." Same day, 12,000 kilometers away in San Francisco, a press release drops: $950 million fresh funding. Valuation: $16 billion. Investors: Tiger Global, Google Ventures, Sequoia, Benchmark. The company: Sierra. And 99% of humanity has never heard of them. Even though 40% of every Fortune 50 company runs Sierra agents. One of the three largest banks in the world. Weight Watchers, Cigna, Blue Cross, Rocket Mortgage, Sonos. 95% of US Black Friday shoppers last year had a conversation with a Sierra agent — and never knew it. 📈 The numbers that don't add up * Valuation 9 months ago: $10B → today $16B (+60%) * ARR end of 2024: $25M → February: $100M → today: $150M (6× in 18 months) * Valuation multiple: 105× revenue. SaaS norm is 5-10×. This isn't a valuation anymore — it's an evangelistic belief. Lead VC Peter Fenton from Benchmark: "Sierra is by all measures the winner in the customer experience category." 🎭 Who is Brett Taylor? Go check him out — he should be as famous as Sam Altman or Mark Zuckerberg. He's not. * Co-invented Google Maps * CTO at Facebook * Chairman of the board at Twitter — sat at the table during Elon's takeover * Co-CEO at Salesforce next to Mark Benioff. Learned the customer list, the pricing weaknesses, the pain points. * Left Salesforce January 2023 → founded Sierra February 2023 The customer trophy case Sierra poached from Salesforce: Sonos, Casper, Rocket Mortgage. Taylor also poached Eric — the head of Salesforce's Agent Force. The result for Salesforce: support team cut from 9,000 to 5,000 in 18 months. Stock down 30% in 2026 — one of the worst Dow Jones performers. ⚖️ Two chairs, one man Remember the OpenAI drama? Sam Altman fired, Satya Nadella flying in, mass-resignation threats, the board imploding? In the middle of all that chaos, who got named Chairman of the Board at OpenAI? Brett Taylor. Now add it up: * Sierra uses OpenAI models → Sierra is a customer of OpenAI * Taylor chairs OpenAI → he sits at both sides of the table * Sierra's $950M round includes Google Ventures — OpenAI's direct competitor * Sierra runs a constellation of 15 frontier models: ChatGPT, Claude, Gemini, Llama, fine-tuned proprietary — they don't care, they're not monogamous His diplomatic answer for years: "We exist at different layers of the tech stack. I would recuse myself if there was an opportunity for conflict." Sure, Brett. And at Davos in January, he was on CNBC criticizing AI valuations. Four months later he takes $950M at 105× revenue. The man who warned about the bubble is inflating it himself. 🤖 The Sierra Agent OS — what actually happens in 500 ms When you call a Sierra agent, this happens before you finish your first sentence: 1. Planner agent receives the customer intent — figures out why you're calling 2. Executor agents tap into different backends — CRM, payment gateway, inventory, knowledge base. Different models for different jobs. 3. Validator agent reviews the response against policy rules before it reaches you 4. Model failover — if OpenAI's API goes down or starts hallucinating, it auto-routes to Anthropic, then to whichever LLM is healthy. Sierra is built assuming every model will fail eventually. One second into your call, Sierra has already orchestrated 3+ models. 🛒 Who actually runs this * Weight Watchers — 70% of all customer sessions are Sierra. CSAT: 4.6/5 — higher than humans. * Sonos — Sierra handles the hard stuff: full setup wizards, Wi-Fi config, music service integration, end-to-end onboarding * Healthcare, fintech, credit cards, mortgages — the list is long and growing. These aren't chatbots. They're process machines that take actions. ⚔️ Why AI agents beat humans in this category * Don't get tired — no morning, no evening, no hangover, no sick kid * No bad days. After 3 angry customers a human is done — the agent's consistency is auditable * Speak every language on earth — Zulu, Mandarin, Arabic, Portuguese, all of them * Analyze tone in real time — stressed, frustrated, resigned — and plan the next sentence to de-escalate * Parallel system access — CRM + return policy + manuals + history all in "head" simultaneously * Learn from every conversation. Humans don't — our brains are too small. 🎬 Who watches the watchman? Two chairs, both empty by midnight. The chairman who left the OpenAI boardroom is the same chairman who signed the $950M term sheet for the company that will be selling you AI agents. And Mr. Ivan? Ivan and his thousand friends spent years learning headsets, learning manuals, learning customer service. Their jobs are now performed by an AI agent that costs peanuts. In the AI economy of 2026 and 2027, your company will turn to Sierra. Because it's cheaper. More efficient. Auditable. And Mr. Brett, with his beautiful conflict-of-interest architecture, is absolutely worth the AI drama. ⏱️ Timestamps * 00:00 — Cold open: Manila + Ivan, San Francisco + $950M * 03:00 — Who is Sierra? 16B valuation, 40% of Fortune 50, 95% of Black Friday * 06:00 — The 105× revenue multiple problem * 08:30 — Brett Taylor's resume — Google Maps to Facebook to Twitter to Salesforce * 12:00 — Salesforce bleeds: 9,000 → 5,000 support, stock −30% * 14:30 — The OpenAI chair + Google money + Sierra customer conflict architecture * 17:00 — Davos hypocrisy: warning about AI bubble while inflating it * 18:30 — Inside Sierra's Agent OS — planner, executors, validator, 500ms orchestration * 22:00 — Weight Watchers (CSAT 4.6/5), Sonos end-to-end onboarding * 25:00 — Why AI agents beat humans on this category * 28:00 — Who watches the watchman? Closing from Bregenz. 🎙️ About the Host Malcolm Werchota runs AI adoption programs for companies across Europe. After 15+ years at Novartis and Schlumberger, today's focus: AI without the bullshit. Lecturer at ESADE and HSLU. Studied in Leoben. 🚀 Resources for Executives * 📚 Chief AI Academy — AI for Decision Makers [https://www.werchota.ai/chief-ai-academy] * 👥 AI Leadership Community [https://chief.werchota.ai/getting-started] * 🌐 werchota.ai [https://www.werchota.ai] 📬 Contact

#121 - AI Doesn't Eliminate Jobs — It Eliminates ROLES. Three Roles You MUST Hire in 2026.

A good friend of Malcolm — from the automotive industry — said it to his face: "Malcolm, your podcast is nice and all, but in our auto sector, literally nothing is moving. Nobody is firing people because of AI." Wrong. So wrong. This episode is the answer. Here's the line that anchors the whole episode: AI doesn't really eliminate jobs. What it does — it eliminates ROLES. And in 2026, the companies that survive are the ones that hire three completely new types of roles that didn't exist two years ago. Look at General Motors right now: 500-600 IT positions gone in one wave. Plus the 1,000 software engineers they cut two years ago. Multiple parallel waves over 18 months. The "important ones who are supposed to roll out AI" — exactly them. And it's not just GM. The pattern runs from San Francisco to Munich. Siemens. SAP. Amazon (14,000 corporate roles last year + another 16,000 this year). Microsoft (15,000 + 15,000, three rounds planned). Workday. CrowdStrike. Block. These aren't trees being trimmed — these are entire forests being clear-cut. 🤖 What is an AI Agent, anyway? Malcolm's working definition, after his 76-year-old dad visited last weekend and watched a live Claude Code demo: "An AI Agent is an AI with arms." It doesn't just chat — it executes. It opens files, writes code, files tickets, books meetings. His dad's reaction watching Claude Code work autonomously: stunned silence, then "this changes everything." If a 76-year-old gets it in 10 minutes, your CFO has no excuse. 🎯 The Three Roles You MUST Hire in 2026 * AI Agent Trainer — Not people who "use AI." People who train AI agents to do company-specific work. Completely different skill. This is the new prompt engineer + ops hybrid. * Buy-vs-Build Specialist — Someone who can look at a problem and call it: do we license a SaaS tool, or do we build it ourselves now that AI makes building 10× cheaper? Wrong call = millions wasted either way. * AI Teacher / Internal Enablement — Someone who can teach other humans how to use AI. Sounds basic. Biggest leverage point in the entire company. Without this role, your $200/month Claude licenses sit unused. 🚦 The Red-Yellow-Green Traffic Light System Score every candidate on the three skills: * 🟢 Green: All three — can train agents, can judge build/buy, can teach others * 🟡 Yellow: Two out of three (hire and develop the third) * 🔴 Red: Zero out of three → 99% of all hires being made in 2026 right now sit here 📋 Stop Running 1990s Interviews If you're still asking "tell me three strengths and three weaknesses" — you are running an interview format from the 90s in a market that has fundamentally changed. Ask instead: * "Have you trained anyone in your last role? Show me the deck." * "Teach me something about AI that I don't already know." * "Share your screen — show me LIVE how you use AI." The screen-share question alone filters out 80% of "AI-savvy" candidates in the first 30 seconds. ⚠️ The Uncomfortable Truth for HR If you sit in HR and you don't have a traffic light system — you are next on the red list. Sit with that for a second. Because the structured, repetitive screening work HR has been doing for 20 years is exactly the work AI agents do best now. Malcolm acknowledges Klarna as a "bad example" — they fired customer service, rolled out AI, then had to re-hire. The Salesforce paradox. But this is becoming the exception, not the rule. The pattern is shifting from "fire then re-hire" to "don't re-hire in the first place." Senior retires? Don't backfill. Junior asks for a repetitive data task? That task doesn't exist anymore. Harvard Business Review has documented this: since ChatGPT, junior hiring for structured work has dropped significantly. ⏱️ Timestamps * 00:00 — Cold open: For my friend in automotive who said "nothing is happening" * 02:30 — GM: 500-600 IT roles + the 1,000 from two years ago * 05:00 — The pattern: SF to Munich — Siemens, SAP, Amazon, Microsoft * 07:30 — Klarna and the Salesforce paradox (fire then re-hire) * 10:00 — Jobs vs Roles — the distinction that changes everything * 12:00 — My 76-year-old dad meets Claude Code — "AI with arms" * 14:30 — The three new roles you MUST hire * 17:00 — The Red-Yellow-Green traffic light * 19:00 — Stop running interviews from the 90s * 20:30 — Why HR is next on the red list * 22:00 — Closing: Leoben, Manuel, Simona, half miracles 🎙️ About the Host Malcolm Werchota runs AI adoption programs for companies across Europe. After 15+ years at Novartis and Schlumberger, today's focus: AI without the bullshit. Lecturer at ESADE and HSLU. Studied in Leoben. 🚀 Resources for Executives * 📚 Chief AI Academy — AI for Decision Makers [https://www.werchota.ai/chief-ai-academy] * 👥 AI Leadership Community [https://chief.werchota.ai/getting-started] * 🌐 werchota.ai [https://www.werchota.ai] 📬 Contact * LinkedIn: linkedin.com/in/malcolmwerchota [https://linkedin.com/in/malcolmwerchota] * Email: malcolm@werchota.ai [malcolm@werchota.ai] 📰 Sources * TechCrunch + Transport Topics — General Motors IT Layoffs 2026 * Amazon Corporate Layoffs reporting (14k + 16k planned) * Microsoft Workforce Adjustments under Satya Nadella * Harvard Business Review — The ChatGPT Effect on Junior Hiring * Gartner + McKinsey — AI Role Redesign Frameworks Tags: #AI #AICookbook #AIAdoption #JobMarket #FutureOfWork #RoleRedesign #GM #Siemens #SAP #Amazon #Microsoft #Automotive #HR #Recruiting #Hiring #AIAgent #BuyVsBuild #Klarna #werchota #ChiefAIAcademy #TheAICookbookShow

#120 - You Cannot Roll Out AI. Period. — Why Anthropic, Goldman, and Blackstone Are About to Run Your Business

Picture this. You're not hiring a consultant anymore. You're hiring the model maker itself. Plus private equity. Not for a strategy paper. For the complete redesign of your core operations. That's what just landed. A new firm — reportedly valued at $1.5 billion. Anthropic, the lab behind Claude, teaming up with Blackstone, Hellman & Friedman, and Goldman Sachs to launch an Enterprise AI Services structure. Four names that normally don't sit at the same table. When they do — the message is brutal: AI transformation is not going to run through dozens of loose consulting projects anymore. It's going to run through a productized delivery machine. In this episode, Malcolm lays out the full picture: why the real bottleneck inside companies was never the model — it's the implementation. Why Blackstone, in its press release, openly calls this the "Implementation Partner Bottleneck." Why the classic systems integrators — Accenture, Deloitte, Capgemini, McKinsey, BCG, IBM — are about to lose a chunk of their power. And why this hits the global mid-market and Fortune 500 immediately: from AP automation to procurement, from sales pipeline to customer service. The truth behind the "build-it-yourself" romance: serious AI workflows in production typically need $1.5M–$2.5M and 10+ engineers over months for one industrial-grade flow. How many devs do you have spare? One? End of discussion. Buy, don't build. The new business model isn't "advisory." It's Equity-for-Implementation: Anthropic + PE partners don't show up with a pitch deck — they take 15–20% of your business unit and get the operational mandate to run AI transformation. If your competitor does this and you don't? Game over. The episode closes with five concrete Monday actions before the next vendor pitch: * Implementation inventory — where are you currently burning money? * Hard build-vs-buy criterion — your dev capacity vs realistic workflows * Ownership map for every external partner — who holds the operational DNA? * One real use case instead of the prettiest demo * The exit test — what happens if the AI partner walks tomorrow? Plus the one question every director must ask: When the model maker itself becomes the operator — who owns your operational DNA at the end of this? The next wave will not be won by those who shout loudest about agents. It will be won by those who wire roles, processes, data, and execution together cleanly enough that a model turns into a working business. ⏱️ Timestamps * 00:00 — Cold Open: "You Cannot Roll Out AI. Period." * 03:30 — Why your company can't make it. The Kafka labyrinth. * 07:30 — Why your employees don't want it. The "AI King" sarcasm. * 11:00 — Why THEY will make it. With mandate. * 14:30 — The secret weapon: 1 billion chats per week. * 17:30 — Two historical parallels. SAP. Industrial robots. * 20:30 — Director liability and the one sentence. * 22:30 — Three negotiation moves before the next term sheet. * 24:00 — The model maker is now the operator. 🎙️ About the Host Malcolm Werchota runs AI adoption programs for companies across Europe. After 15+ years at Novartis and Schlumberger, his focus today is practical AI rollout — no bullshit. Lecturer at ESADE and HSLU. Works with CEOs from 50 to 20,000+ employees. 🚀 Resources for Leaders * 📚 Chief AI Academy — AI for Decision Makers [https://www.werchota.ai/chief-ai-academy] * 👥 AI Leadership Community [https://chief.werchota.ai/getting-started] * 🌐 werchota.ai [https://www.werchota.ai] 📬 Contact * LinkedIn: linkedin.com/in/malcolmwerchota [https://linkedin.com/in/malcolmwerchota] * Email: malcolm@werchota.ai [malcolm@werchota.ai] 📰 Sources * Anthropic — Enterprise AI Services Company [https://www.anthropic.com/news/enterprise-ai-services-company] * Blackstone Press Release [https://www.blackstone.com/news/press/anthropic-partners-with-blackstone-hellman-friedman-and-goldman-sachs-to-launch-enterprise-ai-services-firm/] * TechCrunch — Anthropic + OpenAI JVs [https://techcrunch.com/2026/05/04/anthropic-and-openai-are-both-launching-joint-ventures-for-enterprise-ai-services/] * CNBC — Goldman/Blackstone AI Venture [https://www.cnbc.com/2026/05/04/anthropic-goldman-blackstone-ai-venture.html] * Fortune — Claude Consulting Industry [https://fortune.com/2026/05/04/anthropic-claude-consulting-industry-joint-venture-blackstone-goldman-sachs/] Tags: #Anthropic #Claude #Blackstone #GoldmanSachs #HellmanFriedman #EnterpriseAI #AIServices #ImplementationBottleneck #BuildVsBuy #Fortune500 #MidMarket #Accenture #Deloitte #Capgemini #McKinsey #BCG #IBM #AIConsulting #TheAICookbook #werchota #ChiefAIAcademy #BoardLiability

#119 - Why Token Dashboards Will Soon Decide Who Keeps Their Job

Picture this. It's Monday morning. You open your laptop, go to the internal portal — and for the first time, you see a dashboard where everyone in the company can see how heavily you're using AI. Not somewhere in the future. Not science fiction. Just a real management logic that's quietly being built right now inside major enterprises. In this episode, Malcolm explains why exactly those kinds of AI Adoption Dashboards, Token Leaderboards and internal AI shitlists will show up in European companies within the next 12 to 18 months. Not just at Meta, Disney, JP Morgan, Visa or Salesforce — but also at companies in Bregenz, Zurich, Vienna, Linz or Wolfsburg. The trigger for this episode is a hard reality check: Meta is rolling out its Model Capability Initiative — an internal system that tracks employee behavior on corporate laptops in fine detail. Keystrokes, mouse clicks, screenshots, browser activity. The point behind it is brutally clear: companies want to understand how people work today, so they can hand that work over to AI agents tomorrow. Malcolm connects this to a second development that hits even closer to most companies' daily reality: token dashboards. Who uses how much AI? Who burns the most tokens? Who's visibly working with Copilot, Claude or ChatGPT? And who shows up at the very bottom of one of these dashboards? The uncomfortable truth is this: in many companies, AI usage will no longer just be recommended — it will be measured, compared and culturally loaded. But this episode doesn't stay stuck in fear. For Malcolm, the real question isn't whether these dashboards are coming, but how companies design them. Most enterprises already sit on every data source they need: Copilot usage, VPN logs, endpoint data, Slack, Teams, Jira, ServiceNow, CRM systems and more. The infrastructure to make AI adoption visible already exists. The episode gets interesting where it stops being a control discussion and turns practical. Malcolm explains concretely how companies can push AI adoption without sliding straight into surveillance logic. That includes cash pools for teams that visibly automate work, CEO demos that send real signals, early adopters who get actual time to experiment, and an honest conversation with works councils — instead of dragging them in at the very end. The central message of this episode is uncomfortable but crystal clear: AI adoption is going to become measurable inside companies. And firms that pretend this is just a US problem or a narrow data protection topic are quietly sleeping through a shift that will reshape how they work, how their culture feels, and how they make personnel decisions. 🎙️ About the Host Malcolm Werchota runs AI adoption programs for companies across Europe. After 15+ years in international corporations and leadership roles, his focus today is on practical AI rollout — no bullshit. He works with companies from manufacturing to pharma, from mid-size businesses to large enterprises, always with a sharp focus on real-world applicability and business value. 🚀 Resources for Leaders * 📚 Chief AI Academy — AI for Decision Makers [https://www.werchota.ai/chief-ai-academy] * 👥 AI Leadership Community [https://chief.werchota.ai/getting-started] 📬 Contact * LinkedIn: linkedin.com/in/malcolmwerchota [https://linkedin.com/in/malcolmwerchota] * Email: social@werchota.ai [social@werchota.ai] Tags: #AI #AICookbook #AIAdoption #TokenDashboard #Copilot #Claude #ChatGPT #Meta #Disney #Productivity #Leadership #ChangeManagement #WorksCouncil #EnterpriseAI #FutureOfWork