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- Ep 780: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
Ep 780: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
Google’s new Gemini 3.5 Flash drop, Musk loses OpenAI lawsuit and OpenAI cofounder joins Anthropic.
Everyday AI
May 19, 2026
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Sup y’all! 👋
Big day from Google, as their I/O conference in still in progress as we send this out. (More on the new drops tomorrow, as I’m sure there’s more to come.)
Tomorrow is our ‘AI at Work on Wednesday’ podcast. What do you want to hear more on? New Google releases? Our second Codex pod?
What should we cover tomorrow on our 'AI at Work on Wednesday' series?🗳️ Vote to see LIVE results 🗳️ |
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Jordan
Outsmart The Future
Today in Everyday AI
8 minute read
🎙 Daily Podcast Episode: Most companies are still thinking about AI as one giant buy-versus-build decision. In reality, the smartest companies in 2026 are deciding what to build, buy, partner on, or wait on across every layer of the AI stack. Give today’s show a watch/read/listen.
🕵️♂️ Fresh Finds: Anthropic is expanding Claude’s enterprise push with new infrastructure tools and its Stainless acquisition, Cohere just bought a biotech AI startup, and Mistral is moving deeper into industrial AI with a new acquisition, and more Read on for Fresh Finds.
🗞 Byte Sized Daily AI News: Google’s new Gemini 3.5 Flash drop, Musk loses OpenAI lawsuit and OpenAI cofounder joins Anthropic. Read on for Byte Sized News.
💪 Leverage AI: Most companies are treating AI like one big buy-versus-build decision, when it’s really four separate decisions that require completely different strategies. Keep reading for that!
↩️ Don’t miss out: Miss our last newsletter? We covered: Meta is cutting thousands of jobs while pouring money into AI, a former Microsoft executive says the company’s AI strategy is failing users, and backlash over AI’s impact on jobs is starting to spill into the real world, and more. Check it here!
Ep 780: Build, Buy, Partner, or Wait: The 4-Layer AI Stack Decision Framework for 2026 (Start Here Series, Vol 25)
The most expensive AI mistake of 2026 won't show up on any invoice. 💸
It'll show up two years from now when you can't get your data out, your competitors are eating your lunch, or your team is stuck maintaining software no one actually wanted to build.
Because in 2026, AI isn't one decision anymore.
It's four.
The model. The workflows. Your data. Your business software.
Each layer has its own build, buy, partner, or wait choice.
And most companies are making all four without realizing it.
Today on Everyday AI, we're breaking down the framework that puts those choices back in your hands
Also on the pod today:
• Four-layer AI stack revealed 🏗️
• Build, buy, partner, or wait? 🤔
• Agentic AI transforming workflows 🤖
Listen on our site:
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Here’s our favorite AI finds from across the web:
New AI Tool Spotlight – PollyReach is an AI Agent That Can Now Make Real Phone Calls, Drizz Vision is Reliable Mobile Test Automation Built For Real Apps, Chert Builds and deploys AI on iMessage to reach people at scale.
Anthropic and Stainless — Anthropic just bought Stainless to further enhance Claude’s ability to connect with more tools and data.
Claude Managed Agents — Claude's Managed Agents now let you run AI tools inside your own secure infrastructure, with new support for self-hosted sandboxes and private network connections.
Claude Token Limits — Claude just doubled its token limits on all plans, so you can do even more with Claude Design. See how users are taking advantage and what's new.
Cohere Acquisition — Cohere just bought Reliant AI, bringing top biopharma AI tech and expertise into its platform.
Unitree Robot — Unitree’s G1 robot just pulled off real-time, voice-controlled action generation in a single take
Composer 2.5 Release — Composer 2.5 just dropped in Cursor, boasting smarter, more reliable performance on complex tasks thanks to upgraded training and feedback.
Mistral Acquires Emmi — Mistral AI just snapped up Austria’s Emmi AI to boost its industrial tech, especially for complex physics tasks
NVIDIA Chips — Nvidia’s Jensen Huang says China might eventually open up to high-end US AI chips, but for now, Beijing is playing it cautious.
1. Google Unveils Gemini 3.5 Flash as Its Fastest Agentic AI Yet 📸
Unveiled this week at Google I/O, Gemini 3.5 Flash marks a clear shift from chat-focused AI to autonomous agents that can plan, code, and run complex projects with minimal human input.
Google says the model is its strongest yet for coding and agentic work, running up to 12 times faster than comparable frontier models while maintaining quality. The company is pairing Flash with its new Antigravity 2.0 platform, positioning AI agents as long-running digital workers rather than quick-answer tools.
2. OpenAI and Dell Team Up to Bring Codex Into Enterprise Data Centers 🏢
OpenAI and Dell Technologies just announced a partnership aimed at making Codex easier and safer for large companies to run inside their own data centers, a move that reflects how fast enterprise demand for AI agents is growing.
The collaboration lets Codex connect directly to Dell’s on‑prem and hybrid platforms, so businesses can use it alongside their existing data, systems, and workflows instead of pushing sensitive information to the cloud. That matters as Codex usage surges, now reaching millions of developers weekly and expanding well beyond coding into broader business tasks.
3. Jury Rejects Musk’s OpenAI Lawsuit on Timing Grounds ⏰
After just two hours of deliberation, an advisory jury unanimously found that Elon Musk’s key claims against OpenAI leaders Sam Altman and Greg Brockman were barred by the statute of limitations, a decision the judge accepted and that effectively ends the case at this stage.
The jury said Musk’s charitable trust and restitution claims were filed too late, which also sank his allegation that Microsoft aided the supposed breach. While the verdict is not legally binding, it carries weight, and US District Judge Yvonne Gonzalez Rogers endorsed it, closing the door on Musk’s arguments for now.
4. OpenAI cofounder Karpathy Jumps to Anthropic as AI Talent War Heats Up 🔥
Andrej Karpathy, a prominent AI researcher and former Tesla AI chief, announced he is joining Anthropic this week, marking a major talent win as competition intensifies at the cutting edge of large language models.
Karpathy, who helped co-found OpenAI and later led Tesla’s Autopilot vision team, will build a new group focused on speeding up Claude’s pretraining research, a core part of how advanced AI systems learn. The move comes as Anthropic pushes to rival OpenAI in both valuation and technical leadership, while aggressively recruiting top researchers from across the AI industry.
5. Blackstone and Google Launch $5B AI Data Center Push in the U.S. ⚡
Blackstone and Google unveiled a major joint venture to build a new U.S. company offering AI-focused data center capacity powered by Google’s custom TPUs, marking one of the largest private bets yet on AI infrastructure.
Blackstone is putting up an initial $5 billion in equity, with plans to bring 500 megawatts of compute capacity online in 2027 and scale from there as demand accelerates. The move gives companies another way to access Google’s AI chips outside of Google Cloud, widening choice at a moment when compute shortages are reshaping the AI market.
6. Google Unveils Gemini Omni, a New All‑in‑One AI Model for Video and Beyond 💪
Google used its I/O conference today to formally introduce Gemini Omni, a single multimodal AI model designed to generate and edit text, images, audio, and video together, signaling a major shift in how generative media tools are built and used.
The catch for businesses is timing: Omni is available now only through individual paid Gemini subscriptions, not yet through the enterprise‑grade APIs most companies need.
A single AI strategy is how smart companies buy themselves a trap.
It sounds responsible on paper and around the boardroom conference table. Centralized. Efficient. Very board-deck friendly.
It’s also how you end up building what you should’ve rented, buying what you should’ve owned, partnering where you needed control, and waiting right through the moment your competitors rebuilt the workflow.
The old buy-versus-build question made sense when AI meant chatbots, RAG pipelines, and a whole lot of “please don’t paste customer data into that thing.”
Now AI agents can read, write, schedule, update systems, create files, pull context, and run inside the software your company already uses. That means our company’s AI decision is no longer buy or build like it was 2-3 years ago.
It’s model, workflow, data, and business software. Four layers. Four different calls.
That’s what we tackled on today’s Everyday AI: how to decide what to build, buy, partner on, or wait out before your AI roadmap quietly turns into vendor lock-in with a nicer logo.
1. Break the stack apart 🔥
Most AI strategies fail before implementation because leaders collapse four separate decisions into one.
The model is one choice. The workflow is another. Your data and business context deserve their own decision. Then there’s the software layer, where AI has to do more than politely summarize your calendar like a glorified intern.
The real shift is read-write AI. Reading email is useful. Writing the follow-up, creating the file, updating the CRM, and triggering the next step is where AI starts changing the operating model.
That’s why “we bought Copilot” or “we’re building agents” isn’t enough. Those are labels. The actual strategy is deciding which layer creates leverage and which layer creates risk.
Try This
Pick 10 workflows on Monday and map each one across four layers: model, workflow, data/context, and business software.
Then name one accountable owner for each workflow. If nobody owns the call, the vendor will happily own the outcome.
2. Own what makes you different ⚡
Building with AI is easier now.
That’s the opportunity. Also the trap.
Agentic engineering makes internal software feel suddenly possible, even for teams that had no business building production tools two years ago. Great. But easier building can still produce expensive garbage if the team builds the wrong layer.
Build around what compounds: internal judgment, subject matter expertise, SOPs, meeting notes, proprietary workflows, and first-company data. That’s where your advantage lives.
Don’t burn money building commodity model infrastructure because someone watched a demo and got dangerous. Build the workflow layer when the process is unique, the knowledge is yours, and vendor lock-in would slowly choke the value out of it.
Try This
Choose one low-risk workflow that reflects how your company actually operates.
Think meeting transcripts turned into searchable internal knowledge. Approval routing based on your policies. A niche customer handoff that depends on tribal knowledge. Something annoying, valuable, and weirdly specific.
Build a small version. Measure time saved, failure rate, maintenance cost, and portability.
If the value compounds, keep going. If it only exists because the demo looked cool, kill it before it becomes a budget line.
3. Rent protection, partner on pain 🚀
Buying still makes sense when the workflow is common, regulated, audit-heavy, and already buried inside your existing platforms.
Boring? Yep.
Often correct? Also yep.
If the workflow lives inside your ERP, CRM, finance stack, Slack, ClickUp, or another system your team already uses all day, buying the AI layer may protect more than it costs. You’re paying for controls, audit trails, integration, and less chaos.
Partner when failure gets expensive: cybersecurity, regulated decisions, physical operations, critical infrastructure, and complex domain data. That’s not the place to cosplay as a frontier lab.
Waiting is the sneakiest call.
Sometimes waiting saves you from chasing unstable tools like OpenClaw before enterprise-friendly versions mature. Other times, waiting becomes drift while competitors quietly close the capability gap and redesign work around agents.
That’s the judgment call executives have to get right now. Not in six months. Not after another committee forms a steering committee to discuss committee readiness.
Try This
Run a three-week decision sprint.
Week one: audit 10 workflows, score the four layers, and assign owners. Week two: build one internal workflow, buy one packaged workflow, and kill anything with no visible ROI. Week three: compare performance, renegotiate for data export and model portability, and set a weekly review cycle.
AI strategy in 2026 isn’t about picking a side.
It’s about owning the pieces that compound, renting the pieces that protect you, partnering where failure hurts, and waiting only when waiting is actually a strategy.
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