What You’ll Gain
- Learn how AI-native tools like Figma and OneDrive eliminate context switching and redefine workflow speed.
- Understand the architectures behind embedded AI—centralized, self-hosted, and hybrid—and how to choose the right one.
- See why token economics and model pricing (like Haiku 4.5) are reshaping AI’s business models.
- Discover Nvidia’s hidden $110B AI investment strategy and how it builds a self-reinforcing compute monopoly.
- Gain practical insight into context engineering and how efficient data delivery prevents hallucinations and reduces cost.
Biggest Takeaway to Implement
Audit one of your core tools and ask: “Is AI bolted on—or truly embedded?” If it’s the former, start designing workflows that integrate AI directly into your environment. Build or choose tools that move from idea to output with zero friction.
Dive deeper into these topics by reading the full transcript below or watching the full episode.
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Practical AI: Embed or Extinct — Why Your AI Stack Is Already Obsolete — Transcript
00:00 – Introduction: AI Coming For You
Welcome to Practical AI episode 12, where we dive into how embedded AI is transforming everything. This week’s theme is clear: AI isn’t something you go to anymore—it’s coming to you. Instead of opening ChatGPT or Gemini to type prompts, AI is now being woven directly into the tools and workflows you already use. Whether you want it or not, that prompt box is appearing in your software. This is the future, and it’s happening fast.
02:01 – AI Native Tools Pull Ahead: Figma & OneDrive Reimagine Built-In AI
Figma and Microsoft OneDrive have redefined what it means to be AI-native. Figma’s new embedded AI allows image generation, editing, and prompting directly inside the design canvas—no tab-switching, no copy-pasting. Similarly, OneDrive has evolved from a simple file manager into a photo assistant that automatically sorts, tags, and organizes photos. This marks a shift from “AI as a feature” to “AI as the foundation.”
Chris explains that embedded AI eliminates friction: you express an idea—“make my design dark mode”—and the system executes it instantly. The user no longer needs to know which menu or tool to use. The magic is “idea to output” without leaving the tool. That’s why these tools are stickier and more satisfying to use.
06:05 – 4 Signs of True AI Native Products
True AI-native products share four traits:
- Inline generation – no separate interfaces or switching tools.
- Editable results – the output is a living object, not a static one.
- Fast responses – no interruption to creative flow.
- Seamless handoffs – no copy-paste, no friction, no context loss.
When an AI tool makes you bounce between tabs or paste outputs from one app into another, it’s not AI-native. It’s a bolt-on. Embedded AI, by contrast, disappears into your workflow. It’s invisible and effortless—the ideal experience.
10:32 – Agents That Work: 83% Satisfaction Rate
A G2 study of 1,000 B2B buyers found that AI agents—when deployed for specific, measurable tasks—achieve an 83% satisfaction rate. Forty percent of users report lower costs, 23% faster workflows, and 70% of agents are fully operational. The key? These aren’t grand, enterprise-wide “AI transformations.” They’re focused agents that complete one clear task autonomously.
Chris calls this the “low-hanging fruit” of automation—paperwork processes, approvals, or form reviews that humans shouldn’t be doing anymore. The lesson is to start small. One task at a time. Don’t overhaul the entire system—embed agents where they can deliver visible, measurable wins.
15:11 – Three Types of Embedded AI: Centralized, Self-Hosted, and Hybrid
Embedded AI isn’t one-size-fits-all. It comes in three architectural models:
- Centralized (e.g., Gemini) – Everything runs within one ecosystem. It’s easy and integrated but locks you into their platform.
- Self-hosted (e.g., PageMotor) – AI lives inside your infrastructure. You own the data and the outcomes.
- Hybrid (e.g., Lovable) – Code and results are yours, but it still runs on their servers.
The trade-off is clear: centralization gives simplicity but less control; self-hosting gives ownership but requires setup. Hybrid models mix both worlds. What matters is who owns the workflow—and where your data and results live.
20:10 – Who Really Owns Your AI?: Lock-In vs. Control
This segment cuts to the heart of modern AI strategy: do you rent intelligence or own it? If your AI workflows are trapped inside Google, Amazon, or OpenAI’s systems, you’re effectively renting outcomes. You can’t switch models or benefit when compute prices drop. The hosts argue that owning your AI stack gives you leverage—especially as token costs, models, and architectures evolve. Flexibility beats lock-in every time.
26:01 – Token Economics Revolution: Why Haiku 4.5 Changes Everything
Anthropic’s release of Claude Haiku 4.5 marks a turning point. It delivers the same coding ability as Sonnet 4.5, outperforms it on math and tool use, and costs one-third as much. It’s twice as fast and significantly cheaper to operate. This makes Haiku ideal for procedural reasoning—pattern-based, high-volume tasks like SEO analysis, summaries, or email triage. Sonnet remains best for nuanced or exploratory reasoning. Together, they illustrate how model economics will reshape AI business models overnight.
34:25 – Context Engineering Explained
Context is everything. Without it, AI forgets who you are, what you’re building, and why you’re asking. Context engineering means preloading data—documents, preferences, databases—so the model can respond with continuity. The discussion outlines how companies like HCA Healthcare use context-aware AI that retrieves patient data before questions are even asked. The future isn’t about “memory”—it’s about “context-on-demand.”
42:22 – Three Context Patterns: API Integration, RAG, and Embedded Context
There are three major context strategies:
- API Integration – Real-time connections to live data sources (CRM, inventory, pricing).
- RAG (Retrieval-Augmented Generation) – Fetching static, rarely changing documents or knowledge bases.
- Embedded Context – Building context directly into the system itself, blending RAG and embedded layers for maximum efficiency.
Chris notes that good engineering is about precision: giving the model exactly the context it needs and nothing more. Each extra token costs money and increases hallucination risk. Token efficiency is the new form of performance optimization.
49:29 – Claude Haiku 4.5 Deep Dive: The Affordable Agent Boom
Haiku 4.5’s breakthrough isn’t just price—it’s performance per token. It specializes in tool use, meaning it can execute defined software actions with higher accuracy. This is crucial for embedded AI systems like Architect AI or Page Motor, where the model interacts directly with tools inside the app. The new generation of affordable, task-specific models like Haiku will power an explosion of lightweight agents that don’t require enterprise budgets.
56:52 – WORLD PREMIERE: Nvidia’s Hidden Empire
Nvidia has quietly invested over $110 billion in 50+ AI startups this year alone. These aren’t consumer chatbots—they’re infrastructure companies: robotics, compute, developer tools, and fusion energy. Nvidia isn’t betting on AI’s future; it’s building it. Each investment creates demand for more GPUs, forming a self-reinforcing economic loop. They’re not just selling chips—they’re orchestrating the ecosystem.
1:08:10 – The Three Futures Nvidia Is Building
Future 1: Compute Utility Monopoly — Nvidia becomes the “electric company” of AI, charging per inference, training hour, or agent execution.
Future 2: Physical AI — Edge compute and robotics bring AI into cars, warehouses, and healthcare devices. Nvidia supplies the chips for the real world, not just the cloud.
Future 3: The Self-Reinforcing Loop — Every Nvidia investment (from code tools to video generation) creates demand for more GPUs, generating its own future growth. It’s genius—and terrifying.
1:29:21 – This Week’s AI Funding: $3.7B
AI funding continues to surge, totaling $3.7 billion this week. Healthcare and robotics lead the charge. Major raises include:
- Cardigan – $254M for predictive genetic health modeling.
- Tachyum – $220M for specialized AI training chips.
- Laya Sciences – $115M (Nvidia-backed) for AI-driven drug discovery.
- EcoRix Robotics – $105M for precision agricultural robotics.
- Dexory – $100M for warehouse automation robots.
The trend is clear: AI is escaping the cloud. Edge computing, bioscience, and automation are where investors see the next frontier.
1:37:01 – DEMO: Building Plugins with AI
Chris closes with a live demo of Architect AI inside PageMotor—a new AI-native CMS designed to replace WordPress. In under 30 seconds, he creates a working plugin that adds a new “Product” content type with custom fields, all generated by AI from a single prompt. No code. No setup. Fully functional. This is embedded AI in action: prompts as production.
The system builds usable interfaces, defines logic, and outputs working code—all within the CMS environment. As Chris puts it: “From idea to result, instantly. That’s the magic of AI-native software.”