AI Summary
5 min read"At the start of 2025, AI just seemed to me like the latest entry in a long line of Silicon Valley hype cycles. By the end of 2025, it kind of seemed undeniable that somehow this was going to change the way we all work and fast, really fast."
That’s Chris Jenkins, host of Developer Voices, setting the stage for a conversation with Henry Garner, CTO of the consultancy Juxt. Garner has published an AI tech radar—a quarterly PDF tracking 68 technologies in the AI landscape—and he joins the episode to explain what’s worth paying attention to, what’s fading, and how professional programmers should adapt their craft. The conversation ranges from the psychology of adoption inside engineering teams to the mechanics of neurosymbolic AI, the shelf life of tools like MCP, and the emergence of a new behavioral specification language called Allium. Throughout, the emphasis is on practical judgment: what to use, when to use it, and how to stay grounded amid the noise.
The psychology of adoption and the shift from pilot to air traffic controller
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What you'll learn
- 1 (00:00) **Opening: The Speed of Change** - Chris frames the episode: AI is moving faster than the web did, and the question is how software engineers keep up.
- 2 (04:36) **How AI is Affecting Jobs (The Agency Perspective)** - Henry shares what he sees at Juxt, a consultancy of ~150 senior engineers.
- 3 (08:45) **Resistance, Contrarians, and the Limits of LLMs** - There's a bell curve of adoption; Henry works to shift the mean.
- 4 (12:23) **Vibe Coding vs. Delegated Coding** - Chris introduces "delegated coding" as the professional alternative to vibe coding.
- 5 (15:40) **Language Choice in the Age of AI** - Do certain languages fit better with LLM coding?
- 6 (19:19) **The AI Tech Radar: What's Hot and What's Not** - Juxt publishes a quarterly 60-page radar with ~70 blips.
- 7 (21:28) **MCP vs. Skills: The Current Debate** - Is MCP dead? Are skills the new hotness?
+ Full timestamped outline available in the app
Show Notes
AI is changing the way we all build software — that much seems clear. But the landscape is moving so fast that even the people paid to keep up are struggling. MCP or skills? Fine-tune or just prompt? LangChain or let a thousand agents loose? With almost 70 competing technologies and a shelf life of maybe six months on any advice, how do you figure out what's actually worth your time?
Henry Garner is CTO of JUXT, a consultancy with about 150 senior engineers working at the coalface of AI-assisted development, including building AI platforms for tier-one banks. JUXT publishes a quarterly AI Radar — 68 technologies rated and reviewed — and Henry's been watching his own team go through the full adoption arc, from "spicy autocomplete" skepticism through to building Byzantine-fault-tolerant distributed systems over a weekend with Claude. Along the way we cover MCP vs skills, Conway's Law for LLMs, neurosymbolic AI and the unexpected return of Prolog, the "Ralph Wiggum loop" for getting agents to converge on correct implementations, and Allium — a new behavioral specification language Henry's co-authored that sits between human prose and TLA+, aiming to give LLMs just enough structure to pin down what a system should do without falling into waterfall thinking.
If you're trying to make sense of the AI tooling landscape, or you've hit that wall where your agents keep drifting away from what you actually wanted, Henry's thesis — velocity through clarity of intent — might well help out yours.
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JUXT: https://www.juxt.pro/
JUXT AI Radar: https://www.juxt.pro/ai-radar/
Allium on GitHub: https://github.com/juxt/allium
Allium Documentation: https://juxt.github.io/allium/
Composition at a Distance (Henry's blog post): https://www.juxt.pro/blog/composition-at-a-distance/
A New Vocabulary for an Old Problem (Henry's blog post): https://www.juxt.pro/blog/new-vocabulary-for-an-old-problem/
Model Context Protocol (MCP): https://modelcontextprotocol.io/
LangChain: https://www.langchain.com/
LangGraph: https://www.langchain.com/langgraph
Gas Town (Steve Yegge): https://github.com/steveyegge/gastown
Kiro (spec-driven AI IDE): https://kiro.dev/
Phoenix (LLM observability): https://github.com/Arize-ai/phoenix
Temporal: https://temporal.io/
Taalas (LLM-on-a-chip): https://taalas.com/
Kris on Bluesky: https://bsky.app/profile/krisajenkins.bsky.social
Kris on Mastodon: http://mastodon.social/@krisajenkins
Kris on LinkedIn: https://www.linkedin.com/in/krisjenkins/
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