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- Speed without strategy is just expensive chaos
Speed without strategy is just expensive chaos
Your team can build anything in hours now. That's not always good news.
Able Craft & Code
October 15, 2025
In this issue:
CRAFT: Why building faster doesn't mean you're building smarter—and the three questions to ask before greenlighting that "quick build"
CODE: When to reintroduce friction: shifting from continuous deployment to AI-aware delivery
NEWS: DeepSeek's assembly-level breakthrough challenges the scaling narrative, authenticity becomes the new premium in an AI-saturated world, and Meta's multibillion-dollar talent grab reveals the arms race mid-market companies can't afford to join
CRAFT NOTES
What we’re thinking about
AI has flipped one of software development's oldest constraints: the cost of building something. For decades, saying "no" to feature requests was easy—you didn't have the time, the budget, or the people. Now? Your team can spin up a working prototype in an afternoon.
And that's where things can go sideways.
You're not alone if your backlog has exploded. When the barrier to building drops to near-zero, teams start operating under a seductive new logic: "It'll only take a couple of hours, so why not?"
But here's the trap: speed without strategy is just expensive chaos.
Building faster doesn't mean you're building smarter. It just means you're accumulating technical debt, scattered initiatives, and half-finished experiments at an accelerated pace. Your engineering team isn't bogged down by slow delivery anymore—they're drowning in a sea of "quick wins" that don't connect to anything meaningful.
The real bottleneck isn't coding speed. It's strategic clarity.
Before you greenlight the next "quick build," pause and ask three questions:
Where does work actually stall in our delivery flow? Map your end-to-end process. The constraint usually isn't in the code—it's in requirements, testing, deployment, or adoption. Building faster doesn't fix those.
Should we build this? Should we and can we are very different questions. Fast to build doesn't equal worth building. The ability to ship something quickly is not a strategy.
Are we investing in the human side? Problem framing, design thinking, and change management matter more than ever when technical execution becomes trivial. If you're not doubling down here, you're just automating the wrong priorities.
AI tools are powerful multipliers. But if you're multiplying the wrong things, you're just accelerating in the wrong direction.
The teams that win aren't the ones building everything—they're the ones building what matters.
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CODE LAB
What we’re discovering
"For years, we optimized CI/CD for speed—automating everything, minimizing friction, enabling continuous deployment. But with AI coding assistants, it's time to reintroduce friction—selectively and strategically. AI doesn't understand context or risk. It predicts tokens. Even the most advanced tools can accidentally push code to production despite configured safeguards. We're shifting from Continuous Deployment to AI-Aware Continuous Delivery. Not abandoning automation—refining it. Let AI accelerate development. Let humans protect what matters."
Why this matters for your team: The same automation that made your team faster can become a liability when AI assistants enter the workflow. A single misconfigured prompt or misunderstood context can push breaking changes straight to production. The fix isn't ditching AI tools—it's adding strategic checkpoints: branch protection that requires human review, manual approval gates before production deploys, and strengthened test coverage around refactors. The math is simple: AI saves hours in development, smart guardrails cost minutes in deployment. Production is too critical to leave to implicit behavior.
AI NEWS
What we’re paying attention to
DeepSeek achieved breakthrough AI efficiency by using assembly language to command Nvidia processors to compress data from 32 bits to 8 bits at precisely the right moments—shattering the assumption that AI progress requires only more chips and energy. The move stunned observers who hadn't realized such low-level optimization was possible. For mid-market leaders, this is a reminder that efficiency often comes from deeper understanding, not bigger budgets. Before throwing more infrastructure at your AI challenges, ask whether you're truly optimizing what you already have.
As generative AI floods the internet, audiences are developing filters for what's real versus synthetic. While AI remains useful for quick information delivery, creators leveraging genuinely human qualities—experience, nuance, insight—are rising above the sea of generic "AI slop." Your domain expertise and hard-won insights are becoming more valuable, not less. The question isn't whether to use AI for content—it's how to use it without sacrificing the authentic voice that differentiates your brand.
Meta just hired machine learning expert Andrew Tulloch from Thinking Machines, part of a broader multibillion-dollar talent spree that included a $14.3 billion investment in Scale AI to recruit its CEO Alexandr Wang. Tech giants aren't slowing their pursuit of top AI talent. For mid-market companies, this creates an uncomfortable reality: you're competing for the same talent pool as companies writing nine-figure checks. The good news? You don't need to win the arms race to win at AI. Focus on building environments where engineers do meaningful work with real autonomy, partner strategically with vendors who've solved the talent problem, and remember that execution beats pure horsepower.
SKIP THE ENDLESS EXPLORATION, START BUILDING
Done with endless AI meetings? Ready for practical implementation?