The End of Bodyshopping: Farewell to the Great Desk-Warming Economy
Bodyshopping, that glorious era when tech companies billed clients by the kilo of developer, is finally wheezing its last breath. For years, the...
1 min read
Steve Dennis Aug 7, 2025 11:45:54 AM
For too long engineers have contended with “black boxes” —technologies whose internal workings are opaque. You see the data go in and the results come out, but what happens in between is hidden. This is especially risky now, as large language models (LLMs) and AI systems expand across industries. The end of the SDLC is being called out in the clickbait that is LinkedIn, but truly there is a looong way to go here, and we are here for the full journey.
Today’s AI models use billions of parameters. Their complexity often outpaces our ability to explain their decisions—even for their creators. The result? We are seeing some widespread confusion on how to integrate or trust these tools.
Swapping one black box for another is common—think legacy mainframes upgraded to proprietary cloud platforms, or old code replaced with third-party APIs.
Transparency isn’t just idealism—it’s essential risk management.
Black boxes may be tempting shortcuts, but successful, resilient engineering demands visibility. The era of AI only intensifies this need—so insist on transparency at every step.
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