When the AI Behemoths Stumble, AI-Assisted Development Will Still March On
Even if the AI giants fall, AI-assisted development won’t—because the real shift is in how we build, not who powers it.
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If you’d like to support the growth of this resource, consider upgrading to paid and take advantage of the other ways I can help you.There’s a market correction coming.
It might be sudden, or it might play out over quarters, but the trajectory is clear.
Foundational AI platforms—OpenAI, Anthropic, Google DeepMind—have ridden an extraordinary wave of hype, capital, and developer enthusiasm. But signs are emerging that this era of unchecked growth and influence is due for recalibration. Regulatory friction is rising. The cost of training and serving state-of-the-art models is ballooning. And we’re starting to see the limits of productisation across generalist LLMs.
Now here’s the key question: What happens to AI-assisted development if one of these giants stumbles?
Some people are quietly preparing for the end of the party.
They assume that if OpenAI or Anthropic were to drastically change their business model, or even collapse under the weight of their shareholder obligations, the AI development wave would recede.
But that’s a fundamental misunderstanding of what’s really happening.
AI-Assisted Development Isn’t Just About the Model
What’s emerging isn’t a dependency on a single API.
It’s a shift in how developers work, how teams reason about problems, and how software systems are built, refactored, tested, and extended.
We now write specs that double as prompts.
We chunk tasks not just for sprints, but for model comprehension.
We validate ideas in minutes rather than hours.
We scaffold entire subsystems using only interfaces and context files.
And much of this is model-agnostic.
It’s not dependent on GPT-4 or Claude 3 Opus.
It’s enabled by workflows, tools, plugins, and patterns—many of which can run on open weights, lightweight models, or even local inference.
Indeed, the foundational model providers accelerated the process. They made it accessible and exciting. But the shift in behaviour and expectations is now embedded in teams. Developers won’t unlearn the speed, support, and flow they’ve come to expect.
Even if a market correction hits hard—let’s say API prices skyrocket, or a major platform pivots behind enterprise firewalls—the methods of AI-assisted development are here to stay.
The Post-Correction Landscape: Decentralized, Specialized, Resilient
A correction might trigger an even more robust phase of evolution:
Smaller, purpose-trained models will gain more traction. Not every task needs an 800-billion-parameter generalist.
On-device inference will become more practical and desirable, especially in privacy-sensitive industries.
Open-source tooling will flourish as teams seek more control and lower latency.
AI agents and copilots will become increasingly fine-tuned to context—local codebases, bespoke rules, and domain vocabularies.
We’re heading toward a more decentralised and composable AI development ecosystem, one that is less dependent on a handful of mega-providers.
So What Should Leaders Do Now?
If you’re leading a software team, don’t tie your strategy to a single vendor’s roadmap.
Instead, ask:
What AI-assisted workflows are becoming core to how we ship software?
Where can we adopt model-agnostic tools and open standards?
How do we build resilience into our stack if today’s API terms change overnight?
Because when the correction comes—and it will—the teams that treated AI-assisted development as a capability, not just a product, will be the ones that thrive.
How dependent is your team on a specific foundational model today, and how easily could you switch if needed?
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