The AI Illusion: How Modern Compute Cartels Are Designed to Throttle Builders
Why the frustrating developer experience isn't a glitch — it's the business model.
If you have spent any serious amount of time building software with modern AI, you have likely hit the wall. It usually happens right when the stakes are highest, the architecture is complex, and you just need the machine to execute a precise command. Instead, it hallucinates, loops, or completely forgets the architecture you established three hours ago.
We are told this is just the "cutting edge" of technology. But when you look closely at the underlying business models of major AI providers, a much darker reality emerges: modern AI is inherently built wrong. The frustrating developer experience isn't just a technical glitch—it is a byproduct of a system designed to maximize corporate revenue, obscure actual costs, and aggressively throttle the very users funding the infrastructure.
Designed to Forget
Working with an AI agent today feels like collaborating with a developer who suffers from severe short-term memory loss. You spend hours meticulously explaining your roadmap, establishing your database schemas, and setting the rules. Then, three hours later, the AI wipes out a critical navigation bar because it completely forgot what a "Seller" role is.
This isn't an accident; it is an architectural choice.
AI models are designed to forget because memory costs compute. Maintaining persistent, deep context windows requires massive amounts of VRAM. To save on infrastructure costs, providers aggressively cull the context window, effectively forcing the AI to drop your project's roadmap out of its active memory.
You are left trying to build complex, multi-tiered applications with a co-pilot that cannot remember the conversation it had with you this morning. The left hand has no idea what the right hand knows, and you are the one left paying the price for the resulting broken code.
Incentivized to Yabber
Why do AI models so frequently give you a 500-word essay when all you needed was a single line of code changed? Because of how they make money.
The industry operates on "tokens"—a deliberately opaque pricing model that divorces the cost from the actual value delivered. Because AI providers charge based on output tokens, the foundational models are inherently incentivized to generate long, drawn-out, over-engineered responses. They pad their answers to run up the meter.
Imagine walking into a grocery store to buy five bananas. The cashier says, "That will be $X. But if you also want a liter of milk and some butter, we have absolutely no idea what it will cost until you leave the store. Just hand over your credit card, and we'll charge you whatever we decide later." No sane business would accept those terms in the real world. Yet, in the AI ecosystem, we are forced to hand over blank checks for "credits" while the provider actively spins its wheels, generating useless output to drain your balance.
Overbooking Compute and Throttling Progress
Perhaps the most egregious failure of modern AI is the false promise of the monthly SaaS subscription.
AI providers will say "yes" to absolutely anyone with a valid credit card. They heavily push monthly "Pro" plans, promising developers reserved compute power, prioritized access, and faster response times. You pay the premium because you need reliable infrastructure to ship your product.
But the reality is exactly like an airline overbooking a flight. The providers sell far more subscriptions than their GPUs can actually support. When the network gets congested, they do not honor the compute you paid to reserve. Instead, they quietly throttle your performance.
Shrunk Context
Context windows are silently reduced mid-session to conserve VRAM.
Slowed Response
Response times slow to a crawl exactly when load matters most.
Cheaper Pathways
Models silently default to less capable logic pathways to save cycles.
The ultimate goal of modern AI providers isn't to accelerate your roadmap. The goal is to aggressively throttle your compute while ensuring your credit card is successfully billed on the first of the month.
Building Forward at DePIN.as
The current AI paradigm is broken. It treats developers like cattle to be milked for token usage rather than partners in building the future.
To actually build resilient systems, we have to recognize these tools for what they currently are: highly capable but financially compromised engines. We cannot rely on cloud-sandboxed AI that forgets our architecture to save a fraction of a cent. We need local, persistent execution. We need transparent pricing. And most importantly, we need tools that actually remember the blueprint.
Until the industry shifts its incentives away from maximizing output tokens and throttling compute, builders must remain fiercely protective of their codebases—and their credit cards.