Issue #15 Why AI Isn’t Actually Cheaper Yet
Automation's dirty secret...
When businesses talk about Artificial Intelligence, the conversation usually goes like this: “It’ll make us faster, leaner, and cheaper.”
Deep down, we know this is not true.
That’s because behind the glossy pitch decks and ambitious roadmaps most companies are discovering that AI isn’t the cost-saver they hoped for. In fact, for many, it’s quietly burning through budgets with alarming speed.
The Inference Trap
The biggest misconception is that AI’s main cost is training the model. It’s not. The real expense is inference, the cost of running that model, every time it generates an answer. A small chatbot might seem cheap during testing. But once usage scales? You’re not paying $200/month. Try $8,000.
OpenAI alone reportedly spent $2.3 billion in 2024 just to operate GPT-4. That’s 15 times more than it cost to train it. Startups following the “build fast” playbook often wake up to find their AI “features” have become financial sinkholes.
AI Is Getting Pricier, Not Cheaper
Contrary to popular belief, AI costs aren’t falling. They’re rising, sharply. GPT-4’s 32K token model costs double per token compared to the 8K version.
Want to fine-tune a model? That’ll cost you 8x more than a regular API call and you’re locked in once you do it.
Vendors are following the cloud industry’s well-known strategy: start cheap, scale up the prices once you’re committed.
No Economies of Scale
Traditional software gets cheaper per user as it grows. AI doesn’t.
Every new user means more compute. More compute means more GPUs, more energy, more cost linearly. Or worse.
Even giants like OpenAI reportedly lost $5 billion in 2024 despite huge revenues. Only because compute scales with usage, not efficiency.
Stability AI spent nearly $100 million on compute last year… and made just $11 million in revenue. Brutal.
Vendor Lock-In
Once you integrate a vendor’s tools, you’re stuck. Fine-tune with one provider? You can’t transfer it elsewhere.
Build prompts, workflows, or data pipelines around a specific API? Rebuilding it all will cost a fortune.
Vendors know this and they price accordingly. What starts as $50K/month can easily become $60K+ next year.
Cloud = Unpredictable Bills
AI runs in the cloud. That sounds flexible… until the bills arrive.
Every API call, every byte of data moved it adds up fast. Data transfer fees (the dreaded “egress tax”) often go unnoticed until they quietly rack up thousands in extra charges. And the worst part? You only see the true cost after the usage spikes.
Time Saved ≠ Money Saved
Yes, AI speeds up tasks. But unless that translates into fewer employees or higher revenue, you’re just redistributing effort. Payroll stays flat, and now you’re paying for AI too.
Often, the AI adds new work, people checking outputs, fixing mistakes, writing better prompts. It’s not subtraction but substitution.
No ROI Without Measurement
Many businesses fail to track AI’s real return. They don’t measure baseline time, error rates, or total costs. They just hope it’s working.
That’s how “digital transformation” turns into digital regret. You need hard KPIs: cost per workflow, value of error reduction, maintenance costs, hallucination corrections. Without that, you’re just burning cash in the dark.
The Hidden Cost
AI doesn’t replace work, but it changes it. Now someone must verify outputs, monitor performance, write audit logs, and handle edge cases.
In legal, tech, customer service and many other, humans are still needed. That oversight stack? It costs time, money, and talent. Call it the “Hallucination Tax.”
Can AI Be Cheaper?
Yes, if you treat it like a critical system, not a toy. Limit usage. Optimize models. Cache frequent queries. Monitor costs like a hawk. Build with cost efficiency in mind. Hire talent who knows how to tame the AI beast, not just plug it in.
But most SMEs aren’t there yet. They buy the hype, ignore the math, and get blindsided when the invoice hits.
At the end, AI isn’t a magic cost-cutter. It’s a powerful tool with a high price tag, unless you build smart, measure relentlessly, and plan ahead. Otherwise, automation could become the most expensive mistake on your balance sheet.