Token Shock: Why Enterprise AI Economics Are About to Change Forever
In 2025, enterprise AI was defined by experimentation. Teams piloted Copilots. Departments sandboxed RAG. Innovation groups toyed with LLMs to see what stuck. During this phase, budgets were written off as “learning investments”. Token costs were treated as background noise and rounding errors that CFOs barely noticed.
That is all about to change.
Recently, I had a candid conversation with the CIO of a billion-dollar-revenue public company. Their team has been aggressively rolling AI into real production workflows – customer support, internal knowledge systems, analytics, and departmental automation. When I asked how they were thinking about cost, the answer was stunning:
“Over the next two years, we expect our token costs to increase by roughly 25x.”
At that scale, token costs are no longer an engineering detail. They become a line item that materially reduces financial margins. And this company is not an outlier.
From Prototype to Production
The shift we’re seeing is structural. 2025 was the year enterprises asked “Can we use AI?” The next several years will be about “How do we sustain running AI at scale?”
Production AI is a completely different beast than the prototypes in your innovation lab:
Always-On: Workloads shift from sporadic testing to continuous, 24/7 inference.
Ubiquitous: Usage doesn't just grow; it saturates every department, geography, and workflow.
Demanding: Latency, availability, and reliability are paramount. SLAs become non-negotiable and expensive to guarantee.
A single successful internal AI application can quietly generate millions – or tens of millions – of tokens per day. Multiply that across dozens of use cases, and costs escalate rapidly. Enterprises that built their early AI strategies around premium, API-only models—such as OpenAI’s ChatGPT or Anthropic’s Claude, often accessed via platforms like AWS Bedrock, will face a hard economic reality – because premium intelligence comes with premium pricing.
The Hyperscaler Trap
This economic pressure isn't a bug; it’s a feature. The hyperscalers – Amazon Web Services, Google Cloud, and Microsoft Azure – have a clear incentive to anchor enterprise AI strategies to their most advanced, proprietary models.
The result?
High per-token costs
Tight coupling between applications and specific models
Significant switching costs once AI is embedded into business processes
For many enterprises, the choice increasingly looks like this:
Continue paying premium prices as token usage scales
Absorb the pain and risk of re-architecting AI systems later
Neither is attractive.
Open Source Models: Why "Good Enough" Wins
But there’s another way.
While proprietary frontier models still lead on absolute benchmarks, the gap is often overstated for real-world enterprise workloads. Many use cases – summarization, classification, extraction, domain-specific Q&A, internal copilots – do not require the most advanced reasoning model available.
In fact, multiple independent benchmarks suggest that open source models can outperform closed models on applied, task-specific evaluations. Moreover, in many cases these models operate with lower latency, which is critical in real-world, production applications. Open source models also bring other advantages that matter deeply to enterprises:
Lower and more predictable cost per token
Customizability through fine-tuning and adaptation
Portability across cloud, private cloud, and on-prem environments
Stronger data control, increasingly critical in regions like EMEA.
Rather than being locked into a single hyperscaler ecosystem, enterprises gain architectural flexibility.
The Future Is Routing, Not Betting
The most cost-effective enterprise AI strategy is not about choosing one model. It’s about routing. Different AI tasks have different requirements. Some truly benefit from premium reasoning. Many do not. A smart architecture dynamically routes workloads to the lowest-cost model that meets the task’s quality threshold.
This approach delivers:
Massive Cost Savings, dramatically lowering your blended cost per token
Vendor Independence, so you are no longer beholden to a single API's uptime or pricing
Future-Proofing, enabling you to more easily swap models in and out as the market evolves – without breaking your code.
It’s no coincidence that AI-native tech companies with massive inference demand are already leaning into this model. AI neocloud providers like Groq and Baseten are growing precisely because they enable flexible, efficient inference without hyperscaler lock-in.
Margin Is the New Battleground for Enterprise AI
As AI moves from novelty to necessity, economics will matter more than hype.
Enterprises that act strategically before token costs quietly begin to erode margins will be in a fundamentally stronger position. Those that wait may find themselves locked into architectures that made sense at pilot scale, but fail catastrophically at production scale.
Token shock is coming. The question is whether enterprises will be prepared – or whether they’ll discover too late that intelligence, at scale, has a very hefty price tag.
