When OpenAI launched GPT-5 last week, the reaction was muted. No flashy new tricks or “wow” demo moment. If you stopped there, you might think nothing’s really changed.
But the real story is bigger and far more important for leaders. OpenAI didn’t just release an updated model, they triggered a collapse in the cost of top-tier intelligence across the market. That cost shift will accelerate innovation in ways we’re only beginning to imagine, and it’s happening already.
It’s important to note that there are two main ways people and companies use GPT-5.
- Through the ChatGPT app, individuals and teams interact with the AI directly, writing prompts, asking questions, or creating content. It’s plug-and-play, no coding required, and now GPT-5 is the default model even for free users (with some usage caps).
- Through the API, companies connect GPT-5 to their own systems or products so it can power customer support tools, automate large-scale analysis, or run AI features inside other apps.
The headline here is that OpenAI cut GPT-5’s API price to $1.25 per million input tokens and $10 per million output tokens numbers that would have seemed impossible not long ago. In simple terms, tokens are chunks of words. A million tokens of input is roughly 750,000 words, which is the equivalent of several full-length books. “Input tokens” are the text you feed into the model, and “output tokens” are the text it generates in response.
The new API pricing makes a big difference for large-scale, embedded use cases. Companies can now process massive amounts of data, run more experiments, and serve more customers for a fraction of the cost. Workloads that once felt budget-breaking are now affordable, opening the door to AI innovation at an entirely new scale.
Combine this new cost structure with the decision to make GPT-5 the default in ChatGPT, and you have a dual shift: high-powered AI is dramatically cheaper for heavy users and instantly accessible to hundreds of millions of people, including your competitors. Intelligence that once required careful budgeting and scarce expertise is now abundant and that abundance changes the game entirely.
When intelligence gets cheap, the game changes
Just a couple of years ago, AI was expensive and resource-intensive, so leaders had to be selective about where and how they applied it:
- Licensing and compute costs were high: Running large models at scale through an API could cost thousands of dollars a month, even for modest use cases.
- Access was limited: The best models were behind higher subscription tiers or enterprise contracts.
- Specialized expertise was needed: Integrating AI often required dedicated data scientists or engineers, which added cost and slowed speed to value.
- Budget trade-offs were constant: Leaders had to choose a few high-priority projects for AI investment and delay or reject others.
In other words, leaders had to ration AI usage just like any other scarce, expensive resource.
In a low-cost world, the constraint shifts from budget to imagination. The central question stops being “Can AI do this?” and becomes “How can we reimagine the way we work if this is possible everywhere?”
That’s when innovation accelerates. Experiments that once required hard trade-offs can now be run in parallel, testing ten ideas for the cost of one. AI copilots can quietly monitor, reconcile, and draft decisions in real time, expanding your team’s capacity without adding headcount. Entire archives or research libraries can be parsed in minutes. Intelligence can be embedded into the devices your people already carry, putting expertise within reach at any moment.
Two ways leaders commonly get this wrong
For some, the old assumption still holds: AI feels too expensive or too specialized to deploy widely. Their only exposure has been high-cost pilots, niche specialist teams, or consulting projects where each experiment felt like a big-ticket gamble. That may have been true last year it’s not true today.
For others, the issue isn’t what they say, it’s what their strategy reveals. They’ll tell you they know AI is now cheaper and more accessible but they still budget and resource it like a premium feature. It’s reserved for high-priority initiatives or “innovation” workstreams, rather than being built into core workflows and systems.
In both cases, the result is the same: they’re underestimating how radically the playing field has changed. Intelligence is now abundant. The gate is no longer money it’s imagination and execution speed. The organizations that win will be those that treat AI not as an experimental add-on, but as infrastructure integrated deeply enough that the question isn’t whether to use AI, but how to keep evolving it as the cost curve continues to drop.
Strategies built without this shift in mind risk missing opportunities in a competitive landscape that’s already moving forward. The advantage now belongs to those who experiment, learn, and adapt faster than the cost curve drops.
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