AI Tokenomics: How to balance value and reduce risk

Every AI conversation I have eventually comes down to one of two mentalities: ‘AI is too risky’ versus ‘adopt at any cost’.  

That fight has today's companies split into two extremes. On one side, you've got the tokenmaxxers, the companies who, in seeking a competitive advantage, adopt AI rapidly, use it everywhere, and worry about the return (and downside risk) later (if ever). On the other side, you've got the blockers, the organizations that ban AI out of compliance and security fears, take the least risky position, and call that an AI governance strategy. Right now, neither side is getting it right. But as the market moves from the top of the hype cycle, we are starting to see a far more practical approach — tokenomics.

The economics of tokenmaxxing  

The tokenmaxxers are still operating on last year's directive: Use AI everywhere, bring it into every process. The rationale is that adoption itself builds capability, capability builds a competitive advantage, and by extension, value to the business.

A common tokenmaxxer pattern: Reaching for the most powerful AI model available regardless of what the task calls for, such as a simple research query getting the same treatment as a genuinely complex problem. They never stop to ask whether the task actually needs the most powerful option. They reach for it because it's there. Meanwhile, the token meter keeps climbing.

The problem with this strategy is that the real returns are opaque. In fact, a 2026 survey published by KPMG reported that only 35% of companies had visibility into AI operating costs, while a growing minority (24%) were under pressure to demonstrate value to investors. As long as tokenmaxxers keep spending money for no perceived return, eventually, is the CFO is eventually going to open the bill and ask 'why?' We may be in a new AI world, but basic economic rules will always win out.

The AI blocker's false sense of security

Then you've got the opposite reaction entirely: block it. Ban it. Take the safest possible position to avoid any risk. In extreme cases, the definition of AI is so broad it captures machine learning capabilities that have been in standard business tools for years. I’ve personally seen contracts from organizations that require any use of AI must undergo a complete risk review. When discussing one such contract with a renewing customer, there was a look of fear when I pointed out, “you do realize that by this definition you have been using AI for the past four years!”

Managing AI security: It’s all about the data!

‘Security’ is usually the justification for banning AI outright — and the risks of data leakage in ungoverned models are real. But using yesterday's cybersecurity lens, AI gets treated like a sandbox that needs to be locked down in case it goes rogue. They’re not asking the much more important question of whether the data going into the sandbox should be there in the first place.  

Here's what should really worry the blockers: when you ban sanctioned tools, people don't stop using AI — they just stop asking permission and move to unsanctioned tools. This has real security risks, with 43% of security incidents involving unapproved AI tools, according to IBM’s 2026 Cost of a Data Breach Report. Blocking it doesn't solve the data security and compliance problem. It just guarantees you won't see it coming.  

Where We're Headed: The move to AI tokenomics

While tokenmaxxers and blockers may be on divergent paths now, I believe we're on the cusp of a major shift that stands to bring the two sides closer together. And it won't be a slowdown in adoption. It'll be a rationalization, and one that will push the AI market toward an emerging set of cheaper, more specialized tools.  

Tokenomics strategy: Match the model to the task

Part of the challenge is that today's companies are overwhelmingly using frontier models, which are seen as ’better’ and therefore more secure, but are also more expensive. And here's what makes it worse: the AI companies know exactly what's happening. They're testing pricing, throwing discounts against the wall, watching what the market will actually bear, because they're figuring out the economics in real time too. The tokenmaxxers are footing that experiment, but the blockers are also willing to pay more for the models they see as being more secure.

The role of low-cost, specialized AI tools

But we're also seeing the emergence of cheaper, more specialized options built to close that gap. This generally comes in two forms. It means harnesses — tools built to understand the right model for a particular workload and apply it automatically. Often these specialized tools obscure AI but put business logic around it to make sure companies maximize that value, for example: a security analysis tool that picks the right model for the job, or a penetration-testing kit that does the ‘tokenomics’ for you behind the scenes.

The case for low-cost model adoption

The second low-cost option is in the emergence of cheaper models themselves: open-weights models you can download and run on your own infrastructure, or lower-cost competitors, even if they're a step behind on capability. This approach also better addresses the security concerns of the more conservative. You are not sharing data or moving it offshore as everything is contained within an infrastructure you control and manage. It may not have all the bells and whistles of the Frontier LLMs from the big names, but it gets the job done for a fraction of the price.

Learn more: AI Governance Solutions from RecordPoint

And these low-cost options are already gaining traction, with KPMG reporting 22% adoption rates of low-cost models in Q2 2026, compared to 15% in Q1.  

Together, these two trends are what's making it possible to match value with workloads, instead of defaulting to the most expensive, most secure-seeming option.

Managing model risk at the data layer

While value starts at the model, risk management doesn't. Instead, it starts with the data. To understand how, imagine a security research task that is run through a cheap, offshore hosted model: if the query is generic and the data behind it is already public, there's no sensitive data to expose, which means no real risk. Swap in customer data or trade secrets, and the risk flips entirely, regardless of which model is doing the processing. The model isn’t the variable. The data is.

That means the actual work of mitigating AI risk isn't picking safer models, it's in gaining a complete view of your data estate: classifying the data you have, knowing what's sensitive and what isn't, and being able to see and prove where each type lives and why it's there. But for most companies, AI data readiness has been a blocker, with Gartner predicting that through this year, 60% of companies will have abandoned AI projects because of data gaps.  

Prove AI value without sacrificing security

Once you understand your data, the false choice between security and cost disappears. Low-risk, public data can go to a cheaper model without concern. Sensitive data might require the use of local open weight options, or complex data justifies paying for a higher end frontier option. That's not a trade-off, it's a decision made once the data is understood, and it's the same decision whether you're optimizing for spend, for exposure, or for capability.

Which is really the point. The tokenmaxxers and the blockers are both still fighting over the model, with one extreme maximizing its use, the other restricting it, all because neither has done the work of understanding what's actually running through it. Get the data layer right, and that fight becomes unnecessary, and the required decision is obvious.

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