Generative AI adoption and long-term cost escalations
Right now, generative AI feels like a breakthrough, and it is. The potential to reshape how businesses operate, make decisions, learn from data, and create value is real. But let’s not kid ourselves: none of this comes free. As enterprises soak genAI deep into their systems, training it on proprietary data, folding it into workflows, connecting it to everything, the cost structure changes. What you’re building is dependency. And that’s where future costs could become a real problem.
When you tailor a model to your business, fine-tune it to match your brand voice, your customer data, your internal knowledge, you’re doing more than optimization. You’re creating friction against switching. That technical debt accumulates quickly. Retraining a model on new infrastructure later? Rewriting integrations? Costs multiply, not linearly.
Vendors are aware. As generative AI becomes essential infrastructure, pricing will shift away from simple usage-based models. You’ll pay based on the value the AI delivers, not how much it costs to run. It’s good business, for them. The companies that go all-in on a single model or closed platform may wake up to find they’re writing open checks to stay in business.
Don’t make that mistake. Structure your deployment plans now to anticipate this. You want negotiation leverage later, not regrets.
Manuel Kistner, CEO of New Gravity, put it bluntly: genAI could follow the same playbook as early platform businesses that started cheap to gain adoption, then flipped the switch on pricing once users couldn’t afford to leave. Aaron Cohen, an AI consultant, was even more direct. As models get stronger, costs will get worse. And unless your model strategy is built to flex, you’ll pay.
Historical precedents indicate price volatility in digital innovations
Look back at digital innovation over the last two decades. When markets shift, change happens fast, and prices are the first to react. We’ve already seen this in multiple sectors. Web browsers went from paid products to free overnight. Encrypted traffic, once locked behind $300 SSL fees, is now standard, thanks to Let’s Encrypt. Video calling used to require expensive telecom charges. Skype changed that. These weren’t tweaks, they were total resets.
But genAI is different. The cost of generating output is minuscule. Practically zero at scale. For now, that keeps a lid on prices. But it won’t stay this way unless competition and business structure do. If vendors can deliver substantially more value, and they will, they’ll capture that value in pricing. Not in compute costs. You’ll pay more because your business couldn’t operate without it, not because it costs them more to provide.
Dev Nag, CEO of QueryPal, brings solid examples here. He pointed out that point-and-shoot cameras were wiped out by smartphones. In 2010, manufacturers shipped 109 million units. By 2023, it was under 2 million. Did people take fewer photos? No. They took exponentially more. The value didn’t disappear, it moved. And when value shifts that fast, business models don’t stabilize; they transform.
So expect volatility. Business leaders love predictable costs. That’s not where we are with AI. The economics of genAI are still being defined. If you treat it like a traditional software tool, you’ll miss the shift and potentially get caught on the wrong side of a pricing curve that moves much faster than expected.
Forecast differently. Build flexibility into your cost models, and ensure you’re not embedded too deeply into pricing assumptions that won’t last.
The core risk of vendor lock-in in GenAI deployments
There’s a critical risk in the current rush to adopt generative AI at scale: vendor lock-in. It’s not a theoretical concern. It’s an operational one. When a business unit picks a specific genAI model, starts feeding it sensitive training data, and customizes it around proprietary systems, that relationship becomes deeply rooted, fast. Integration happens across departments. Teams become familiar with the tools. And replacements suddenly look expensive, slow, and disruptive.
This creates dependencies you can’t afford to ignore. Once you’re heavily invested in a single platform or vendor, options narrow. Not just in cost. In flexibility. In innovation. In negotiating terms. Many enterprises are now building on top of APIs from hyperscalers like AWS, Google, or OpenAI. On paper, it’s about productivity. But functionally, it’s building ecosystems that lock you in.
Stephen Klein, CEO of Curiouser.AI, put it into context. He described this as a model where companies are essentially renting, not owning, their AI capabilities. You’re constantly paying to access capabilities you helped configure. And if the vendor decides to hike pricing or shift their model architecture? You have limited recourse, because what you built is deeply tied to them.
The bigger your deployment, the more tangled that relationship becomes. If you’ve invested millions training a model with your data and embedded its outputs into your applications, switching vendors later isn’t just a cost issue, it’s a multi-quarter project with high execution risk. Most businesses aren’t prepared for that.
You need to plan for optionality now. Manage against over-exposure. Avoid building in ways that can’t be unwound. Keep vendor diversity in your stack and design with portability in mind.
Open-source and multi-model strategies as a countermeasure
If long-term flexibility matters to your business, and it should, then open-source models and multi-vendor strategies are worth serious consideration. They may not be as polished out of the box, and yes, they require more internal work. But that trade-off gives you something critical: control.
With open-source models, you’re not waiting on a vendor to release updates, adjust pricing, or clarify licensing. You’re not boxed into a decision that no longer fits your growth path or cost targets. If structured well, these systems can scale, adapt, and evolve without hauling the full cost of commercial licenses or restrictive platform architectures.
Stephen Klein highlighted the upside, and reality, of open source: it takes work. You’re assembling and refining in-house. You need people who understand how to optimize models, align them with internal data, and maintain performance as workloads scale. It’s a technical lift, no doubt. But many teams are already doing that level of customization with closed models. The key difference is that with open-source systems, those investments pay longer-term dividends.
Companies that build multi-LLM compatibility into their infrastructure, enabling workloads to shift across providers or models, also gain leverage. If one vendor changes pricing or alters terms, you have the option to move. Not in theory. In execution. That repositioning power can keep pricing accountable and reduce exposure to lock-in.
The rise of models like Meta’s Llama gives the ecosystem alternative paths for customization without relying on centralized providers. That freedom can scale across business units without inflating cost over time or eroding strategic control.
Bottom line, if you want long-term control over AI costs and outcomes, building in open adaptability isn’t optional. It’s foundational.
Optimistic perspectives on interoperability and manageable price hikes
Not everyone’s expecting genAI costs to spiral out of control. There are solid reasons to believe some level of pricing stability is possible, at least for now. One of those reasons is interoperability. Many enterprises are choosing to integrate AI tools using lightweight layers that don’t hardwire their entire tech stack into a single vendor. That gives them options to switch if pricing becomes a problem.
James Villarrubia, former head of digital innovation and AI at NASA and a presidential innovation fellow, sees this as an important difference from earlier fears around cloud or SaaS migration. He highlights how enterprise systems are being deliberately designed to interact with multiple APIs, many of which have been built to support OpenAI’s format. Because vendors rushed into this space using similar structures, switching between them isn’t as expensive or complex as it could be.
Another positive signal is the shift toward foundational models with reduced customization needs. Enterprises used to fine-tune heavily. Now, according to Villarrubia, more are moving directly to core models like Meta’s LLaMA and applying minimal additional training. That trend lowers both the cost of model updates and the risk of platform lock-in.
It also changes how companies measure switching costs. If most of your intelligence layer runs on generic APIs and your models require little retraining, pivoting doesn’t come with massive operational drag. That shifts bargaining power back toward the enterprise.
There’s also a strategic side to this. Model makers know that aggressive price hikes could backfire and slow enterprise adoption. If providers want companies to upgrade to newer, more powerful models, they’ll need to keep pricing accessible. Villarrubia argues that simplifying the architecture and encouraging modernization is in the vendors’ best interests, which means they won’t benefit from restricting affordability.
So it’s not all pressure and downside. If enterprises continue designing systems for portability and limit fine-tuning, they can maintain flexibility and keep vendors honest.
Competitive dynamics may temporarily contain extreme pricing
The current genAI market is still competitive. A lot of players, fast innovation, and model diversity are keeping prices in check. That’s helping enterprises negotiate better deals and delay serious cost pressure. But that window may not last.
Market consolidation is inevitable. You can’t sustain the current level of fragmentation indefinitely. When the top few vendors begin to dominate, pricing flexibility disappears, and with it, optionality. Planning needs to acknowledge that future. Strategic positioning today protects cost structure tomorrow.
Still, competition is real for now. Vendors are cutting rates, launching new features rapidly, and pushing open-weight models to gain market share. Enterprises should be taking advantage of that. Negotiate shorter-term contracts with re-evaluation clauses, leverage multiple providers where possible, and implement tools that make shifting traffic between models viable.
Dev Nag, CEO of QueryPal, has already highlighted how low marginal cost products can shift markets suddenly. But it’s important to see that genAI vendors won’t necessarily follow the same trajectory, especially when models are tightly coupled with enterprise data and use cases. The fundamental economics of digital services provide downward pressure, but the business models being deployed may create upward pricing momentum if unchecked.
In short, executives should capitalize on today’s competition while preparing for reduced choice later on. Build pricing flexibility into infrastructure. Don’t assume current dynamics will hold. The companies that lock into rigid relationships during this phase could find themselves stuck when the market consolidates and prices shift.
Use the competition wisely. It’s not just about lowering immediate costs. It’s about ensuring long-term leverage in a shifting landscape.
Risks associated with long-term contracts in a nascent market
Locking into a long-term contract for generative AI solutions may seem like a way to secure predictable costs. But in a market evolving at this speed, it can create more risk than stability. GenAI products haven’t been tested over long time horizons. Core models are still rapidly improving, infrastructure demands are shifting, and competitive pricing hasn’t yet stabilized. Committing to fixed terms in this environment can result in overpaying for outdated value, or being stuck with limited performance when superior options become available.
James Villarrubia, former head of digital innovation and AI at NASA, spoke to this directly. He cautions against five-year contracts for technologies that have barely existed that long. The concern isn’t theoretical, it’s operational. Too much can change in that period, including model performance, integration pathways, and market dynamics. Committing to static pricing in a market that doesn’t yet operate on stable terms makes it harder to course correct when needed.
Enterprises may feel pressure to secure “early mover” deals. But the cost of flexibility often outweighs the temporary benefit of a bargain. Vendors will push for longer commitments to lock in revenue and grow their footprint. That’s not necessarily in the best interest of the buyer, especially when flexibility might be more valuable than short-term discounts.
Instead, businesses should push for contracting terms that reflect the state of the technology: short-cycle agreements, dynamic pricing tied to outcomes, and evaluation checkpoints. For organizations already experimenting with multiple models or platforms, tying themselves to one path for too long eliminates the option to capitalize on future innovation.
This is not about avoiding commitment, it’s about staying aligned with technological change. Business leaders who want to lead in AI adoption must structure supplier relationships to support rapid advancement, not resist it. Price certainty is important, but so is deployment agility. Overcommitting early risks falling behind just when the tools finally mature.
Final thoughts
Generative AI isn’t just another tool you plug in. It’s infrastructure-level tech that’s already reshaping how businesses operate. The upside is real, faster outputs, smarter processes, scalable creativity. But with that power comes complexity you can’t ignore. Costs won’t stay flat. Dependency will grow. Vendors are positioning themselves to capture value, not just deliver it.
As a decision-maker, your job isn’t just to adopt AI, it’s to adopt it on your terms. That means setting the framework for flexibility now. Don’t wait for pricing pressure to hit before you think about switching paths. Build infrastructure that gives you leverage, not just speed. Design AI strategies that include optionality, not vendor dependence. Push for contract terms that reflect the pace of innovation, not legacy pricing models.
You don’t need to slow down, but you do need to stay in control. The shapes of genAI costs, capabilities, and competition are still changing. Make sure your organization isn’t just a passenger. Be the one steering.
