AI companies resort to aggressive monetization models
The reality is simple: AI is expensive and monumentally resource-intensive. Large language models like ChatGPT, Claude, and Grok require enormous computational power to process even basic user prompts. GPU demand alone drives up infrastructure costs, and that’s before you account for engineering, model training, and continuous product iterations. Users get powerful tools that feel free. But to the companies building them, it’s anything but.
Tens of billions of dollars have already flowed into this space from venture capital, private equity, and large corporate backers. These investors expect returns, and soon. The go-to-market strategy for AI platforms has largely been freemium: offer a strong free tier to drive user adoption and layer in chemistry-driven monetization systems over time. That time has come.
Startups and incumbents are layering multiple models to generate revenue. Subscriptions are a starting point, but those only go so far. Custom API rates add value for enterprise-scale operations. Product placement, affiliate retail connections, and even paid editorial prioritization are in full rollout. More playbooks are coming every quarter.
Companies building LLMs are competing on sustainability. The AI platforms that survive will be the ones smart enough to scale without sacrificing integrity or transparency.
Paid prioritization reflecting a “Payola” model
Some of the biggest AI companies are now integrating paid content prioritization directly into their platforms. This is about strategic partnerships that decide whose information reaches users first.
OpenAI has established what it calls the “Preferred Publishers Program.” More than 30 publishers, including The Wall Street Journal, Vox, Condé Nast, and The Atlantic, have signed on. They provide access to their content in exchange for exposure, premium placement in chatbot answers, and financial compensation. Their links show up more frequently, their names get highlighted, and they get referral traffic, engineered at scale.
Perplexity AI is doing something similar. It has content deals with organizations like Le Monde, The Los Angeles Times, and Der Spiegel, giving these publishers a more visible role in AI responses. These companies also earn a portion of ad revenue in return.
Here’s what matters: users aren’t told this. They assume AI responses are structured by quality or accuracy. In reality, these responses are shaped by contracts, revenue-sharing, and visibility guarantees.
From a business standpoint, this changes information distribution. If you’re relying on search traffic or AI summarization to represent your brand fairly, understand what’s happening behind the scenes. Every marketing officer, every media executive, and every content strategist should be asking: is our content prioritized, or are we absent from the conversation? Because this isn’t a passive feed, it’s a filtered feed, weighted by deals.
Affiliate link marketing through in-chat purchases
OpenAI is now moving into direct affiliate commerce inside ChatGPT. This means users won’t just ask questions, they’ll buy products, right within the chat. No need to leave the interface, no need to search somewhere else. The intent is to reduce friction and monetize recommendations in real time.
Sam Altman, CEO of OpenAI, has reportedly introduced a 2% affiliate fee model on purchases initiated through ChatGPT. Product listings will be linked through partner merchants. When a user decides to buy, OpenAI gets a cut.
This direction has major implications. It gives OpenAI a financial reason to surface certain goods or recommend certain services. The architecture of these systems is built to optimize for conversion.
For executives managing ecommerce, digital retail, or competitive positioning online, this is where algorithmic influence becomes transactional. Companies who form early partnerships gain exposure in a controlled environment that will define buyer behavior for years.
User trust, of course, is a factor. If AI tools openly disclose affiliates, they’ll retain some transparency. If they don’t, the perception of bias will become a real issue. Leaders designing customer experience and brand strategy need to plan now, for both visibility and verification, before affiliate-driven AI becomes the norm across every commerce interaction.
Service quality degradation through a “Shrinkflation” model
Performance costs money. AI companies are quietly adjusting product tiers to manage escalating GPU and infrastructure overheads. One of the emerging strategies is reducing quality in free-tier services, less accurate outputs, shorter or flatter responses, fewer advanced capabilities. This cuts compute demand and preserves capacity for paying subscribers.
Anecdotal evidence from user communities suggests these changes are already happening. Free users often report slower responses, less relevant answers, or limited access to new features, while premium-tier users gain faster speeds and better model access. These aren’t cosmetic differences. They reflect deliberate resource allocation, and it’s pushing users toward paid subscriptions.
From an executive standpoint, it’s a clear signal. Tier separation is widening. If you’re deploying AI within your organization or integrating external LLMs into your product stack, understand what your tier actually offers. Not all users receive the same quality of model output. This impacts reliability, customer perception, and outcome consistency.
Companies building AI products also need to be precise with expectations. As free tiers degrade and premium tiers expand, clarity around what’s included becomes a strategic requirement in enterprise contracts. Under-delivering because of misaligned tiering will erode trust fast.
Expanding monetization channels amid rising operational costs
AI is powerful, but it’s not cheap to run, not even close. What we’re seeing now is AI companies layering on monetization streams across every possible point of user engagement. Subscriptions, API fees, ad integration, branded partnerships, on-platform purchases, content licensing, even consulting. That’s the current reality.
And it’s still not enough.
These models burn massive amounts of capital. Model training alone can exceed hundreds of millions of dollars per version. Deployment scaling, real-time inferencing, memory requirements, all of it scales with usage. Monetization isn’t optional, it’s urgent.
For leadership, understand this: the AI providers you’re working with or integrating into your stack will keep experimenting with monetization. That means pricing volatility, tightening feature sets, and likely consolidation over time. Supply of compute is limited. Return on investment will take precedence over experimentation.
If you’re making procurement decisions, factor this financial pressure into vendor selection, contracting timelines, and future pricing models. Choose partners with aligned incentives, transparent models, and credible roadmaps. Don’t base critical infrastructure decisions on services that only work in a non-revenue environment. That era is ending quickly.
Key takeaways for decision-makers
- Monetization pressure is reshaping AI products: AI platforms are under intense financial pressure due to high compute costs and massive VC funding. Leaders should anticipate further monetization layers that may impact transparency, neutrality, and user trust.
- In-conversation ads are changing user trust dynamics: Embedded advertising inside AI-generated responses, already in motion at xAI and Amazon, blurs the line between information and promotion. Executives must assess the risk to brand perception when leveraging or participating in these models.
- Paid content deals influence perceived objectivity: Platforms like OpenAI and Perplexity prioritize partner content in their AI responses without disclosing these affiliations to users. Decision-makers in media and content should evaluate the long-term tradeoffs between traffic boosts and editorial independence.
- Affiliate-driven AI commerce shifts customer journeys: OpenAI’s in-chat shopping experience introduces a financial incentive to recommend specific products, potentially skewing results for profit. Brands should consider early integration to maintain visibility while ensuring ethical alignment.
- Tiered service models reduce quality for non-paying users: Free tiers of AI tools are experiencing downgraded performance to reduce infrastructure load and drive conversions. Organizations depending on free or lower-tier access should reassess service expectations and upgrade where precision matters.
- Monetization expansion will shape long-term partner viability: AI companies are deploying overlapping revenue strategies, from subscriptions to content licensing, yet still face sustainability gaps. Enterprises should align with providers offering stable, transparent monetization to reduce strategic risk.
