Generative AI’s Stacked Future: Power, Platforms, and the New Economics of Intelligence

In the turbulent landscape of contemporary technology, perhaps no domain is as simultaneously promising and fraught with complexity as generative AI. The rise of models like OpenAI’s GPT-4, Google’s Gemini, and Meta’s Llama has shifted how society interacts with information, media, and itself. Yet beneath the headlines about human-like chatbots and AI “co-pilots,” a deeper story is unfolding, one involving shifting business models, uncertain economics, legal gray zones, and a race for technological supremacy reminiscent of prior platform wars, but with much higher stakes.

Generative AI’s rapid ascent is undeniable. Records already tell part of the story: OpenAI’s ChatGPT became the fastest-growing consumer app in history; tech giants have funneled billions into large language models (LLMs) and the computational infrastructure to support them. In the process, an entire new “stack” of AI-centric products and services is emerging, similar to the way the early PC and internet eras gave rise to their own ecosystems. At every layer of this stack, entrepreneurs sense both the possibility of new monopolies and the specter of being outmaneuvered by larger, resource-rich players.

To understand where all this could lead, we must look beyond technical wizardry and investor euphoria to the fundamental dynamics at play.

The Economics: (Not) Printing Money

There’s a palpable tension at the heart of the generative AI boom: the technology is expensive to build and run. Unlike the traditional software-as-a-service (SaaS) boom, where the primary costs are software development plus hosting, each interaction with a modern LLM consumes significant computing resources, especially given the enormous sizes of these models. As financial analysts have noted, the cost to serve a single chat or image can dwarf the equivalent cost for a search query or social media post.

This economic reality is a double-edged sword. On the one hand, it raises barriers to entry for would-be competitors, potentially entrenching the lead of companies like OpenAI/Microsoft, Google/Alphabet, and Meta, all of whom possess global-scale data centers and investments in AI-focused hardware like Nvidia GPUs. On the other hand, brittle or negative gross margins challenge open-access deployments and the very idea of “AI for everyone.” As one VC mused in a recent breakdown, “you don’t have the endless scale and near-zero marginal cost that made cloud SaaS so profitable.”

As a result, the industry is seeing massive investment more akin to early telecom or electric utilities than to pure software. A “winner-take-most” dynamic in foundational models is possible but not guaranteed, smaller players may still thrive serving verticals, or as “layer two” startups building on top of baseline models with proprietary tweaks and integrations.

Barriers to Entry and the Open/Closed AI Divide

A generational debate is underway: should large models remain closed, guarded by their creators as proprietary IP, tightly controlled APIs, and license agreements? Or should they be open-sourced, spurring faster innovation at the (potential) cost of less oversight?

Meta’s release of Llama 2 and Llama 3 under permissive licenses has energized a global open-source AI movement. Hobbyists, research institutions, and startups have leapt to build applications and custom models atop Llama, arguing that decentralized innovation is crucial for trust, transparency, and security. Meta, for its part, claims this is a play to leapfrog competitors through broad adoption, even as questions swirl about the broader consequences.

The counter-argument, from the likes of OpenAI and Google, is rooted in the dual perils of misuse and commercial risk. With models that can already draft code, mimic voices, and compose plausible news or disinformation, bad actors have more tools than ever. Concerns about child exploitation, scams, and deepfakes have prompted calls, even from AI’s pioneers, for careful gating of advanced models. Meanwhile, the cost to build state-of-the-art LLMs (including licenses to vast swathes of copyrighted data) further bolsters the closed-model approach.

Foundation Models as Platforms: Lessons and Cautions

It is not lost on industry observers that the debate about the AI “stack” is also about control and rent-seeking. The battle echoes those fought in the eras of mainframes, PCs, operating systems, and cloud APIs. There is a powerful incentive for whoever controls the dominant LLMs to own the interfaces to developers, opening doors to lucrative platform economics: think of Microsoft with Windows, Apple with iOS, or Amazon with AWS.

Yet there are risks, too. History shows that platform winners accrue power not just through technical superiority or capital, but via network effects, ecosystem lock-in, and smart developer incentives. Already, a Cambrian explosion of “AI-powered” apps has arrived for everything from customer service bots to automated contract review. Most are built atop a handful of foundation models (OpenAI, Anthropic, Google, Cohere, Meta), channeled through APIs that can be cut, repriced, or modulated at any time. The fates of countless startups now hinge on the strategic moves of the model superpowers, a lesson driven home by abrupt pricing changes and new product launches by OpenAI this year.

At the application layer, this instability has spurred some founders to seek “model independence” by training custom models or leveraging open-source alternatives, wary of being “API prisoners.” The challenge: smaller models tend to lag in raw performance, limiting their utility in high-stakes domains. But for certain niches, domain-specific chatbots, on-device assistants, enterprise workflows, they may well suffice.

Legal and Social Hazards: Copyright, Hallucinations, and Trust

No story about generative AI is complete without grappling with its legal thickets. The large datasets used to train LLMs criss-cross the open web, often ingesting copyrighted material without explicit permission. Lawsuits from news organizations, book publishers, and artists are mounting, raising questions about fair use, attribution, and liability. As courts wrangle with definitions, some tech players are negotiating direct licensing agreements, an echo of the streaming music wars over a decade ago.

Meanwhile, the issue of “hallucinations”, confidently wrong or misleading outputs, remains a core technical and trust challenge. As LLMs are deployed for everything from search to customer interaction, the boundary between suggestion and fact blurs. This instability, paired with the emergence of AI-generated misinformation, is already fueling calls for new regulatory frameworks across continents.

The Way Forward: Navigating an Uncharted Age

What’s the lesson for the tech industry, policy-makers, and society at large? First, that generative AI will reshape how information is produced, trusted, and monetized, likely in disruptive fits and starts. We are still early in the process; the contours of winners and losers, open and closed, regulated and rogue, are far from settled.

Entrepreneurs would do well to study history: platform shifts favor those who move fast, but moats are fickle and easily replaced in periods of rapid innovation. Incumbents, flush with capital and cloud, are not invincible, but new forms of lock-in, data, compute, and trust, may replace the intellectual property moats of the past. Meanwhile, users and regulators must demand both transparency and accountability, lest the next phase of the internet be dictated by a handful of inscrutable black boxes.

Generative AI’s story is still being written. Its promise is enormous, but so are its risks and its unfinished business. Today’s glory could be tomorrow’s cautionary tale, but amid uncertainty, one thing is clear: nobody can afford to sit on the sidelines.