The Unstoppable Growth Of Generative AI (AI Outlook Part 1)
The digital creation of an image using Stable Diffusion.
When Tirias Research first forecasted global generative AI demand in 2023, we predicted token output would reach an aggressive 20 trillion tokens, such as a letter, word, or punctuation, by the end of 2024. That estimate was soon overwhelmed, as actual usage surged to a staggering 667 trillion tokens. The latest forecast now expects demand to grow 115x by 2030.
Disclosure: My company, Tirias Research, has consulted for IBM, Nvidia, and other companies mentioned in this article.
Growth drivers
The growth in generative AI usage was more explosive than even the most aggressive forecasts could anticipate. Then, a new demand surge began in September 2024 with the release of ChatGPT-o1, the first widely deployed "reasoning" model. Unlike previous generations, it didn't just answer questions, it reasoned through them, generating more thoughtful, logical, and nuanced responses. Reasoning required far more behind-the-scenes "reasoning tokens" per session, resulting in a drastic increase in token generation. In addition, user engagement skyrocketed. By the end of 2024, the time spent generating content via generative and reasoning AI models had grown by more than 22x compared to the previous year.
Overview of conditional computing and model sparsity
There was also an unprecedented and accelerating rate of innovation. From the introduction of transformers in 2017 to ChatGPT-1 in 2022 and reasoning models in 2024, the innovation timeline continues to accelerate. Advanced model architectures, such as mixture-of-experts (MoE), enable more efficient reasoning while keeping active parameter use low. Open-source models, such as Meta's Llama series, challenge closed-source dominance by offering lighter, faster alternatives that run locally on laptops and smartphones. And optimizations operational efficiencies, such as sparse attention and conditional computing, are resulting in more efficient models like DeepSeek R1 (introduced in 2025), which originally used only 37 billion active parameters per token compared to Llama's 405 billion or over 1 trillion in some closed models.
Token demand by the numbers
Tirias Research forecasts continued growth in the number of users, visit frequency, time spent, and AI-generated content. Additionally, with agentic APIs rolling out in 2025, AI agents will start autonomously chaining AI models together, forming thoughts, executing tasks, and collaborating with other services. Human prompting will no longer be the sole driver of AI activity once autonomous agents begin to generate usage on their own. As a result, the annual rate of token generation is expected to skyrocket from 677 trillion in 2024 to 2,092 trillion by the end of 2025 and 77,000 trillion (77 quadrillion) by the end of 2030.
Generative AI forecast 2024-2030
Simon Solotko, Senior Analyst at Tirias Research, explains: "The AI ecosystem is under unprecedented pressure. Multimodal capability, user demand, and agentic and multimedia workflows are advancing so quickly that even efficiency gains in compute hardware and software won't be enough to offset the surge in demand."
A 2028 snapshot of the forecast demonstrates that the use of AI assistants and agents is likely to be concentrated among a small number of providers. However, on the infrastructure side, AI models accessed via APIs are anticipated to drive a wide range of business and consumer applications by enabling AI capabilities for customer-facing service providers.
2028 forecast estimates of service providers token share and the token production infrastructure
The industry may consolidate into a natural monopoly similar to Google's dominance in Internet searches. Being the first to market with ChatGPT and wide brand recognition, OpenAI currently dominates the AI market for AI models and token generation. Whether OpenAI retains its lead remains uncertain.
Future Trends
Larger models will continue to grow in size and complexity, outpacing hardware improvements. The largest models already exceed the memory of any single accelerator, requiring clusters of GPUs and entire racks to process tasks. However, innovations in distillation and efficiency will aid in scaling down to smaller, more targeted models. The introduction of DeepSeek represented a significant leap in model efficiency, resetting the performance baseline.
AI Agents will become pervasive. Industry leaders, such as Nvidia's Jensen Huang and IBM's Arvind Krishna, foresee every employee working with multiple AI agents. Some agents will live in machines, others in virtual spaces, and still others in physical robots. AI agents will also begin to collaborate.
AI competition will increase. As models mature, differentiation is no longer just about size or speed; it encompasses a broader range of factors. Services are integrating AI models into workflows, APIs, and interactive applications, pushing toward end-to-end task automation and entertainment. At the same time, cost pressures are forcing every player to adopt cutting-edge techniques for faster training, improved inference, and lower computational cost. This competition goes beyond the enterprise; AI is now shaping geopolitics as countries race to innovate.
In addition, AI will continue to evolve. By the end of the decade, AI-generated images and video could overtake text as the primary source of AI-generated content and driver of future compute demand. Much of this content may be created on edge devices. Media content generation, combined with autonomous AI agents and machines, will usher in the next wave of AI.
Final Thoughts
Unlike past technology adoption curves, generative AI doesn't appear to be slowing. Rapid improvements in both capability and efficiency are accelerating demand. As agentic AI expands beyond human usage, the number of "users" of generative AI will multiply exponentially.
I will discuss the rising AI demand for images, video, autonomous agents, and autonomous machines, as well as the global infrastructure requirement and total cost of operation (TCO) of generative AI in future articles.
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