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A massive shift is underway as the artificial intelligence industry pivots from obsessing over large pre-training investments to a new frontier: optimizing inference. This shift is transforming the economics of AI, paving the way for new opportunities in innovation and competition.
The early days of the AI revolution were marked by a simple philosophy: bigger is better. Companies poured billions into training increasingly large models, believing that increased scale would inevitably lead to improved performance. While effective, this came with astronomical costs in computing power and energy consumption.
Now, we’re witnessing a more nuanced evolution. Just as humans didn’t evolve larger brains in the last 5,000 years, instead developing tools and social structures to enhance their practical intelligence, the AI industry is finding ways to do more with less. The focus has shifted from raw computational power to the ingenious application of existing resources.
The Inference Renaissance
This new era is exemplified by the recent developments from GPU vendors like SambaNova, Groq, and Cerebras. Their breakthroughs allow for the execution of complex AI workflows in the time it previously took to process a simple prompt. This leap in inference speed is akin to giving AI the ability to think and react at human speeds – or faster.
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The economic implications are profound. Faster inference doesn’t just mean quicker responses; it enables entirely new applications of AI that were previously impractical due to latency issues. From real-time language translation to instant complex data analysis, the possibilities are expanding rapidly.
The Pricing Revolution
This is not just limited to hardware. Even the giants of the AI world are adapting. OpenAI, once focused primarily on training ever-larger models, has dramatically reduced the cost of using its GPT-4 class models. Output token prices have plummeted from $60 per million at launch to just $10 today, while input token costs have seen an even more dramatic 12-fold decrease.
These price reductions are not just about making AI more accessible. They shed light on a fundamental change in how value is created in the AI economy. The ability to quickly and efficiently process information is becoming more valuable than the raw size of the model itself.
From Models to Systems
OpenAI’s o1, reflects this new direction and is referred to as a “system” unlike previous large language models – one that employs planning and reflection during inference time to improve the quality of its responses. This mirrors how the human brain constantly uses feedback to refine its “draft predictions” of the world.
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Shifting from static models to dynamic, self-improving systems represents a new paradigm where it’s no longer just about what a model knows but how quickly and effectively it can apply that knowledge to novel situations.
The Tool-Driven Intelligence Boom
Just as the development of tools catapulted human ancestors from savanna-dwellers to world-shapers, the integration of specialized tools is amplifying the capabilities of AI systems. We’re moving beyond simple question-answering to complex, multi-step problem-solving.
This enables AI to tackle tasks that require not just knowledge but also strategy and creativity. From AI coding agents that can fix LLM’s coding errors to solve real-world programming tasks to Sakana’s “AI scientist” that can plan and execute multi-stage research projects, we’re seeing the emergence of AI systems that don’t just respond but emulate feedback loops that are similar to human thinking.
The Future—Collaboration, Ingenuity, and Human Alignment
As we navigate this new world of AI, winning is no longer guaranteed by having the biggest model. Instead, success will come to those who can most effectively leverage inference optimization, tool integration, and agentic workflows.
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The implications extend far beyond tech, with AI becoming more efficient, capable, and further integrated into daily life. From personalized education to hyper-efficient supply chains, the potential applications are boundless.
Importantly, this shift towards inference optimization and tool-driven intelligence presents a more promising and potentially safer future for AI development. Rather than a world where ever-larger models automatically become more intelligent in mysterious and potentially uncontrollable ways, we’re moving towards a more familiar and manageable paradigm for humans.
The focus on tools, workflows, and collaborative problem-solving mirrors concepts humans have refined for thousands of years. Humans have also been able to deal with the accelerated speed of computation, as modern GPUs can do about as many multiplications a minute as all humans on the planet in a year. However, we do not see GPUs as ” super-intelligent;” we see them as system components. Similarly, faster LLMs allow us to build better and more intelligent systems.
This alignment with human modes of thinking and working should lead to AI systems that are more interpretable, controllable, and aligned with human values. It positions us to leverage these powerful AI capabilities as we’ve historically managed other technological advancements – as tools to augment and extend human capabilities rather than replace them.
AI is no longer just about raw power. It’s about the clever application of resources and the ingenuity of workflows built with AI as a foundation. As we trade training costs for inference ingenuity, we’re not just changing how AI works – we’re reimagining what it can do.
This new direction in AI development doesn’t just promise more capable systems; it offers the hope of a future where artificial intelligence and human intelligence can work together more seamlessly, leveraging the strengths of both to tackle the complex challenges of our world.
About the author: Andrew Filev is founder and CEO of Zencoder, developer of an AI copilot. Filev previously
founded Wrike, a provider of collaborative work management solutions that attracted more than 20,000 customers and was acquired for $2.25 billion.
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