2 minute read
You might not realize it, but AI is moving faster than any tech we've ever seen.
Take OpenAI as an example. After launching GPT-3.5 in late 2022, they quickly followed up with GPT-4 in early 2023, improving accuracy and cutting down on hallucinations. The speed is insane.
OpenAI isn't stopping there. They've since introduced GPT-4 Turbo and GPT-4o, followed by GPT-4o Mini, a compact version designed for efficient, high-performance tasks. They're also about to launch Sora, a model that generates videos from text, and real-time multimodal models that can interact with audio and video seamlessly.
But it's not just OpenAI pushing boundaries. The pace of large language model (LLM) development has been crazy across the board. Anthropic's Claude models, Meta's progress with the open-source LLaMA, and Google's Gemini project are all part of this rapid evolution. Each of these companies is racing to develop more powerful and efficient models, pushing the limits of what LLMs can do almost monthly.
In fact, on Hugging Face (a platform for sharing and collaborating on AI models), there are now over 300,000 models, with thousands of new LLMs being added each month. This constant influx of models is driving AI technology forward at an incredible pace.
And the future? You can guess it when you know Amazon has invested $4 billion in Anthropic, Microsoft and Meta have spent billions on Nvidia's H100 chips to power their AI work, and Google plans to invest over $100 billion in AI initiatives over the coming years. By 2040, it's said that AI could match the performance of the top 25% of humans in certain tasks—40 years sooner than initially expected.
You probably see now why it's so hard to keep up with AI in an enterprise setup.
It's too much work to be handled by one superhero within the organization, and it's very hard for big teams to stay on top of every new invention in AI.
Even if you manage to implement some AI within your teams' workflows, the work will become obsolete soon after. The only way around it is to strategize your AI transformation.
In the next chapters, we'll go through it all. You'll learn how to implement AI across your organization's departments successfully.