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You’re two years behind the curve, but it is ok - we all are.

When it comes to generative AI and tools like OpenAI, it's important to acknowledge that we're always playing catch-up. We're essentially two years behind the leading edge of this technology. To illustrate, it's been over a year since ChatGPT in its current form was launched, but the model it was based on was trained a year before its release. This time lag means we're constantly operating with technology that's already a bit outdated by the time it becomes available to the public.

The reason for this lag isn't due to a lack of innovation but rather the immense challenge of developing and training these models. The sheer size and complexity of these AI systems make the training process incredibly resource-intensive, requiring massive computational power. Consequently, while we might feel we're at the forefront of technology, it's both reassuring and daunting to realise that somewhere, researchers are already working on even more advanced models.

Recent advancements have primarily focused on improving the performance and utility of these tools. These incremental improvements enhance the user experience and broaden the applications of generative AI. However, we know that a major revision of the underlying models will eventually come, and this will likely bring about significant changes.

This anticipated evolution will undoubtedly impact various aspects of technology and society. It will be fascinating to observe how these advancements reshape the infrastructure we've built around current models and what new possibilities they unlock.

Key Talking Points

1. Current State of Generative AI - We're always about two years behind the latest advancements due to the time it takes to train and deploy new models.

2. Challenges in Model Training - The complexity and size of these models require substantial computational resources, contributing to the delay in bringing new technology to the public.

3. Continuous Improvements - Recent advancements focus on enhancing performance and utility, making these tools more efficient and versatile.

4. Future Revisions - A major update to the underlying models is anticipated, which will have far-reaching impacts on technology and infrastructure.

5. Implications of Advancements - Understanding that more advanced models are in development can be both reassuring and daunting, as it highlights the continuous progress in the field.


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