#186 Is AI Really Slowing Down?

Key Takeaways

• The perceived AI slowdown is a natural phase. Rapid advances in hardware and foundational models create a Coriolis-like effect on applications, which need time to catch up and will always be few phases behind.
• Some usecase will get disproportionately accelerated and disproportionately deaccelerated.
• Content creation, while a flagship use case, may hit a trough of disillusionment. AI can curate and generate content, but without the creator infusing their unique perspective and emotion, the result may lack soul - a deficit readers will likely perceive.
• Information discovery and knowledge synthesis - encompassing both search and research applications - will continue to advance rapidly
• Significant disruption in software development life cycle (SDLC) processes, where we are also innovating, will continue on.
• As with any hype cycle, a minor slowdown may dissuade some early adopters, but the broader adoption curve of AI is beyond the point of no return. A reduction in speed does not indicate a directional reversal.

Introduction

I recently returned from a trip to the East Coast, and it got me thinking about the peculiar nature of cross-country flights. We all know the routine—an extra hour or so heading west, courtesy of Earth's rotation, trade winds, and the quirks of our atmosphere. But imagine for a moment that our planet received a major upgrade.

Picture Earth's core, traditionally powered by "core processing units" (CPUs), now turbocharged with "geo processing units" (GPUs). For those on the ground, this would mean an exhilarating new spin. But for our friends in the air? They'd be in for quite the wild ride.

This cosmic speedup is a fitting metaphor for our current AI landscape. There's been a lot of chatter lately about AI progress slowing down, but that perspective misses the broader picture. Sure, the applications we see might seem to be moving at a snail's pace, but what's happening beneath the surface tells a different story.

The Natural Evolution of AI Slowdown

Just as our imaginary Earth's atmosphere needs time to adjust to its new rotational speed, the AI landscape is experiencing a similar phenomenon. The perceived slowdown in AI progress is, in fact, a natural and expected phase of its evolution.

Tech giants are making substantial investments in AI hardware and investment below 11 figure does not even garner attention.The cost of training next generation SOTA models is touching the roof and may get to 12 figure in next 3-5 years, by some estimates. This reflects the significant computational resources poured into cutting-edge AI development.

However, practical deployment of AI use-cases takes time to catch up with these hardware and foundational model advancements. This lag is natural and necessary as businesses begin to integrate these powerful new tools into their operations. Understanding this lag is crucial for managing expectations and appreciating the long-term potential of AI advancements.

Uneven Progress Across AI Applications

Like how trade winds and the Coriolis effect impact flights differently based on direction, various AI applications are progressing at uneven rates. Some use cases are surging ahead, while others are momentarily lagging behind. This uneven progress is a reflection of the diverse challenges and opportunities present in different AI applications.

For example, content creation, which was once AI's flagship use case, may be headed for a reality check. While AI can generate and curate content at unprecedented speeds, there's a growing recognition that without human touch, the output often lacks soul. This deficit is likely to become more apparent as audiences grow more discerning.

In contrast, other applications such as information discovery and knowledge synthesis are advancing rapidly. These areas showcase AI's prowess in processing and connecting vast amounts of data. As search and research applications continue to evolve, they demonstrate sustained and impressive progress, highlighting the potential of AI in transforming these fields.

The software development lifecycle (SDLC) is experiencing ongoing disruption and innovation, propelled by AI advancements. From code generation to testing and deployment, AI is reshaping traditional SDLC practices and we are also at the forefront of these innovation.

AI Adoption: Beyond the Point of No Return

As with any technological revolution, the AI landscape is subject to hype cycles. While minor slowdowns may dissuade some early adopters, the broader adoption curve of AI has crossed a critical threshold. We're beyond the point of no return, and what might appear as a reduction in speed is not indicative of a directional reversal.