Chapter 1: The AI Imperative

Upon arriving in Bilbao for the DigitalTek Awards this past Sunday, I began reflecting on my presentation for today. I stumbled upon a TechCrunch article discussing what startup founders should consider regarding AI in 2024, specifically the need to reevaluate their business models.

The initial step is to assess our current position: we transitioned from machine learning to generative algorithms, both of which are crucial for scaling models. This shift mirrors the impact of the internet in the late 1990s, when Andy Grove famously stated, “in five years, all companies will be Internet companies or they won’t be companies at all.” This was the “net imperative,” where businesses had to establish an online presence to foster new ideas, reimagine existing ones, or leverage new opportunities.

Having experienced that era firsthand, particularly as an educator in emerging technologies—let’s just say I had a fuller head of hair back then—I vividly recall the skepticism and surprise many expressed regarding the “net imperative.” Yet, within a few years, it became evident that if the internet went down, productivity plummeted, leading many to head home.

We are now at a similar juncture: firms that fail to evolve into AI-first entities swiftly or those that aren’t built with this mindset risk becoming obsolete. It's essential to clarify that transforming into an AI-first organization isn't merely about utilizing ChatGPT or incorporating it into our offerings, just as having a website didn't make a company “internet-based.” The transformation involves much deeper changes.

The integration of algorithms into our business strategies will dominate conversations in the coming months. A conflict of interests looms between those developing algorithms—mathematicians, statisticians, and developers—and those seeking rapid funding to bring products to market. Consequently, companies and entrepreneurs must discern which aspects of their operations rely on generic algorithms from suppliers versus those requiring the crafting of proprietary algorithms tailored to their data and insights.

Reproducing the mistakes of the past, when the colossal platforms we know today were born, would be detrimental. Relying on a platform that dictates the rules and can change them at will poses significant risks. We've witnessed this numerous times, leading to the emergence of powerful entities operating outside regulations and ethical norms.

Merely customizing algorithms provided by these platforms will not suffice, not only due to our ongoing dependency on them but also because we might never know if those platforms leverage our data to enhance their own tools.

In summary, being AI-first does not mean emulating the Big Tech model. Instead, it requires a recognition that while developing an algorithm capable of comprehending human language may be out of reach, creating simpler algorithms that can intelligently process our data and automate tasks is certainly achievable.

The first step is to adopt a data-first philosophy, feeding that information into our algorithms. These algorithms may initially be unreliable, but they will evolve over time. The goal is to utilize the right tools, avoiding unnecessary reinvention while seamlessly integrating our efforts with our business objectives and clearly communicating our expectations to our collaborators.

Transforming or establishing businesses for this new technological age will not be straightforward. Some may choose to ignore the necessity for change, dismissing it as irrelevant. However, it’s crucial to recognize that an economic revolution is on the horizon—not because algorithms will gain consciousness, but for reasons that are easier to grasp. We’ve navigated this terrain before, and hopefully, we will continue to face such pivotal moments. When a competitor emerges who offers similar services more efficiently because they anticipated these shifts, remember, I warned you.

Chapter 2: Embracing Change

This video explores how ElevenLabs' AI technology can mimic human voices flawlessly, showcasing the advancements in voice cloning and its implications for various industries.