Product management is a crucial role within any organization, as it bridges the gap between customer needs and business objectives. It involves understanding the pain points of customers and translating them into tangible solution requirements.

Product management is a crucial role within any organization, as it bridges the gap between customer needs and business objectives. It involves understanding the pain points of customers and translating them into tangible solution requirements.

But what exactly does it take to be an effective product manager? To answer this question, we must first examine the dynamics between startups and incumbents in the realm of AI and technology.

In the past, the value generated from AI predominantly favored incumbents rather than startups. We saw this with the emergence of the internet, where giants like Google, Amazon, and Facebook captured a significant portion of the value, while some incumbents extended their franchises onto the internet. The split between startups and incumbents in this wave of innovation was around 60:40 or 70:30.

Similarly, with the rise of mobile technology, incumbents such as Apple and Google dominated the market, with every mobile version of their apps becoming the go-to choice for users. However, startups like WhatsApp, Uber, and Instagram still managed to carve out their own share of the value, resulting in a split of 20:80 between startups and incumbents.

Interestingly, when it comes to crypto, startups have been the primary drivers of value creation, with little participation from existing financial services or infrastructure companies. Bitcoin, Ethereum, Coinbase, and Binance are just a few examples of successful startups in the crypto space.

So why do incumbents often have the upper hand over startups? One reason could be the data advantage that incumbents possess. However, as companies now have access to a broader internet as an initial training set and are adopting models that work efficiently with smaller data sets, this advantage may be diminishing.

To overcome the challenges posed by incumbents, startups typically need to build products that are not only dramatically better but also target new customer segments or distribution channels that incumbents cannot serve. A 10X better product is often the key to success.

In the realm of AI, the landscape is constantly evolving. While previous AI innovations such as AlexNet, CNNs, RNNs, and GANs were groundbreaking, this current wave of AI feels different. The technology has become significantly stronger, enabling the creation of products that are 10X better than what incumbents offer.

GPT-3, although not yet widely adopted by startups, has the potential to pave the way for a new ecosystem of AI-driven businesses. A model that is 5-10X better than GPT-3 could further augment incumbent products while giving rise to a host of new startups in various industries.

What sets this wave of AI startups apart is the presence of infrastructure-centric companies that have gained broad adoption and are experiencing rapid growth. OpenAI, Stability.AI, Hugging Face, and Weights and Biases are just a few examples of such companies. Their existence provides startups with greater opportunities and access to cutting-edge technologies.

Moreover, there are highly repetitive, highly paid tasks that can benefit from AI. These tasks, such as coding, marketing copywriting, and website image generation, often lack efficient workflow tools. By incorporating AI features into workflow tools, startups can create products that address these needs and provide significant value to users.

However, it is crucial for startups to avoid the "hammer-looking-for-a-nail" problem. Instead of simply focusing on the capabilities of AI, it is essential to identify actual end-user needs and untapped markets that can benefit from this technology. By prioritizing user needs, startups can ensure that their AI-driven products truly solve problems and create value.

As someone who has been involved in AI-related products for over 15 years, I believe that startups are finally poised to receive real value from AI. The speed of innovation, the strength of the technology, and the presence of infrastructure-centric companies all contribute to this exciting prospect.

In conclusion, for startups to thrive in the realm of AI, it is important to build products that are significantly better than incumbents, target new customer segments or distribution channels, and prioritize actual end-user needs. By doing so, startups can capture a larger share of the value created by AI and pave the way for a new era of innovation and growth.