In my previous post, I discussed why GenAI is the next transformational technology and why powerful GenAI models will become the next tech platforms like Apple and Android for mobile applications. The technological arms race is already intensifying, with major tech companies and open-source players bringing their A-game and new GenAI models being introduced almost weekly. The stakes are high and the outcome of the game will rearrange tech companies along the power curve.

3-Dimensional Chessboard

There are 3 layers in how the GenAI technology will be developed and used. This week, I explored the platform layer.

Platform layer: companies who build and train GenAI models, including foundational and large language models like ChatGPT as well as smaller models for specific use cases.

Application layer: companies who build on top of GenAI models, fine-tune them further, and make them more user-friendly and accessible for end-customers.

Customer layer: consumers and businesses who are the end-customers of GenAI technology to meet their needs, such as increasing productivity. Customers will vote with their wallet, usage, and attention to determine the winners and losers across the platform and application layers.

Platform Layer

There are a lot of GenAI models that we don’t often hear in the headlines. Clem Delangue, co-founder and CEO of Hugging Face, said ~15,000 companies uploaded ~250,000 ML models onto Hugging Face. About half of these are open-source and the other half are proprietary models. In addition, big tech and industry leaders are also building their models. Below is a simplified illustration of the platform layer (non-exhaustive):

The debate will continue whether there will be a large number of models vs a few dominant models to rule them all. It is prohibitively expensive and complex to develop general purpose large language models. However, we are likely to see a long tail of smaller models designed and trained for specific use cases and can get the job done faster and cheaper. Thus, companies building the application layer will need to figure out what is the best model for their specific use cases.

OpenAI’s Game Strategy

OpenAI has a well-thought-out game strategy followed by ‘fast and furious’ execution. In a move reminiscent of The Queen's Gambit, Sam Altman sacrificed 49% of OpenAI to Microsoft, a calculated decision that could lead to significant long-term strategic advantages. OpenAI is going after the most critical resource, poaching AI talent from its biggest competitors such as Google, Meta, and Apple.  

OpenAI is investing in the application layer through a $100Mn startup fund. To accelerate enterprise adoption, they formed strategic alliances with Bain, BCG, and Morgan Stanley. To build its defense, OpenAI has aggressively dropped its API price by 90%, and integrated ChatGPT into SnapChat, Slack, Instacart, Shopify and more. These well-orchestrated bold strategic moves are all in pursuit of becoming the dominant player on the GenAI Chessboard.

By integrating OpenAI's powerful models with Microsoft's comprehensive ecosystem of products (Bing, Windows, Office, Azure, virtual assistant, Xbox), other tech giants will face enormous pressure to devise their game strategies and play catch-up.

The true power of GenAI lies in its capabilities to have human-like conversations and generate human-like outputs. It’s still early, but as the technology advances, GenAI has the potential to be the next transformational technology to change not only how we do everything, but what we do. I have described below 3 reasons why I am excited about GenAI and its potentials to disrupt and create new value, likely at the scale of mobile and the internet.

1. Technology-Problem-Fit

Historically, transformational technologies introduced new capabilities and solutions to unsolved problems and found a technology-problem-fit. As a result, they scaled exponentially while disrupting the status quo and creating massive value for people and businesses. In the case of GenAI, what’s the technology-problem-fit? Let’s unpack that.

PROBLEM

Smartphones made it easier for people to get what they want, and softwares made it easier for companies to run their businesses. However, there are many unsolved problems for consumers and businesses that are too complex and require human-level intelligence hence done by humans, for now. To put into perspective, an average company with mostly white-collar workers spend x100 more in salary than software.

So the unsolved problems are the tasks, roles, and even industries that are done by humans - that’s a massive opportunity for GenAI. Which problems will be productized into a defensible business model? That’s a separate question that I am exploring as the focus of my next post. So stay tuned!

TECHNOLOGY

AI has been around for quite some time, but for most companies, it has been challenging to reap the benefits due to high barriers such as difficulty in finding and hiring AI talent, lengthy and costly to train and develop effective AI models. Consequently, only a small percentage of companies were able to leverage AI for high-value use cases. But, that’s about to change.

We saw two significant leaps in recent years. First, AI and large language models (LLM) have surpassed human capabilities in certain areas, most notably in reading comprehension and natural language understanding (see chart below). Second, tools like ChatGPT have made it very easy for anyone to use and benefit from GenAI through simple user interface.

Sam Altman, the founder of OpenAI, said powerful AI models will be the next tech platforms, paving the way for an explosion of new companies, similar to Apple and Android platforms did for mobile apps like Uber and Instagram. He predicts the new value creation will be at the scale of the App Stores.

Today, we already see use cases of GenAI demonstrating its technology-problem-fit. For example, Seek AI enables companies to ask and get answers from their data without the need of a data scientist and touching any raw data. Runway ML is super impressive and enables anyone to create and edit videos using commands in natural language - see the video below to see the GenAI magic in action. In the coming years, the capabilities of GenAI will likely compound exponentially and will bridge the gap between the technology and more complex problems.

2. Speed of Adoption

The launch of Dall-E and ChatGPT were the inflection point for GenAI, similar to the launch of iPhone was for mobile. In just 2 months, ChatGPT reached 100Mn users, a feat that took the internet ~10 years and mobile technology ~6 years. Even the most viral apps like TikTok took 9 months and Instagram took 30 months to reach 100Mn users. But these are not apple to apple comparisons. Let me unpack 2 reasons why GenAI adoption will be different and faster than anything we have seen before.

NO DEPENCENCY

Most transformational technologies, including their viral applications, rely on underlying hardware and infrastructure. For example, the internet depended on personal computers, and mobile apps needed smartphones. But today, almost everyone has a smartphone or computer hence can use ChatGPT.

GenAI use cases have no dependencies and barriers, except for compute power and cost. It's like the freeway is already built and all roadblocks have been removed. Also, ChatGPT is just one use case, and as hundreds of killer products and use cases emerge, GenAI adoption is set to skyrocket.

LANGUAGE INTERACTION

To reach mass adoption, no matter how great the technology is, an average person should effortlessly understand and use it. The iPhone, for example, is one of the best innovations in product design and user experience. Still, people had to learn how to interact with it, such as typing on a small screen and figuring out how to install an app. But with GenAI, the interaction is through natural language, as if talking to a human. You probably spent more time thinking about what to write in your first prompt in ChatGPT than figuring out how to use it. There will be no new learning curve for people to use GenAI, which will further accelerate adoption.

Today, those who speak the computer language harness the power of technology. But computers are learning to speak our language, so that power will be democratized. For me, the most fascinating aspect of GenAI is that it brings together the most powerful technology to solve the most complex problems, yet the user interaction is beautifully simple and intuitively natural.

3. Scale of Impact

Chamath Palihapitiya, founder and CEO of Social Capital, has identified two important trends for the next decade. First, the marginal cost of energy generation will drop to zero with the proliferation of solar power. Second, the marginal cost of compute is going to zero as ASICs, FPGAs, and GPUs will double compute capacity every 18-24 months. These trends will remove the computational capacity and cost constraints for GenAI, leading to an exponential acceleration in its advancement and adoption.

We will likely see a Cambrian explosion of startups across both axes, horizontal and vertical, bringing GenAI applications to solve complex problems. While it is highly uncertain what the aggregate net impact of this transformational technology will be on jobs, industries, economies, value creation vs destruction, and value distribution across consumer surplus vs businesses, it is certain that GenAI will lead to an unprecedented productivity increase by a factor of multiple digits. Let’s unpack that with an example. 

According to Emad Mostaque, founder and CEO of Stability AI, we are ~5 years away from generating movies in real time without the need to write scripts. If this becomes the reality, we could see personalized fan versions of the ending to the Game of Thrones. Personally, I would be excited to create the fourth series of Richard Linklater’s Before trilogy. While this may seem far-fetched, ten years ago, Dall-E and ChatGPT would have seemed just as improbable. YouTube and TikTok have democratized content creation while capturing a significant share of watch time. Now, imagine a scenario where everyone can create content without turning on a camera.

From the example above, just imagine the impact on the cost of movie production. Imagine the impact on Hollywood, streaming services, and the entire entertainment industry. Imagine what kind of conversations Bob Iger at Disney, Reed Hastings at Netflix, and Shantanu Narayen at Adobe are having in their 10-year strategy refresh sessions. And imagine the impact across all other industries. I will end this post with those imagination.

Thanks for reading! To receive future posts, subscribe here:

Subscribe now