Will you be a loser in the AI race?

The winners in the AI race are those enterprises that have embraced generative AI like GPT-4, using it to automate, optimize, and innovate. These companies have seen the immense potential of AI, not just as a tool for automation but as a way to generate novel insights, create unique customer experiences, and drive strategic decision-making.

However, not all are thriving in this AI-driven world. The losers in this race are often those who fail to recognize the value of AI, are resistant to change, or lack the necessary skills and resources to implement AI solutions effectively and fast. They risk falling behind in productivity, customer engagement, and overall competitiveness.

Yet, it's not just about large enterprises. An exciting ecosystem of AI startups is emerging, offering unique solutions and services that leverage generative AI. As the world leans into generative technology, many are left wondering how to stand out in a landscape where everyone seems to be nurturing similar ideas. You start seeing them called "GPT for something". These nimble organizations, unburdened by legacy systems, are able to quickly innovate and adapt, bringing fresh perspectives and approaches to the table. However, also, in the long run, they may all disappear under the shadow of ChatGPT's Plug-ins and Browsing capabilities if their value proposition is not defensible in the long term.

It's important to think ahead. What's the long-term utility value of your creation, and what's its defensibility against competition? As Generative AI transitions from a feature to a product to a full-fledged business, understanding that your model will keep improving based on Reinforcement Learning and moderated data training, the solution also will keep getting better.

Rapid deployment of your product in the market is crucial. Watch closely for what works and what doesn't, and understand what makes consumers uncomfortable. Don't aim for perfection right out of the gate – it's more important to launch your feature first and let the model learn and improve over time. For instance, Jasper's "Tweet generator" beta launch is a prime example of this approach. Despite its early flaws, getting the product out there was the right move and serves as a model for your own endeavors.

Over time, 4 aspects of products will standout, whether you are a large enterprise or a startup, this applies to you both:

Data Input

You have to understand better what data you feed into the model. What are the biases? Is it skewed towards a certain ideology or lacks the niche knowledge of the industry you operate in? More data doesn't mean more intelligence. The ability to make connections in your Neural Networks will be more powerful than the data that is provided.

Prompting

At this point, we all have to get better at literature and communication in the language. Being elaborate and having the ability to communicate with the LLMs will be key to better understanding the brain of the model you are building.

Moderation

Make sure you review the outputs of the model given a prompt. Hallucinations are real. If you base your executive decisions on hallucinations, your business may take a sharp turn downwards. Keep in mind that LLMs are very convincing because they do not lack the confidence to make you believe in what they say. Make sure to review every bit of output if you want to prioritize the quality.

Reinforcement Learning

The process of learning from feedback and refining the model's behavior over time is crucial in ensuring the model's predictions align with desired outcomes. RL enables the model to explore and experiment, optimizing its performance in real-world scenarios.

As an AI product owner, leveraging RL can help in improving the product's performance and making it more robust. However, it's important to note that RL also requires careful monitoring and management. Without appropriate oversight, the AI could learn behaviors that are undesirable or even harmful.

In the next era of AI, the successful players will be those who can effectively harness the power of generative AI models like GPT-4, continually adapt and innovate, and ensure that their AI applications are safe, reliable, and beneficial to their users. They will be the ones who can make the most out of AI, turning it from a tool into a strategic advantage.

Now, there will inevitably be malevolent actors who exploit LLMs to produce harmful content, spam, or scams aimed at manipulating society into believing falsehoods. As with all technological advancements, winners and losers are defined not only by who adopts the technology swiftly and effectively but also by the balance of intentions. The emergence of fake content, such as deceptive news or false propaganda, becomes especially critical during elections or shifts within governmental institutions. This will likely lead to more polarized views and increased belief in falsehood. The biggest loser may ultimately be society itself, as our faith in the "truth" of facts may be undermined. The winners of the next decade will likely be determined by the regulations we establish.