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Borrowing from Dr. Strangelove, we would say that it’s time we stop worrying about when AI will kill all humanity and start loving it. The industry appears to be suffering from cognitive dissonance. On one hand, newfound fears about ChatGPT are replacing the initial euphoria, and on the other, a plethora of AI startups have popped up like mushrooms in monsoon. Corporates are banning the use of ChatGPT or AI tools while simultaneously wanting to jump on the AI bandwagon due to FOMO (Fear of Missing Out).

The Hype Around Prompt Engineering

‘Prompt engineering’ is being touted as the holy grail of using LLMs (Large Language Models). Judging by posts on social media platforms, it appears to be the hottest skill on the market. There are individuals guaranteeing incomes of $10K per week by automating content creation and publishing using their software and ChatGPT. Those of us who lived through the dotcom boom and bust cycle must recognize this brand of Kool-Aid.

The Reality of AI Startups

Like what happened with the dotcom and other bubbles, most of the newly minted ‘get-rich-quick’ startups that solely rely on ChatGPT or similar platforms are going to hit a brick wall and fail. The ones founded with solid business plans and meaningful strategies will stand to gain tremendously.

Opportunities in the AI Landscape

Here’s our cheat sheet on where the opportunities may lie for various market segments to capitalize on AI. The AI landscape is changing extremely fast, akin to Moore’s Law on steroids. While access to compute power and petabytes of data will continue to be a strategic advantage for now, newer approaches may reduce barriers to entry, as evidenced by the Chinchilla paper by DeepMind.

Building an AI Strategy

To help build your AI strategy and adoption roadmap, consider these points. The old cliché is still valid – it is a marathon, not a sprint. Avoid getting sucked into FOMO. Think through the strategy before pulling the trigger on any action plan.

• Natural Language Interface: Natural language will evolve to be the primary user interface. Plan for audio, not just textual interfaces like those currently employed by ChatGPT and other LLMs.
• Prompt Engineering: This will become an essential skill for data scientists and ML engineers to help evaluate various LLMs.
• Private LLMs: Fine-tuned to the user’s content, these will be a goldmine for both large enterprises and startups looking for new products and offerings.
• Specialized Smaller Models: While monolithic models will continue to evolve, smaller models fine-tuned to specific tasks will become more popular, especially if they are designed to continue learning in real-time.
• Machine Learning for Developers: TensorFlow’s library for JS will bring machine learning to gazillions of full-stack developers, accelerating new products and ideas.

Advice for AI Adoption

For those planning to adopt AI, we recommend spending the second half of 2023 evaluating various AI tools and models with targeted POCs. Use the findings to define an AI transformation strategy that will help create a differentiation among your competitors.

Focus on Personalized Education and Healthcare

For those planning new products or services, focus on personalized education and healthcare. These areas present significant opportunities for innovation and impact.

By shifting our perspective from fear to opportunity, we can harness AI's potential to drive meaningful advancements and improvements across various sectors.