AI's Predictive Powers will Change how we Live & Work

As much as image generation is fun, the power of GenAI is prediction.

The technology operates very similarly to people you might meet:

* Some people have studied and are experts in a single topic for a decade. They’re experts in that topic and can easily infer, correct, and complete tasks. They’re unreliable for everything else.

* Some people are generally knowledgeable and have a good understanding of many topics. They aren’t experts but can reliably assist you in many ways. But they’ll also be wrong sometimes.

OpenAI, Anthropic, etc.— are highly knowledgeable in almost every topic. That’s the result of being trained on all accessible information online, data they’ve licensed, plus data they’ve allegedly stolen.

AI products built on these frontier models are immediately powerful for completing any task. But if you build a point solution on proprietary data explicitly trained on a narrow topic, it can achieve an expert level.

That was the focus of our conversation with Tyler Hochman, the Founder and CEO of FORE Enterprise. We discussed unlocking AI’s predictive power by focusing on expensive and repeating problems. How any business or founder can leverage and/or specialized data sets to train AI models to deliver powerful prediction capabilities.

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He’s built AI-powered software to predict when employees may leave their jobs, offer fashion advice, and help professional sports teams improve performance.

This video explains how to train your model using Figma files.

This conversation highlights how important your first party will become. This data includes more than just your customer data; it should include documenting workflows, quantifying initiatives, and developing a matrix of your offerings/capabilities. Anything repeatable must be quantified as a learning tool.

Example of a data collection strategy for AI training

When OpenAI launched a new image generation feature in ChatGPT, everyone jumped on it. AI-generated images infested our feeds in the Studio Ghibli style.

These images sparked a lot of worthy debate about copyright infringement, which added to the ethical concerns about how OpenAI trains its model. A recent study highlighted evidence that ChatGPT is trained on copyrighted works.

Given that AI models are running out of data to consume, they need to find clever ways to access a new data set.

Enter ChatGPT’s image generation tool and Ghibli craze. Millions of people have been feeding their photos into the model, giving it access to an entire universe of new training data to improve the quality of its image generation capabilities.

Lesson: Collecting user-generated content can provide your custom model with access to training data that was never possible before. This holds true whether your product is a document scanner, video generator, accounting software, run tracking app, or anything else.

As we move into the next phase of AI model evolution, the data you have access to might become your best competitive moat. Thus, businesses with access to ethically sourced content from their communities and customers have an advantage.

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Future of AI-powered workforces

Yesterday, LinkedIn exploded with screenshots of an internal memo sent by Shopify CEO Tobi Lutke to teams. It marks the most public evidence that AI is moving from a toy we experiment with to a critical skill that you’ll be scored in your next performance review.

The data backs up that AI adoption is surging within workplaces.

A study by the Wharton School at the University of Pennsylvania collected data on which use cases AI is most used for. The report highlighted use cases that every business and employees rely on daily or weekly.

Not so long ago, employees secretly used AI at work. The year-over-year data indicate that AI products are becoming adopted at an organizational level.

AI’s impact on our lives will be dramatic & potentially dystopian

Stanford’s 2025 AI Index Report offers metrics demonstrating the significant leaps forward AI has made across performance and usage metrics. The technology has already surpassed human baseline performance on many measures.

And the technology’s predictive capabilities are showcased in how effective LLM’s performance in clinical diagnosis. It points to a future where every one of us —physicians, educators, factory workers, and beyond— will rely on AI to make more informed decisions.

MUST READ: Futures essay about future of superintelligence

The AI 2027 essay, written by researchers and journalists, examines the question of what happens on a global level as we approach AI superintelligence.

A long and worthy read, it illustrates that we are much closer to superintelligence than the public may believe and that the snowball effects of achieving it are massive. They predict dystopian outcomes unless the world unifies around regulations and safety guidelines.

If their predictions are true, we’re being distracted by the table stakes of Ghibli image generation and coding tasks. This technology will utterly transform our personal and professional lives. It will give governments immense power over one another. And it will open Pandora’s box of dreams and nightmares.

If you need to chat through the implications of these predictions, email us info@designof.ai.

We’ll definitely discuss this in detail in our upcoming episode with the authors of the AI Con book and hosts of the Mystery AI Hype Theater 3000 podcast.

Podcast recommendation: The Most Interesting Thing in A.I.

The Atlantic’s Nicholos Thompson started an amazing podcast showcasing strategic topics about AI.

Listen to the Andrew Ng episode. It dives into important topics about the future of frontier models and the implications of running out of training data (if it happens).

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