AI’s Next Big Leap

If it’s Wednesday, it’s Wilkinson Wire! This week, we dive into a pivotal shift in AI's trajectory: from generative models to collaborative reasoning. While the rapid progress of GPT models is slowing, the next phase promises something more profound—AI systems that "think" deeper, collaborate smarter, and solve real-world challenges more effectively.?

The AI world is at a turning point. GPT models ushered in a new era, but the pace of improvement is slowing. OpenAI’s latest efforts, including the "o1" reasoning model, signal a strategic pivot: moving beyond traditional scaling laws that rely on endless data and compute power. Instead, reasoning models prioritize depth and adaptability, enabling AI to tackle more complex, multistep problems by “thinking” longer.

This shift also reflects fierce competition. Anthropic’s Claude 3.5 recently outperformed OpenAI’s GPT models in AI research tasks, highlighting the pressure to innovate. Yet, the industry faces hurdles—synthetic data risks redundancy, and escalating costs challenge scalability. These constraints are pushing developers to explore smarter, more sustainable approaches.

Although the pace of AI advancements may be slowing, humans lag in effectively integrating AI into professional practices. A new Stanford study showcased ChatGPT's remarkable diagnostic accuracy, scoring an “A” while physicians scored an average of “C”.? Even when physicians used ChatGPT as a diagnostic aid, their performance showed no significant improvement.?

This gap underscores the critical need for improved workflow integration, such as targeted training and new systems to truly deliver on AI’s promises of enhanced accuracy and efficiency. Rather than worrying about the slowing pace of AI progress, companies should focus on effectively applying AI to real-world use cases and building systems that maximize its ROI. If you’re looking to upskill your leadership team’s AI expertise or leverage AI systems across your organization, my innovation firm Ingenuity is here to help.

The big picture? AI is evolving from a tool that generates to one that collaborates. Reasoning systems could redefine how AI integrates into science, technology, and enterprise, moving beyond incremental gains to unlocking deeper problem-solving potential. We’re entering a phase where AI might truly partner with us, expanding creativity and innovation in ways we’re just beginning to understand.

Tip of the Week

Every week, I’ll include exercises to help you develop your AI skills. Below are a few exercises to integrate AI into your workflow.

• As a Data Analyst, use AI to analyze reports, market trends, or customer feedback, then summarize key takeaways. This enables faster, data-driven decisions that improve outcomes.
• As a Marketer, use AI to design structured, results-focused marketing plans for new products or to optimize existing campaigns.?
• As a Product Manager, use AI to streamline product development and strategy. You can analyze customer feedback, prioritize feature requests, and suggest data-driven roadmaps to optimize product performance.

?? Prompt (for copy and paste):

• "Summarize these sales data trends: [Paste data]. Provide the top three takeaways and potential actions to leverage the insights."
• "Develop a marketing campaign outline using the 'Growth Scaling Framework.' Identify key growth drivers for our [product/service], set measurable goals, and outline scalable strategies. Include specific tactics and metrics to track effectiveness."
• "Analyze customer feedback: [Paste feedback]. Identify the top three feature requests and recommend how to prioritize them in our development roadmap."