💸 The $3 Trillion AI Opportunity Everyone Missed (Chris Bora Substack)

• Current AI investment narrative may be wrong: Despite concerns over a $600 billion “AI infrastructure bubble,” this article argues that we’re actually under-investing in AI infrastructure needed to support the future potential of AI-driven personalized advertising.

• A shift in advertising value: AI’s ability to generate personalized, real-time audio and video ads based on detailed intent and conversation data could revolutionize programmatic advertising. This shift mirrors the rise of programmatic display advertising, which grew from $0.5 billion in 2009 to $153 billion by 2022.

• Three key forces driving growth: Massive growth in intent data from AI chatbots, advanced AI-generated media capabilities, and scalable programmatic infrastructure are expected to combine to exponentially increase ad performance and return on investment (ROI).

• Exponential improvement potential: AI-driven ads show significant early results, such as a 7.2x ROI improvement and up to 15x better performance at scale. Unlike traditional ads, these results are expected to increase as user engagement grows and AI continues to refine its personalization.

• Projected market expansion by 2030: With personalized AI-driven ads expected to reach a $3 trillion market size, a breakdown includes $990 billion for programmatic video, $550 billion for audio, and $440 billion for interactive ads. The AI infrastructure supporting this growth is projected to need 20-30x the current GPU capacity.

• AI ad infrastructure as a transformative investment: Analogous to Google’s early investment in AdWords or Amazon’s investment in cloud infrastructure, today’s AI investments could be foundational to capturing an unprecedented advertising market opportunity, with potential cost reductions of 90% and performance boosts of up to 7x.

🫀 AI-powered tool may offer quick, no-contact blood pressure and diabetes screening (Heart.org)

• Innovative AI-powered screening: Researchers in Japan are developing an AI-powered system that can screen for high blood pressure and diabetes using a high-speed video of the face and palm, potentially offering a fast, contactless method for health monitoring.

• Accuracy in preliminary tests: The AI system achieved 94% accuracy in detecting stage 1 hypertension and 75% accuracy in identifying diabetes, showing promising early results in capturing subtle blood flow changes linked to these conditions.

• Potential for at-home health monitoring: With future enhancements, this technology could enable people to monitor their health at home, potentially detecting high blood pressure and diabetes earlier, particularly for those who avoid traditional medical exams.

• Developmental challenges ahead: The system requires adjustments for real-world use, such as managing arrhythmias and adapting to different lighting conditions, before it can be integrated into devices like smartphones or mirrors.

• Significance in cardiovascular care: If successful, this AI tool could revolutionize cardiovascular disease prevention by providing non-invasive, accessible health screenings, though further validation and regulatory approval are needed to ensure reliability.

🌊 How we’re helping partners with improved and expanded AI-based flood forecasting (Google)

• Broadened flood forecasting coverage: Google has expanded its AI-driven riverine flood forecasting model, now covering over 100 countries and 700 million people, up from 80 countries and 460 million people, offering critical flood information to millions globally.

• Enhanced model accuracy: The latest model improvements provide seven-day forecasts with the same accuracy previously achieved at five days, thanks to new architecture and increased labeled data, making predictions more reliable and timely.

• New API for researchers and partners: Google’s upcoming API, currently open for partner signups, will provide access to hydrologic forecasts, even in data-scarce regions, aiming to advance flood research and improve response times.

• Virtual gauges for expanded data coverage: With the addition of 250,000 "virtual gauges," Google’s Flood Hub now provides flood forecasting information for areas without physical sensors, supporting research in 150+ countries where local data is limited.

• Historical data access for research: Google’s GRRR dataset, with historical flood forecasts dating back to 1981, is now publicly available to help researchers analyze flood trends and develop strategies to mitigate future impacts.

• Global climate action through AI: Through collaborations, such as with Brazil's Geological Service and organizations like World Vision, Google’s flood forecasting tools have been instrumental in crisis response, delivering lifesaving information and resources to vulnerable communities during climate-related disasters.

✨ PowerPrompt™

Imagine you’re the chief visionary for a groundbreaking project in [your industry or goal]. Your mission is to transform the landscape within 5 years using advanced AI. Frame this as a roadmap in three sections. Each section should include specific strategies for overcoming obstacles and maximizing opportunities with cutting-edge AI tools and innovations. Use conversational tones and invite active feedback at every phase. Lastly, conclude with a call to action designed to rally your team around a shared vision that is both ambitious and achievable."

Prompt Structure:

Phase One – Foundation Building: Outline strategies for setting up key AI-driven systems, data sources, and core capabilities that will serve as the project’s foundation. How will you identify and overcome initial hurdles? Describe these strategies.

Phase Two – Acceleration and Expansion: Articulate steps for scaling the project, leveraging AI to optimize processes, increase impact, and drive rapid growth. What tools and AI innovations will you introduce to transform performance?

Phase Three – Sustainability and Innovation for Future Growth: Lay out a vision for long-term success, exploring how AI can not only sustain the project but also pioneer future advancements. How will you encourage innovation to keep your team inspired and adaptable?

🧰 AI Tools & Resources

• Danelefin: AI-powered stock picker

• Theo: Transform any AI assistant into a strategic co-pilot

• AlphaFold3: Google just open-sourced its DeepMind AlphaFold3 available on Github. This package provides an implementation of the inference pipeline of AlphaFold 3.

🎉 THAT’S ALL FOR TODAY!

See you next time! 👋

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