New Survey Reveals Growing Problem With AI | Trading the Markets With AI, April 15, 2026

Key Concepts

• Agentic Payments: Automated, machine-to-machine financial transactions facilitated by AI agents.
• Local LLMs (Large Language Models): AI models run on local hardware rather than cloud servers, addressing data privacy concerns.
• Bespoke Blockchains: Custom-built blockchain networks tailored for specific institutional use cases (e.g., payment rails) rather than utilizing general-purpose public chains like Ethereum.
• AI Compute Hardware: The physical infrastructure (GPUs) required to train and run AI models, now a high-demand commodity.
• Technical Indicators: Mathematical calculations based on price, volume, or open interest, used by traders to predict market trends.

1. Meta’s New AI Model

Meta has launched a significant new AI model following a $14 billion investment.

• Performance: Early testing suggests it is highly competitive with Claude, Gemini, and Perplexity. It is noted for being "lighter and faster."
• Specialization: The model is optimized for reasoning, science, math, and health, rather than coding.
• Business Model: Currently free for users with Facebook or Instagram accounts. A paid tier (approx. $10/month) is being tested to offer increased tokens, context, and memory.
• Strategic Goal: Meta aims to bring AI to its massive user base of approximately 3 billion people.

2. Visa and Agentic Payments

Visa has entered the blockchain space by becoming a validator on "Tempo," a new blockchain focused on payments developed by Stripe and Paradigm.

• Institutional Shift: Traditional finance (TradFi) is increasingly building "bespoke" blockchains rather than adopting existing public crypto projects.
• Machine-to-Machine (M2M): The focus is on facilitating payments between AI agents, signaling a shift toward an "agentic economy" where machines handle transactions without human intervention.

3. Stanford AI Report & Public Sentiment

The Stanford annual AI report highlights a significant gap between expert optimism and public anxiety.

• Key Statistic: Only 10% of Americans report being more excited than scared about AI.
• Media Influence: The speakers argue that "doom and gloom" headlines regarding job losses sell more clicks than positive narratives about productivity.
• The "Endgame": The hosts argue that the long-term goal of AI is to automate menial, depleting tasks, eventually freeing humans to focus on creative and meaningful pursuits.

4. AI in Pharmaceuticals

Major pharmaceutical companies are integrating AI to accelerate drug discovery and clinical trials.

• Strategic Partnerships:
  • Novo Nordisk: Partnered with OpenAI to integrate advanced AI into drug development.
  • Novartis: Secured a seat on the Anthropic board of directors to influence the development of AI in medicine.
• Efficiency: AI processes massive datasets that previously took years and billions of dollars to analyze, potentially slashing development timelines.
• Technical Note: These companies use bespoke, private models trained on internal proprietary data, not public-facing tools like ChatGPT.

5. AI in Sports Betting

An experiment testing eight top AI models on a full Premier League season resulted in failure, with some models going bankrupt.

• Limitations: AI struggled with real-world variables such as player injuries, team morale, and momentum.
• Future Outlook: While direct sports betting is currently difficult for AI, the speakers suggest that integrating AI with prediction markets (which aggregate human sentiment and real-world data) could be a viable future application.

6. The "Allbirds" Pivot

Allbirds, a footwear company, recently pivoted to become an AI infrastructure company.

• The Pivot: The company sold its shoe business IP for $39 million and shifted its focus to leasing AI compute hardware.
• Market Analysis: While the shoe market was highly competitive and saturated, the AI compute market is experiencing massive demand. However, the hosts note the challenge of competing against established hyperscalers like AWS and Microsoft Azure.

Synthesis and Conclusion

The overarching theme is the rapid, often chaotic integration of AI into traditional sectors. While public sentiment remains fearful due to media sensationalism and job displacement, the institutional adoption—led by giants like Visa, Novartis, and Meta—suggests that the technology is becoming foundational to global infrastructure. The transition period is marked by "growing pains," such as failed sports betting experiments and corporate pivots, but the long-term trajectory points toward increased efficiency in research, finance, and computing. The hosts emphasize that while the "hype" is real, the true value lies in the practical, bespoke applications of AI that are currently being built behind the scenes.