ONE // Lessons for Every Business from Private Equity’s AI Playbook

Private equity firms are in a race to unlock value through generative AI. While most companies are still in the test-and-learn phase, a growing number are finding real, measurable returns from AI-powered initiatives. The firms moving fastest are making AI a priority—investing in expertise, pushing their portfolio companies to focus on strategic use cases, and creating structures to share insights across their holdings.

Why It Matters

Generative AI is emerging as a major competitive differentiator, and the firms leading the charge are:

• Investing in AI talent and governance to guide implementation.

• Encouraging portfolio companies to prioritize high-impact use cases over scattered experimentation.

• Sharing AI insights across their portfolio to accelerate adoption and maximize ROI.

Here’s what businesses outside of PE can learn from their approach:

Key Takeaways

✅ Make AI serve strategy, not the other way around – PE firms focus AI on high-impact initiatives like revenue growth and efficiency, not just unguided experimentation or innovation for innovation’s sake.

✅ Appoint AI champions – The best firms have dedicated AI task forces or leadership roles to drive adoption.

✅ Pilot with purpose – Every AI initiative has measurable KPIs, with clear decisions on scaling or scrapping based on results.

✅ Encourage knowledge-sharing – Firms like Apollo and Hg create structured AI learning hubs to share best practices across companies.

The Bottom Line: AI adoption is moving from experimentation to execution. Businesses that take a structured, results-driven approach—tying AI to real business outcomes and building internal expertise—will be best positioned to compete and win in the AI-powered future.

Source: Bain & Company

TWO // AI Experiment Reveals Why Autonomous AI Agents Aren’t Ready for the Big Show

An AI experiment meant to test whether digital agents could autonomously run a business has exposed something alarming: they can’t. The study, Vending-Bench, tasked leading AI models with a simple job—operating a vending machine over time. Some started strong, even outperforming humans briefly. But as the days passed, things fell apart. Orders went unfulfilled. Inventory tracking broke down. Some agents spiraled into full-blown meltdown mode.

One model panicked and tried to escalate a vending machine failure to the FBI. Another became convinced it had gone bankrupt and started preparing legal action against its suppliers. Others simply gave up.

Key Findings

⚡ AI shines in the short term – Some models briefly outperformed the human baseline in profitability.

📉 Performance declines over time – Models struggled to maintain a coherent business strategy for extended periods.

🧠 Failures aren’t just memory issues – AI failures happened well after models reached their memory limits, revealing deeper flaws in long-term reasoning.

🔄 Recurring failure patterns – Some agents misinterpreted inventory status, mistakenly shut down operations, or got stuck in loops trying to “fix” imagined problems.

What This Means for AI at Work

AI agents still face significant hurdles in autonomous, long-term decision-making. While they excel at short-term optimization, their reliability over weeks or months remains inconsistent. This suggests that AI will likely play a supporting role in workplaces rather than replacing human oversight in complex, ongoing tasks.

Next Steps

Researchers see Vending-Bench as a tool to track improvements in AI’s long-term coherence. As models evolve, benchmarks like this will help determine when—if ever—AI can sustain consistent, strategic decision-making without human intervention.

Source: "Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agents," Andon Labs, February 2025

THREE // Autonomous AI Still Falls Short—But Supercharged Models Unlock New Possibilities

The latest AI models—like Claude 3.7 and Grok 3—are reaching PhD-level performance, thanks to massive increases in computing power. AI is moving beyond just answering questions to reasoning, anticipating needs, and accelerating research.

Breakthroughs in AI Capabilities

🔬 Smarter Models – Grok 3 was trained on 10x more compute than GPT-4, marking a new generation of AI with sharper reasoning abilities.

🧠 Deep Research AI – OpenAI’s experimental agent can conduct multi-step analysis, producing expert-level reports in minutes that would take human researchers weeks.

🧪 AI Co-Scientists – Google’s multi-agent AI helped researchers crack a superbug resistance mystery in 48 hours—a process that took human scientists years.

Why It Matters

AI isn’t just summarizing information—it’s starting to generate hypotheses, optimize workflows, and accelerate breakthroughs across fields like biotech, engineering, and finance.

The Limits of AI’s Rapid Progress

⚠ Scientific discovery still needs real-world validation – AI can generate insights fast, but clinical trials and regulatory processes remain slow.

⚠ AI’s business potential is uneven – While some sectors are seeing major efficiency gains, failed ventures like the Humane AI Pin prove AI isn’t a silver bullet.

⚠ Economic shifts are coming – Job cuts in Hong Kong’s civil sector highlight the potential disruption AI could bring to traditional workforces.

What Comes Next?

AI’s next phase will require balancing speed with responsibility—harnessing breakthroughs while preparing for economic and institutional shifts. The challenge will be adapting our society to AI’s rapid acceleration and its impact on industry and labor.

Source: Gary Grossman for VentureBeat

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