Dambisa Moyo
Dambisa Moyo
THE LONG VIEW
as a universal basic income. Many business leaders fear that the private sector will increasingly be held responsible for AI’ s unintended consequences, creating pressure to raise corporate taxes.
Taken together, these concerns help explain why many business leaders are adopting a more cautious approach to AI adoption. Palantir CEO Alex Karp recently captured this sentiment in an interview with CNBC, accusing OpenAI and Anthropic of“ stealing the weights and alpha of my business” through token-based pricing. more frequent and severe, accelerating the fragmentation of the global technology ecosystem and making it increasingly difficult to operate across borders.
These threats are already attracting the attention of policymakers. In its latest Financial Stability Report, the Bank of England warned that rapid advances in frontier AI capabilities have heightened cyber and operational risks, increasing the likelihood of faster, more coordinated disruptions.
Finally, to justify the enormous sums being invested, business leaders must set
Many business leaders fear that the private sector will increasingly be held responsible for AI’ s unintended consequences, creating pressure to raise corporate taxes.
Against this backdrop, business leaders should consider four steps as they develop their AI strategies. First, companies should review and restructure AI contracts so that model providers have more skin in the game. If LLM providers are confident they can deliver billions of dollars in productivity gains and cost savings, they should be willing to tie at least part of their compensation to the value they create rather than rely solely on usage-based pricing.
Business leaders should also continually compare the cost and time required for AI to complete a task with the cost and time required for humans to perform it, using clear operational and financial metrics to assess AI’ s economic value.
Second, companies should avoid becoming overly dependent on any single model provider. Many firms are already experimenting with multiple models, assigning different providers to different functions or deploying AI tools within operational sandboxes before rolling them out company-wide.
More broadly, companies should consider open-source alternatives. Many of today’ s leading open-source models have been developed by Chinese firms and arguably outperform their Western counterparts. They also come at a fraction of the cost. Token prices for MiniMax M3 and DeepSeek V4 Pro, for example, are less than one-tenth of those for Claude Opus 4.8. That is why companies like DoorDash and Airbnb are turning to Chinese models. For many Western companies, however, geopolitical and cybersecurity concerns are likely to outweigh those advantages.
That brings us to the third step business leaders should take. Greater reliance on AI will inevitably require stronger cyber defenses. Beyond the cybersecurity risks associated with today’ s LLMs, advances in encryption, decryption, and quantum computing are likely to make cyber threats timelines for seeing clear evidence that AI is delivering on its promise. With global AI spending projected to reach $ 2.5 trillion in 2026 and corporate token bills up 320 % since 2023, the pressure to prove AI’ s economic value is bound to increase.
If that evidence fails to materialize, growing investor impatience could trigger a dramatic repricing of AI-related assets. The longer expectations outpace results, the greater the risk of a painful market correction.
DAMBISA MOYO, an international economist, is the author of Edge of Chaos: Why Democracy Is Failing to Deliver Economic Growth— and How to Fix It( Basic Books, 2018). She serves on the boards of Chevron, Starbucks and National Geographic.
24 | FINANCIAL ADVISOR MAGAZINE | SEPTEMBER / OCTOBER 2026 WWW. FA-MAG. COM