AI may be making companies faster, but the bigger economic story is that it can also scale bad decisions just as quickly — and that is becoming a real risk for banks, payments firms and big tech investors betting on automation. Nguyen Ba Diep, co-founder of Vietnam’s MoMo, put the issue plainly at The Next Forum 2026: if businesses apply AI with flawed thinking, they can “multiply those mistakes a millionfold.”
AI risk in finance grows as adoption scales
That warning matters because AI is no longer a side project. It is moving into customer service, fraud detection, credit, operations and executive decision-making, where errors can hit revenue, compliance and trust all at once. For investors, the question is not whether AI boosts efficiency — it often does — but whether the companies deploying it have the judgment and controls to keep those gains from being overwhelmed by costly mistakes.
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Diep’s example was simple but telling: where a senior manager once received three or four reports a day, employees can now generate 30 to 40 reports in an hour and expect immediate responses. In other words, AI can overwhelm human oversight as easily as it can improve productivity. That is why the long-term winners in AI will not just be the firms that automate the most, but the ones that can filter signal from noise and keep quality intact at scale.
MoMo’s own platform offers a useful case study. The company says it serves more than 30 million users, connects over 500,000 businesses and merchants, and works with more than 70 banks, financial institutions and insurers. It also says its systems flag nearly 29,000 unusual transactions a day and proactively warn more than 10,000 users about potentially risky activity. That shows how AI can be put to work defensively, not just offensively — a crucial distinction for financial services, where trust is the product.
For investors, that makes AI both an opportunity and a due-diligence test. The upside remains enormous for platforms that can use AI to personalize services, reduce friction and improve risk management. But the downside is equally real if AI is layered onto weak processes or poor governance. That is why companies such as Microsoft, Nvidia and Alphabet still face a familiar long-term market truth: the best AI businesses are not only the ones with the most powerful models, but the ones with the strongest discipline around deployment, data and oversight.
Regulators are watching the same fault lines. Anthropic’s push for a federal AI safety bill, along with broader warnings from policymakers and institutions such as the IMF, underscores a growing recognition that faster AI adoption needs tighter controls. The more AI becomes embedded in finance, cloud and enterprise software, the more investors should expect scrutiny over safety, reliability and legal exposure.
The takeaway for long-term investors is not to avoid AI, but to respect its compounding power in both directions. Used well, it can create durable advantages and new cash flows. Used carelessly, it can scale errors faster than management can correct them. In a market still captivated by AI growth, the companies most worth owning over the next 3 to 10 years may be the ones that pair innovation with restraint. That makes this a story worth watching closely.
| Entity | Gains | Losses |
|---|---|---|
| AI-enabled firms with strong controls | ▲Faster scaling, lower costs | ▼Slower adoption if overly cautious |
| Companies with weak governance | ▲Short-term speed | ▼Bigger errors, compliance risk |
| Investors in disciplined AI leaders | ▲Better long-term compounding | ▼Missed gains in hype-driven names |
| Regulators and safety advocates | ▲More influence on standards | ▼Less room for unchecked deployment |
