Debunking Common AI Myths in Alternative Investments

As artificial intelligence continues to dramatically shift the alternative investment landscape, several misconceptions persist about its implementation, cost, and impact. Let’s separate fact from fiction to help investment firms make informed decisions about AI adoption.

• AI is biased: TRUE 

While AI can exhibit bias, this often reflects societal biases in training data. In alternative investments, this bias can be effectively mitigated by focusing on bounded, associative tasks and using carefully curated, industry-specific data. By constraining AI to well-defined tasks with clear parameters and verifiable outputs, we can minimize the impact of potential biases while maintaining high accuracy and reliability.

• AI is too expensive: MAYBE

Due to the inflated hardware costs, the cost of quality assurance and validation of those AI solutions can also run up costs easily. Initial implementation costs can be significant, but the long-term ROI often justifies the investment. AI can dramatically reduce labor costs associated with repetitive tasks. It may be extremely expensive today, but as the hardware required to support AI becomes more commoditized, the cost is likely to decrease over time. For firms looking to modernize their processes, create long-term efficiencies, and position themselves ahead of the rest, the cost is likely worth it.

• AI is inaccurate: TRUE 

The risk of AI producing incorrect information can be minimized by constraining AI to bounded tasks and implementing rigorous validation processes. However, if you wonder outside of the bounded tasks, AI can confidently present convincing but 100% incorrect data, otherwise known as hallucinations. While keeping queries “bounded,” it’s critical for firms to incorporate a human feedback system.

• AI is exposing sensitive data: MAYBE 

Concerns about AI exposing sensitive data are manageable through internal hosting, strict data governance policies, and integration with existing security frameworks. Without these guardrails, the risk of your data being leaked (or being fully exposed) is extremely high. If you, or a trusted third party like Canoe, can host your model, the risk of exposing your data is greatly reduced due to the security parameters that are set in place. For example, Canoe has an eight-layered cybersecurity infrastructure to safeguard our clients’ data.

• AI is approaching human intelligence: FALSE 

For the foreseeable future, this is completely false. Regardless of how much we learn about the human brain, it always remains a thousand times more complicated than we thought it was. In May of 2024, scientists performed a cellular scan of one cubic millimeter of the human brain. If they had tried to include the whole brain, it would have required 1.6 zettabytes of storage, $50 billion for the hard drive alone, and a 140-acre data center. Despite exponential progress on the AI front, we’re still likely decades away from truly tapping out how complex the human brain is and being able to match it.

• AI is causing job displacement: TRUE 

AI is more likely to augment human capabilities than cause wholesale job losses, shifting focus from repetitive tasks to more strategic, high-value activities. However, many of the jobs that exist today, including software engineering, once did not exist. Some jobs will become more efficient and higher-value, and some will disappear altogether, but new jobs will also be created to fill new needs.

We’re finding that our clients’ experiences often contradict common AI myths. For instance, rather than job displacement, many report that AI has allowed them to reallocate talent to more strategic roles.

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This information was published in February 2025. All data points and insights referenced within the document are as of that date.