"Artificial intelligence is not a substitute for human intelligence; it is a tool to amplify human creativity and ingenuity." - Fei-Fei Li, Co-director of Stanford's Human-Centered AI Institute
Generative AI as investment shortcut
Every generation of investors believes it has found a shortcut to outthink the market. While previous eras relied on personal tips, or phone messages or YouTube gurus, today's hot take is Generative AI. It explains balance sheets like a finance professor and serves up stock ideas with calm fluency. But the grave risk is it ends up reinforcing existing biases and moving everyone in a common direction!
Frequency of data generates problems
These Gen AI models do not truly think - contrary to the anthropomorphic projection many have of them - but predict the most statistically likely words in an order. Firms with the biggest digital footprints, such as big banks and IT giants, dominate the training diet of these models. So, the AI suggests what is most visible rather than what is best. That is a grave danger!
Check out our posts on AI investments; click here
Growth stories are shadows
The Indian market is not neatly packaged and is full of chaotic edge cases, as any vibrant market ought to be! Real growth stories are often found silently standing in the shadows, such as specialty firms or small lenders in unknown towns. The AI training data will mostly miss such stories, so the AI model will mostly miss these names in its inferencing (answering stage), and so the real future heroes (multipliers of wealth) will be missed by many. So, GenAI answers are very tricky, from this perspective.
Western versus Indian
India doesn't have any leading frontier AI model. So the models actually behave from a Western perspective. They apply American frameworks like index heavy allocation to Indian markets, which operate on different scripts involving promoter risk and regulatory shocks. Any smart Indian investor will instantly realize the gap.
Everyone has an edge, No one has an edge!
Now if enough investors lean on the same AI assistants, we could all end up owning the same portfolio. Instead of diverse perspectives, we get algorithmic consensus. When too many portfolios hold the same familiar stocks, any minor corrections risk turning to major ones!
Summary
Generative AI simplifies financial research but reinforces familiarity bias by favoring large companies with big digital footprints. Its reliance on Western data often misguides Indian investors with foreign frameworks. Relying on this tool creates herd behavior, leading to risky synchronized portfolios rather than true diversification.
Food for thought
If every investor uses the same algorithm to pick stocks, who will be left to spot the market anomalies that create real wealth?
Check out our posts on AI investments; click here
AI concept to learn: Training data bias
Machine learning models learn from the specific data fed to them during creation. If this input information is skewed toward certain regions or industries the AI output will unfairly favor those areas while ignoring others. This limitation means users receive a distorted view of reality based on what the model has previously seen rather than what actually exists.
[The Billion Hopes Research Team shares the latest AI updates for learning and awareness. Various sources are used. All copyrights acknowledged. This is not a professional, financial, personal or medical advice. Please consult domain experts before making decisions. Feedback welcome!]
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