Co-Founder, Private Debt Platform | Bringing Transparency to Private Debt and Asset Backed Financing using Data and AI
There is a lot of talk about how Gen AI can create solopreneur unicorns. In the recent months, my team and I have been working hands on trying to embed Gen AI across select use cases for our business, using ChatGPT4 / ChatGPT4o / Devin / Gemini. Here is my view on hype vs reality: 1. Impact on Software Build Efficiencies (Very Real) As someone with rusty programming experience, I was able to create simple “apps” with the help of my data scientist and numerous attempts at generating and debugging the code with Google colab and GPT4o. Bottomline, our own tech stack which took over 10 months with a few engineers / data scientists can be done now in Half the time or less ( not a scientific estimate). Huge… and I mean game changing gains in software development efficiencies is quite real. 2. Impact on Data Engineering / Data Science functions (Hype as of now) The real Achilles heel for GenAI right now seems to be working with structured data (excel / flat file). It is quite flimsy at transforming and analysing structured data. This is understandable because LLM and its transformer architectures were not meant for this. So, Data engineers and Data scientists will have to continue to do most of the heavy lifting, until a different model emerges. 3. Solopreneur unicorns (Hype): While the technology is not yet ready for someone without any programming experience to create complex software products using prompts, I wouldn’t be surprised if the subsequent versions of ChatGPT, Gemini, Devin, Llama etc.. get us there in a couple of years. However, such solopreneur products / services created using Gen AI will become commodities, given the low barrier to entry. The Product is not the differentiator anymore. The secret sauce maybe your team, your data, your ability to hit bulls eye on the customer problem and your execution. In summary, I think solopreneur Unicorns are a Hype. On the flip side, small start ups become much easier to start given much lower capital requirements. This is great of course.
Aaron Wong
5mo
Coming from DE side, I agree. I tried getting chatgpt to write python/sql data pipelines but lot of times the codes from chatgpt are not very optimised, clunky or just doesn’t work (wrong data). But, it’s really good at generating snippets that performs a single function.
Also with regards to point 3, I think the future is more ability to ask right questions, understanding the industry, process and context and our ability to articulate with the right prompts.
Gen AI Strategy Consultant | AI Powered Digital Transformation,
5moWith regards to point 2 n the latest release, chatgpt does pretty good job on the data pre processing including data transformations. This was my recent experience