It was exactly an year ago when a relatively unknown organization released a low-key 'research preview' product. That product was ChatGPT and rest, as all of you know, is history.
By many measures (like the one depicted below) and opinions of well renowned experts, the innovation unleashed by Google's Transformers paper and Open AI's commercial success has ushered us into a take-off phase of AI. (fast or slow take-off is the topic of many a twitter debates).
In this post (Day 5 of 20 posts in our series on Generative AI), we'll discuss the emerging narrative around 'Co-pilot for X', where X is a typical knowledge task in a typical business setting (we're not talking about discovering new theories of Physics or solving Math Olympiad problems).
Think of an AI Copilot as your digital Sherlock Holmes, minus the deerstalker hat but with all the smarts. In the world of words and data, it's like having a turbocharged assistant. Writing an email? It's your grammar-savvy sidekick. Crunching numbers? It's like having a mathematician in your pocket. And for legal eagles, imagine a junior associate who never sleeps, sifting through case law at the speed of light. It won’t make your coffee, but it’ll brew up answers faster than you can say "Elementary, my dear Watson!" Just remember, it's brilliant but not infallible – the real detective work is still up to you!
(yes, ChatGPT contributed to some of the above word salad. But you get the picture)
We'll build the argument in 3 parts:
Lets take a short detour towards the aviation industry - which has a long history of incremental technological advancements. In flight navigation, 'Cost of Error' cannot be any higher (its literally a matter of life and death). Spurred by tremendous investments by OEM's and managed under an extremely robust governance / oversight framework, Autopilot systems, which date back to the early 20th century, have evolved from simple mechanical devices to complex, computer-driven systems that can handle many aspects of flight. This transition didn’t replace pilots but transformed their role, emphasizing monitoring and decision-making over manual control.
Now, coming to knowledge work, lets take a look at few factors:
We believe these factors combine to makes Copilot the right paradigm for infusing AI into knowledge work
We believe that today's models represents strong, narrow form of AI, and the best opportunity to integrate AI is with human in the loop, i.e. in a copilot paradigm.
One of the best examples of companies attempting Autopilot is Tesla FSD. And while the AI is stunningly advanced and improving every day, the engineers have realized that achieving the last 1% in accuracy is 100x more difficult than the achieving the last 10% in accuracy, etc. So, it might be counterproductive to wait for the models to become sufficiently advanced that autopilot capabilities could be achieved.
As a business manager, the value that you can extract from Copilot exists today, not in discounted future.
Since the copilot paradigm originated (mostly) with 'Github copilot', lets uncover how a 'coding assistant' can help coders across various levels of proficiency. By their own definition, Github copilot is an 'AI-pair programmer that can help developers write code more efficiently'. It can
We believe that AI copilots are still in their infancy. As of late Oct '23, Github Copilot claims to have over a million paid users (that's about 1% of total Github's user base), across 37K organizations. By any stretch of imagination, that has to be the largest ever deployment of an AI product. There are known issues like it producing code that is not functional or the drift between intent and execution, or code that does not answer the intended problem. Other issues exist like:
We believe that Microsoft's recent launch of Copilot in Office Productivity Suite will further cement this idea. Adoption is likely to be strong due to:
In conclusion, we believe that the constraint on changes is not technological. From an R&D perspective, every technological component required to produce these game-changing goods is currently accessible. It is now necessary to assemble the parts. This new class of products offers businesses and knowledge workers a once-in-a-lifetime opportunity.
We may have just seen first few waves of the Tsunami caused by energy event that occurred on Nov 30th, 2022.
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