OpenAI – Deep Research

OpenAI tries to put RFM out of business.

• OpenAI’s new Deep Research offers a new functionality that takes the grunt work out of conducting research but without insight, the ability to be contrarian and data that sits behind a paywall, I think RFM and Counterpoint Research are safe for a while yet.
• Deep Research is a new tool that will take a request and scrape the internet for all the information that it can find and then assemble a report along the lines requested by the user that answers the question.
• The query can take anything from 5 to 30 minutes which sounds very compute intensive, but I suspect that there is a lot of load management going on here also.
• Setting expectations for a query to take a long time means that the research can be conducted when the servers have some latent capacity giving OpenAI more efficient use of its servers.
• This in turn will improve the economics of OpenAI which by its own admission are not working particularly well as its highest-tier product at $200 a month is currently losing money.
• Deep Research will also have the ability to include data proprietary to the user but when this will be available is not clear at this time.
• The demonstration (see here) is impressive and I can see how this is going to commoditise certain aspects of the research business, but it has a number of weaknesses that it will struggle to overcome.
  • First, insight: which is one of the most valuable areas of the market, industry and finance research businesses.
  • Deep Research will be very good at gathering all of the bits of data together and mining the long tail, but it is likely to really struggle when it comes to working out what it all means.
  • This is because like all of its predecessors, it is based on a large language model (LLM) which are based on statistical pattern recognition.
  • This means that it has no understanding of causality and will not be able to distinguish between coincidental factors and those where one causes another.
  • This is fundamental to being able to distinguish reality from fantasy, and all LLMs that exist today still have this limitation.
  • Second, out of the box: where real discovery lies in doing something completely different to what has happened in the past or going against an established opinion or widely-held view.
  • Being based on statistics, LLMs are always going to go for the most likely scenario meaning that they can never be truly creative or come up with something that no one has thought of before.
  • Third, non-public data: where Deep Research only has access to publicly available data.
  • Much of the really valuable factual information sits behind paywalls and is derived from non-public sources meaning that Deep Research will not have access to these datasets.
  • OpenAI has said that in time, users will be able to use their own databases (i.e. research data subscriptions) as part of the inputs but even then, there will still be much of the knowledge base that it will not be able to access.
  • To be fair, humans also have the same problem, but humans are much better at thinking of creative ways to get around these problems and making educated guesses as opposed to just randomly making stuff up as LLMs are prone to do.
• This new product sounds great, but OpenAI was cautious to caveat the results by saying that users should check all of the sources to weed out the hallucinations.
• Furthermore, as Deep Research will draw conclusions based on data that it does not know is real or fake, all of the conclusions that the system creates will immediately be suspect.
• The net result is that this is an early release of a product that was already in the works to put the spotlight back on OpenAI at a time when it is seeking to raise even more money at double the valuation of 2024 ($300bn).
• The fact that a lot of this money is expected to come from SoftBank, explains why this new service is being launched from Japan and OpenAI admitted in its launch video that the trip was arranged at short notice.
• I think that Deep Research and its inevitable competitors could impact the lower end of the market research business meaning that junior analysts are going to be expected to produce more as well as move slightly up the value chain.
• The real threat though is to outsource research businesses that currently use humans to do these sorts of tasks and they may find that demand for their services falls off a cliff.
• In the absence of LLMs being able to understand cause and effect and therefore properly reason, businesses like RFM (which sells insight and opinion) and those that have proprietary data (like RFM’s partner Counterpoint Research) should remain largely unaffected.
• All of OpenAI’s competitors are also likely to launch similar products (Google has already but it is not publicly available yet) and we are certain to have to incorporate these services into our workflow to remain competitive.
• This means a change to the way in which we do business but RFM is not retiring just yet.