Oct
14
I am on my way back from Boston. These days America seems a strange place, pivoting towards extremes and polarised as never before in my experience. It has become a land in which I am afraid to discuss politics. My American friends are still my American friends: charming, cultured, intelligent. I am still in love with them, but they do not talk politics either. We have grown afraid of what we might hear ourselves saying, and what effect it may have on the people to whom we are saying it. It was not like this when I first visited this city in 1974.
So it is best to concentrate on business. Over 100 companies, members of the Outsell community, met together in Boston this week. From my own viewpoint, as a rather ancient observer who has attended each of the previous 17 such meetings, I thought that this was a great success. It had that element of turning point, of mutual and collective recognition of change.Of course we had to get through the ritual of complaints about the state of the USeconomy first. A short spell in Europe would convince most people that the American economy is in a pretty healthy state. I do not want to downplay the problems, but in terms of growth rate and in terms of employment, the American economy is in an enviable state and it is clear that the US based companies that I have spoken to this week have been investing steadily in the future for the past year, a year in which margins have not touched record levels, but are still so significantly restored from the pandemic period that reinvestment is a realistic possibility. But any optimism here is held down under a heavy cloak of caution. It is easier to persuade oneself to invest in infrastructure or in cost saving retooling, or in headcount reduction through technology adoption than it is to invest in new product development for markets which are emerging but which do not quite exist yet.
At the previous meeting of this community , in 2023, attendees were given no doubt that the vision of the future was an AI infused vision: machine intelligence was going to drive the world of content generation from publishing and information provision through data and analytics and into a Brave New World of generative AI. This was an important moment. It was like saluting the Internet before the creation of the Web. Or saluting Gutenberg. without knowing that the scriborium had another hundred years of successful operations still to run. in other words, it was a moment of faith and belief and expectation. It was not a moment for concrete demonstrations and outcomes.
Yet recognising change and saluting it has never been more important. The 100 years that they waited from the mid 15th century for the printing revolution to actualise itself had their equivalent in the 14 years that we waited between 1979 (the launch of my first searchable database) to our first real Internet connection in 1993. Sustaining investment during the time period required, in this case,to move digital from dial up to TCP/IP was a real problem. As well as good sense and good judgement it also required, as investment so often does, faith.. And, as I have often said here, the road to AI has not been a short one. In my own terms I dated my own faith from a Media Lab seminar at MIT run by Marvin Minsky in 1985.
So what did I really expect of the 12 months between Outsell 2023 and 2024? I travelled to Boston hoping for more of the same.
More cautious optimism? More gradual exploration of the commercial issues ?What I did not expect was a room full of companies all experimenting in some way or another with the concepts, ideas and investments involved in utilising AI in some way in their business. I sensed a real change in business attitudes. I do not now see information based businesses sitting back as at the beginnings of the digital age, and waiting for something to happen. I do not see small businesses backing away from change, or big data-based businesses saying that they will wait upon big AI tech to sort out the world before they invest for themselves. The picture emerging now is complicated, but it is about engagement and about embracing change. Above all, in the information and communication sector, I sense an eagerness now to look hard at the technology and ask what it may do for them. This time round, rather than become the victims of change, the industry that I have served for the past 55 years seems determined to be an instigator of change. And that is a very welcome relief!
if you strip away all of the analysis and all of the strategy, investment at the final point of decision becomes a balance of hope and fear. Many of the people I have spoken to this week entertain the real fear that unless they can bring costs down and re-engineer their processes, whether they are creating a credit rating or a scientific research article or drafting a legal document, they are going to come up against competitive headwinds, as ungentle as hurricane Milton. As I heard of these developments from the platform and discussed them around the room, it was apparent as well that re-engineering process through AI was also about adding value , and thus increasing competitiveness.
The meeting this week contained a session on improving data quality for AI. Five years ago corporate response to this sort of presentation was negative: it was all very well to talk like this, but these things cost money and nobody was prepared to pay more for better data. Here the atmosphere was totally different. There were few players in the room who were not actively improving their own data, and no one was not aware of the role of machine intelligence in automating and accomplishing this.
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Flying into Boston, my mind was full of Big Tech licensing controversies, the regulatory framework that is emerging and whether we were all going to be swallowed up by Big AI Tech, who would buy our data for a pittance and then “hallucinate “it into the search boxes of our users. These preconceptions were entirely wrong. Much more time was spent discussing case studies and exemplars. There was more emphasis on using small learning machine environments, RAG, and applications dependent upon company-owned data sets, plus some licensed files, and publicly available data inputs . Yes, you could create your own training sets for your own application in your own niche.. You did not need to ride on the back of the big AI developers:some were using open source or licensed AI software. The urge to experiment was borne both from a perceived need to anticipate competitive threat – “ they seem to think that anyone can come in here, buy the right data sets, engineer them to user requirements, and take our markets away from us “– and an optimistic feeling that the existing players were in a more powerful position than they had ever realised. They had the customer relationship , they knew the needs and requirements and the ways in which those were changing, and above all, they had the data. There was a confidence in the room that well structured, well prepared data which effectively covered the niche in depth was a key strength. By the end of the conference participants were reminding each other that AI would never succeed unless it had access to data of the highest quality. It was all very well to hoover up the dark web. It was apparently okay scrape other peoples websites. It was even okay to pay puny license fees as a gesture to regulatory conformity. But, the players in the room seemed to be saying, we have the real data and we know exactly how important it is. And we also now know that AI is only as good as the quality of the data it ingests allows it to be. AI as a consumer experience will be very popular, and will build very large starter audiences, some of whom will stay as they discover real personal value in its usage. But in business and professional and research marketplaces, things will be very different. Here, in the next 2 to 3 years, there will be real opportunities to lop as much as 30% off processing costs, while adding a similar amount to data value. The title of the Outsell conference was “From hype to reality“. Many members of this community clearly now recognise reality as opportunity.
By the end of the meeting, my mind began to fill with the next projection. How will the use of our AI products and services, as we develop them, change the way in which our end-user clients work? it is clear that major pharma players like BioNtech are rolling out AI research assistants. Similar developments will take place in every industrial and commercial sector. Should we be their partners in these developments? Or will their developments be fuelled by licensing our data and cheating us out of the value to be added to it? Exploring those unresolved problems remains the role of research and thought leadership, but at least, at this point, we are discussing the future with a common agreement that there is a future: in 1985, starting out in strategy advisory work, I faced the initial difficulty of persuading anybody that digital did present any sort of future at all . Even if we acknowledge that we had not yet travelled very far, those of us who left Boston last week seemed sure of the direction of travel and were optimistic about its potential.