The 2025 GenAI SitRep

 

As 2026 announces itself as the year where “the hombres will be separated from the niños” , I’d like to look back at the evolution in GenAI and present a few predictions on it for the next two to three years.

When ChatGPT was launched in November 2022; it immediately raised high hopes for a new wave in machine learning, making the interaction with neural networks more intuitive using natural language. And as time progressed, the models were trained on ever increasing data volumes as the incumbents saw this as the only way to win the platform war and telling their investors about first mover advantage to get funds at surreal valuations. Now they are finding out two things. One: size is not the distinguishing factor in the platform competition. It’s about purpose built and well curated models where the competition is heading and two: there is no platform competition because the platforms were already there: Microsoft integrating ChatGPT in Office365 and Google doing the same with Gemini in its product suite.

Other platforms like Meta, shopping platforms like Amazon, Ali Baba or banking applications, either promote home grown LLMs or use one or more models among the hundreds of thousands open access models they can find on Hugging Face.

Nevertheless, there is one aspect of platform economics emerging, the creation of exit barriers. Once you’ve chosen for a platform to integrate in your business applications the SDKs and the models used and improved by RAG and other means (which we are working on) will increase switching costs.

The first three years in GenAI were a lot about overpromising and under delivering. I am not checking if Gartner already talks about the trough of disillusionment but I have met a few clients where disillusion has set in. Cases of organisations reducing their after sales service staff and replacing them with chatbots had to reverse their decision. We all know of lawyers producing phony legal precedents are being fined.  Not langer than a couple of weeks ago, a judge in California has fined plaintiffs’ law firm Hagens Berman, one of its partners, and another lawyer a combined $13,000 for the “misuse” of artificial intelligence in several court filings in a lawsuit against the parent company of adult content social media site OnlyFans.

Much of the GenAI offers are “inside out” like when a top official of ChatGPT bluntly stated that the users will need to learn what they can do with their product. To use Lee James and Warren Schirtzinger’s concept of the marketing chasm, later popularised by Geoffrey Moore’s terminology, we are very much pleasing the techies but the pragmatists are left in the cold and are waiting on the other side of the chasm for a useful application to cater to their needs.  That doesn’t absolve the pragmatists from the obligation to examine the impact of GenAI on their organisation, their business processes and their talent management. But today, little efforts are made to support them in that endeavour.

Forget about network effects, it’s all about trusted data

The real battle will be fought on the data terrain. And this is where our knowledge modelling methodology comes in play. Its output combines the strengths of “older” machine learning techniques with state of the art LLM technologies and prepares domain knowledge for reliable use in a well-defined, professional context.

It is also about scope management. There is a simple, over and over empirically conformed law that illustrates the correlation between consistency and scope of information: the smaller the scope, the higher the consistency.

Let me give you two examples to illustrate this thesis.

In the accounting domain the concept of cash flow is unequivocally defined as the money that flows in and out of a business and is measured from different perspectives: operations, investing and financing. Cash flow is an important metric to determine the value of a business.  But a business valuation in itself is a more fuzzy concept composed of various, sometimes contradicting metrics as not only quantitative metrics like free cash flow, debt to equity ratio, price/earnings ratio etc… are under scrutiny. There are a lot of qualitative or more fuzzy metrics and evaluations like risk, customer loyalty, market position,  growth potential and brand preference that are equally important to this concept.

In the supply chain domain, Solventure’s Bram De Smet pointed out in his book “SupplyChain Strategy and Financial Metrics” that crisply defined metrics like cash, cost and service level become a dynamic and fluid mix as a function of your business strategy. Cash, cost and service level may be opposing forces in realising an optimum outcome to support the strategic direction. The author leverages Treacy & Wiersema’s model of value disciplines, showing how product leadership, customer intimacy, or operational excellence each imply different preferred positions on the service–cost–cash triangle.

Crawford & Matthews’ five value drivers (price, access, service, product, experience) are used to further link market strategy choices to supply chain and financial design.

What we need in GenAI is a method, models and software that support both the low level, crisp and well defined knowledge building blocks while producing meaningful concepts that combine these building blocks in a reliable way. Imagine on top of that, we can implement de Bono’s lateral thinking as a proxy for creativity…

The S-curve, where are we in GenAI?

GenAI technology started out as an expensive platform war with ever increasing investments in GPUs, data volumes and training efforts. But contrary to the classic S-curve evolution, it was rapidly widely adopted as the  major players were seeding the market via free, universally available chatbots in a browser or an app, Microsoft making a move with CoPilot and Google introducing Gemini in its Google Search engine. True to the S-curve’s pattern, improvement is slow as the fundamental concepts are being figured out. This is where we are today.

When the period of rapid innovation and massive adoption will follow is hard to predict but there are signs it won’t take too long. I keep seeing more and more colleagues investigating real life use cases instead of considering it as a glorified search engine or an evolved autocomplete. I think before 2028 we will see some real killer apps like streaming music and video, E-Commerce and social media were for the Internet era. Instead of a top down movement, fuelled by massive investments and even “incestuous” investments compared to Lernout and Hauspie’s bloating its revenue figures during the Internet bubble, we will see bottom up initiatives on these platforms delivering measurable value.

We hope the application we are working on, will one day be part of GenAI’s killer apps.