On the text leaderboard of Arena, a public scoreboard that ranks AI models by user votes, the top three scores sat about five points apart at the time of writing [1]. The site's own margin of error, its estimate of how far off each score could be, was about five points too. Read strictly, the top of the table was a tie, printed as a ranking all the same. That is the starting problem for anyone working out how to choose an LLM.
Which AI model is cheapest to run?
The price lists look built to answer that: every vendor publishes a rate per token, and comparing two rates takes ten seconds. The trouble is that cost per token and cost per task can rank the same two models in opposite orders.
For years Formula One engines were made faster the obvious way: more fuel, more displacement, more of everything. In 2014 the sport changed what it measured. Each car got 100 kilograms of fuel for a whole race, a third less than before, and a second rule capped how fast it was allowed to burn them: 100 kilograms an hour, measured by a sensor fitted to every car [1].
The AI Act doesn't treat every company the same way: whoever puts an AI system on the market (the provider) carries much heavier obligations than whoever simply uses it inside a company (the deployer). Have company-wide ChatGPT, Claude or Copilot licenses made you one or the other? In most cases, no — but understanding which side you're on, system by system, still matters.
The European pharmaceutical market, worth €313 billion in exports and running a €194 billion trade surplus, is under structural margin pressure that isn't reversing. With patent cliffs threatening $300B+ in lost revenue, rising tariffs, and stricter regulation, brands in the sector are facing growing complexity across content compliance, field execution and commercial performance at scale.
Il mercato italiano degli integratori alimentari, del valore di 5 miliardi di euro e primo in Europa, sta attraversando una trasformazione profonda. Con un consumatore sempre più autonomo, la pressione crescente dei marketplace globali e un quadro normativosempre più stringente, i brand del settore si trovano a gestire una complessità crescente tra relazione cliente, compliance e contenuti su larga scala.
Yesterday I gathered our entire tech team for a conversation I'd been putting off. I kept delaying because the ground won't stop moving. I was rewriting slides the night before as new stuff dropped.
Software development, as we have traditionally understood it, is coming to an end, but software engineering itself is not disappearing. Instead, it is evolving into something fundamentally different.
The world of content management is undergoing a radical transformation. With rising expectations for speed, personalization, and quality, businesses are facing increasing complexity in content production and distribution.
The pharmaceutical sector is facing increasingly complex challenges, linked to the growth of available data, rising cost pressures, and the need to respond quickly to market demands. Artificial intelligence is emerging as a key solution, capable of supporting companies in making informed and rapid decisions.
In the Digital Health landscape, Large Language Models (LLMs) are emerging as revolutionary tools. These models, trained on vast amounts of textual data, are capable of understanding and generating natural language.
We’re proud to share that Hyntelo has received a B score for Climate Change disclosure from CDP, the world’s leading independent environmental disclosure system.
