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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.
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 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.
Il settore Energy & Utilities sta vivendo una trasformazione senza precedenti. Con oltre il 75–85% dei clienti già passati al mercato libero, il contesto competitivo è più intenso che mai: clienti più informati, più mobili e più esigenti mettono alla prova ogni giorno la capacità delle aziende di comprendere e trattenere il proprio portafoglio.
L’Intelligenza Artificiale sta entrando sempre più nella vita di tutti i giorni, ma quanto è realmente diffusa nel lavoro degli informatori scientifici? E, soprattutto, quali opportunità offre per trasformare il loro ruolo in chiave strategica?
In un contesto dove velocità, pertinenza e personalizzazione sono essenziali, le aziende si chiedono: come possiamo decidere meglio, più in fretta, e in modo più rilevante per ogni cliente o processo?
L’Intelligenza Artificiale è molto più di una moda passeggera. Nel settore Life Science, e in particolare nel contesto italiano, l’AI sta generando cambiamenti concreti per medici, pazienti, ISF, aziende farmaceutiche, ospedali e istituzioni.
