Alex Ledovas gives his take on what we can expect in the coming 12 months. Freight markets have always been driven by fragmented information. Rates, vessel supply, cargo demand and congestion rarely exist in one structured dataset, while some of the most valuable commercial intelligence still lives in emails and informal conversations across platforms such as WhatsApp or Teams. AI-driven shipping technology is starting to change how this information is interpreted. Advances in machine learning and language processing now allow unstructured communication to be analysed systematically, turning scattered conversations into identifiable patterns that reveal underlying market dynamics. Shipping communication follows a language of its own, shaped by abbreviations, implied meanings and industry-specific phrasing that often signal commercial activity or sentiment well before they appear in formal data. By learning these patterns, AI helps structure fragmented information into clearer signals around how supply and demand evolve across regions and vessel classes. When combined with traditional datasets such as vessel movements and port activity, this structured view adds critical context. It allows market participants not only to see what is happening, but to better understand why conditions are changing — an increasingly valuable capability in fast-moving freight markets. From structured insight to smarter decisions Once freight intelligence is structured, the opportunity shifts to how it is delivered and used. Historically, supporting decisions required time, experience and manual effort. Analysts prepared reports, brokers compared scenarios mentally, and teams relied heavily on intuition built over years in the market. While that expertise remains essential, the growing volume and pace of information make it harder to process everything effectively. Shipping technology is now entering a more evolutionary phase. With freight indicators analyzed in the background, insights can be surfaced in more intuitive and timely ways, aligned with how people actually make decisions. The focus is shifting toward answering commercial questions directly, comparing alternatives, evaluating trade-offs and understanding scenarios in real time. This shift is also driving the early emergence of user-facing AI tools designed to support commercial decision-making. Rather than replacing human judgement, these systems enhance it by combining structured market data, private commercial intelligence and real-time analytics. The breakthrough is not automation of decisions, but faster understanding of consequences — allowing professionals to spend less time assembling information and more time positioning and negotiating. Looking ahead, 2026 is increasingly shaping up as a year when these tools move from experimentation to practical workflow support. The next phase of shipping technology appears less about prediction, and more about interpretation. Reviewing 2025 and what the signals suggest for 2026 Looking back at 2025, the dry bulk market was shaped less by a single dominant trend and more by the interaction of several forces unfolding simultaneously. Commodity trade evolved unevenly, fleet positioning became more sensitive to marginal shifts, and geopolitical tensions introduced persistent friction across global trade routes. Three factors stood out in shaping freight outcomes during 2025 and are likely to remain central in 2026. Coal highlighted how long-term transitio