If you were an online poker player a little shy of ten years ago, your strategy had almost everything to do with psychology and a bit of raw internet bluffing. These are skills that almost seem laughable right now, off the back of the smartest players using AI tools to rehash their online poker strategies.

It all started in 2017 with the arrival of Libratus and other AI tools designed from the ground up to play poker. In fact,Libratuswas the first AI-powered software to beat a pro at poker. Today, even better AI and machine learning tools are changing the very essence of online poker strategy. How?

Poker was (or rather thought of as) a people's game for quite a long time. Your strategy was entirely instinctive because the player had to largely scan opponents' faces and body language for any tells and reads. If a player across the table always rubbed their nose before they bluff, that's all you need to go by, at least until they're wise to it.

That was a craft, and players had perfected it to the point that dozens of books were written about it, including the famousCaro's Book of Poker Tells. It worked until AI came along and started gobbling up millions of hand histories, spitting out strategies so precise that human instinct looked like guesswork by comparison.

Of course, instinct is still alive and kicking, especially in live broadcast poker. AI tools may spit out perfect bet sizes in microseconds, but they cannot tell when a player is scared short. Can AI feel the weight of a final table? Most likely not, yet the ranks of Phil Ivey or Doyle in his prime could. The smartest players today do not have to choose between precise AI data and gut feeling - it is all about doubling down on the best of both worlds.

Libratus did not just beat a couple of poker pros in 2017. It beat them badly, over 120,000 hands of heads-up no-limit Texas Hold 'em, and it did so by learning rather than following a script. It was not taught how humans play. It figured out how to play optimally through sheer computation.

Then camePluribus in 2019, a follow-up from Carnegie Mellon University and Facebook AIthat cracked multiplayer poker, something experts had assumed was decades away. Where Libratus played one-on-one, Pluribus held its own at a full six-player table against some of the best in the world. Poker pros were forced to take a hard look at their strategies and ask what exactly they were missing.

Training tools now incorporate neural networks to give feedback on hand decisions. Take PokerSnowie, for instance. It speaks to AI to compare your play against mathematically sound alternatives and pinpoint o where you are leaking play. You upload a session, and instead of guessing what went wrong, you get a clear picture.

PioSolver works differently, letting players model specific spots and calculate Game Theory Optimal responses across a huge range of scenarios. These are not toys. They are the same tools serious professionals use to study, and they are now available to anyone willing to put in the work.

One of the bigger changes AI has brought to poker training is the capability to practice targeted scenarios without risking a cent. Instead of sitting through hours of live play to encounter a specific situation, players can now set it up, run it thousands of times, and study the outcomes in detail.

Source: International Business Times UK