This is Stephen Chidwick for Octopi Poker, and today I'm going to be talking about what I think are the most common misconceptions about GTO poker.
Why GTO Isn't Just About Memorization
I hear all the time that people think playing GTO—or learning to play in a GTO style—is all about memory. It’s about combing through lots of charts, remembering what to do in all these different situations, so that when the situation occurs in-game, you can just recall what you learned and copy that strategy.
I think this is a huge misconception.
First of all, it’s completely impossible. If you think about even one hand of poker on a single board—just button versus big blind—there are countless different combinations. Every single decision point is a new node in the game tree: I could bet small, bet big, check; you could check-raise small, check-raise big, and so on. For every one of those nodes, I’d need to remember a strategy for every possible combo I could have—hundreds of combos per node. Multiply that by every position pairing, stack depth, and tournament stage with various ICM pressures, and it becomes unmanageable.
Even if you could memorize everything, you'd spend your entire life just trying to click through the decision tree.
So instead of doing that, what I find beneficial about studying GTO is learning principles—trying to spot patterns in the data that deepen your theoretical understanding. Those general principles or heuristics can be applied to new situations in-game.
That’s one of the coolest things about poker: when a solver solves a hand, it plays billions of iterations against itself, trying different lines and eventually converging on an equilibrium—one where both players know what the other is doing and any deviation is exploitable. As humans, though, we have to sit there and, in 5 to 30 seconds, come up with a strategy on the fly. One iteration, one decision—using only our brains. As advanced as solvers are, they still miss a ton of context. They know nothing about psychology, player tendencies, or physical reads. Our brains—ideally—combine all of that with our theoretical understanding and come up with a novel solution for each hand.
That’s what I find fun about the game.
So, to me, GTO doesn’t take away from the excitement of poker. It adds to it.
GTO Strategy Is Not Boring. It's Creative
Another common misconception I hear is that GTO poker is boring. I think this is completely false.
First, no one is actually playing true GTO. Even the people considered closest to optimal aren't inputting the complexity and depth that a solver does. If you run the GTO trainer and play against it, you’ll constantly be thrown into extremely tough spots—overbets, unusual lines, strange sizings. Most humans simply don’t create that level of difficulty.
But you can learn some incredibly creative lines from solver output.
For example, there's a play we call a demi-bluff, where you bet a hand like bottom pair that gets better hands to fold—say, middle pair—but still gets called by ace-high flush draws. Then on the river, you check back. Was it a value bet? A bluff? It was both. Before I started looking at solver outputs, I didn’t even realize that kind of hybrid value/bluff was possible.
So yes, GTO poker is actually very rich and creative. Not boring at all. Another misconception I often hear is: “GTO doesn’t apply to the games I play. No one plays like that. So why bother?” But that misses the point.
Looking at solver outputs and comparing them to how people actually play is the first step to finding and exploiting mistakes. If you don’t know your opponent is deviating from the optimal strategy, then you can’t counter it effectively.
The goal isn’t to play like a robot. It’s to understand how the game works at a theoretical level, and then apply that understanding to whatever player pool you’re facing. Your opponent might not follow GTO, but you can use your knowledge of it to punish the mistakes they do make.
How to Use GTO in Non-GTO Games
Let’s say we’re looking at a flop of A♠ K♦ 7♣, lowjack vs. big blind in a single-raised pot. The big blind checks, as they should with their whole range. The lowjack is in a strong position—this is a great flop for them.
The solver prefers a bigger c-bet sizing here, and mixes in some checks. But I often see people in practice play this spot as a pure small-size range c-bet.
So what can you learn from this?
First, you now know the solver likes checking some underpairs—like pocket sixes, fives, etc.—and betting larger with strong top pairs and draws. If your opponent is always betting small, they’re likely not following this mix. That means they’ll arrive on turns and rivers with ranges that are skewed—maybe too many weak hands, or too many medium-strength hands.
Now, let’s say the turn is the three of clubs. You’ll see that the solver starts leading a lot—almost half the range. That’s because we have 18% flushes in this node. The ace and king block a large number of suited combos the lowjack would open with. So even if our range’s overall equity isn’t as high, we still carve out value from flushes.
We actually do have a significant equity advantage here.
Now, someone might say, "Okay, but in my games, people don’t lead on the turn—they just check everything." That’s great! So you’ve c-bet the flop, they’ve check-called, and now the turn hits. You know they have three times as many flushes as you do, and they check.
According to the sim, if your opponent had led with half his range, including his stronger flushes, we’d be betting the turn with a more balanced strategy. But now that he checks, his range is stronger than the solver assumes.
So in practice, we want to be more passive. Hands like ace-jack and ace-ten? We might just pure check them. We want to be highly unbalanced toward value when betting the turn.
That’s a key takeaway—even though our opponent isn’t playing GTO, we can use the sim to learn how to adjust optimally because of that.
I hope walking through different boards and strategies helped you learn something useful for your game. And if you had any of these misconceptions, I hope this helped clear them up.
Enjoy learning with OctopiPoker.ai. This has been Stephen Chidwick—see you next time.
