I think most of those interested in poker by now have already heard about "game theory optimal" poker, usually called GTO poker (GTOP). GTOP basically analyzes the ways in which players seeking to be unexploitable can maximize their gains and minimize their losses; in other words, GTOP studies the results of playing poker optimally. These results are called "equilibrium strategies". In game theory, a Nash equilibrium is a game state where all of the players involved have no incentive to change their strategy set given the strategy sets of everyone else, as they can gain nothing more than what they already have gained by keeping the current strategy. Thus, in poker, to say that players have reached equilibrium is to say that they have no incentive to change the strategies they have settled on, as doing so won't increase their expected payoffs. Being a game of incomplete information with a major chance element, equilibrium in poker will be obtained by players mixing their strategies, which is to say that they won't apply a strict pure rule such as, e.g., "always bet very big on very strong hands and never on bluffs", because then this pattern would eventually be figured out by your opponents and then they could increase their payoffs at your expense by always folding to your big bets when they know their hands are probably worse than what you could have, for example. To preempt exploits, then, a player needs to mix her strategies in a way that makes her unpredictable to her opponents, and this will involve balancing value bets, checks, calls, and bluffs at a given frequency. And of course, by assumption all of the other players will play exactly the same way.
It doesn't take much to realize literal optimal play is impossible for any human player, as the number of possibilities and their relative probabilities are overwhelming to limited beings like us, which is why people interested in figuring out optimal play in many different situations go to computer programs called "solvers" for the answers. Also, the strategies one figures out through GTO studies are based on the assumption that one's opponents are playing optimally, which we know will not be the case even for people who study a lot of GTO regularly. Finally, particular decision points are often compatible with more than one action in an equilibrium set and there will specific such points where the least frequent action will do better. And so from these observations a lot of people complain that GTO is useless and might even be counterproductive, as by relying on it one could be foregoing an exploitative play that would maximize one's returns once they have identified exploitable spots in a given opponent's play. Understandable as it is, this is a bad complaint. As every competent practicing scientist knows, most mathematical models aren't straightforwardly applicable to real world situations because they have a lot of simplifying and unrealistic assumptions, but they can still be relevant if their crucial relationships are approximated or even totally instantiated in real world circumstances. In poker, trying to be as balanced as possible based on GTO thinking will more often than not be more profitable in the long run than using an exploitative approach as a default, and striving to be unexploitable is especially important considering the heterogeneity of opponent types there exists in poker as well as their learning in the game dynamics. To avoid the impression that this is simply a mere assertion, I'll just note that it's not by chance that most top level players on stupidly high stakes games and tournaments (live and online) play according to GTOP, many of them even using solvers themselves to study ways to improve their game. Moreover, GTOP isn't incompatible with exploiting mistakes or weaknesses one's identified in a game: if you know that someone is playing poorly in a particular way, you'll likely increase your payoffs at their expense by exploiting such and such mistake and should do so, and by being aware that this is out-of-equilibrium play you'll be able to more easily revert to equilibrium play if you notice the game dynamic has changed and thus will avoid becoming exploitable.
All of this finally brings me to this book. Brokos manages to give readers a very good introduction to GTO poker. All of the important concepts for playing good modern poker are there: equity, expected value, card ranges, card blocking etc. And all of the concepts are introduced carefully and step by step by using simple toy games so that the reader will be comfortable with them before working with other concepts that build on the previous ones. So I believe there's a lot to be gained by interested lay people and even by those who already have some experience with trying to apply strategic reasoning to poker, as the concepts and perspective the book provides will help both the former and the latter with how to better systematize their strategic thinking. For example, I've benefitted a lot by thinking in terms of range targetting and card blocking, which were concepts I've first seen elsewhere but that are also present in the book.
While I do believe it's a very good and accessible introduction to GTO, I'd recommend that people very unfamiliar with basic statistical and game-theoretic concepts and reasoning get a little grip of these before (or during) reading the book. Brokos does introduce all of the important concepts in the book in the simplest way he can, but I'm afraid it might not be enough for a lot of people too unfamiliar with them, as one can easily struggle with later parts of the book that are counterintuitive if one is not used to statistical and game-theoretic thinking. For example, people used to statistical thinking will quickly see why equilibrium strategies tend to do better given the assumption that a player will play an indefinite amount of games, even if particular out of equilibrium or less frequent equilibrium plays profit in particular instances, while people not used to statistics will probably tend to take particular instances as evidence against the importance of envisioning equilibrium plays, thereby downplaying the very important notion of adopting a pattern of behavior that's better off in the long run with multiple decision points that will be very different from the particular instance one is attending to at any given real life situation. To be clear, here I mean only that one will benefit more if one is comfortable with the logic of game theory and statistics, and so it's not necessary at all that one should also dive into the full blown mathematics of these disciplines. Nowadays there are several youtube channels dedicated to teaching and fixing GTO reasoning, so another way of improving the book's reading experience (and going beyond what you learn there) is to check on them. My favorite ones are Finding Equilibrium and GTOWizard, but there are enough of other options out there for different tastes.
A shortcoming worth mentioning here is that Brokos provides no references, neither did he bother to make a "further reading" list, which is standard practice in introductory books.