for bad players, ‘‘There are a lot of really strange plays
now that these guys are making that are eff ective — but
if people saw them back in the day, I think that they’d be
invited into the game every night.’’
Against weaker players, Koon will sometimes inten-
tionally diverge from theoretically perfect poker, bluffi ng
more than he should or betting large when the A.I. says
he should bet small, to take advantage of his opponents’
mistakes. But against the best professionals, he will most-
ly just do his best to replicate the solvers’ decisions — to
the extent that he is able to remember the A.I.’s preferred
bet sizes and the frequencies with which to employ them.
Because he knows his own human biases can creep into
his decision making, Koon will often randomly select
which of the solver’s tactics to employ in a given hand.
He’ll glance down at the second hand on his watch, or
at a poker chip to note the orientation of the casino logo
as if it were a clock face, in order to generate a percent-
age between 1 and 100. The higher the percentage, the
more aggressive the action he’ll take. ‘‘I’ll say: OK, well I
just rolled 9 o’clock. So that’s 75 percent. That’s a pretty
aggressive number.’’ In that instance, Koon might choose
the largest of the solver’s approved bet sizes for his hand,
whereas if the second hand had pointed to 3 o’clock, or
25 percent, he might have checked.
Using optimal strategy is no guarantee, of course,
that Koon will win any particular hand. Given enough
hands, however, the math says he should do no worse
than break even — and will in practice do much better
than that, depending on how far his opponents’ strate-
gies diverge from theoretically perfect play. If you were
to play thousands of hands against a solver, Koon says,
‘‘it’s going to win, I promise.’’
Koon is quick to point out that even with access to the
solvers’ perfect strategy, poker remains an incredibly
diffi cult game to play well. The emotional swings that
come from winning or losing giant pots and the fatigue
of 12-hour sessions remain the same challenges as always,
but now top players have to put in signifi cant work away
from the tables to succeed. Like most top pros, Koon
spends a good part of each week studying diff erent sit-
uations that might arise, trying to understand the logic
behind the programs’ choices. ‘‘Solvers can’t tell you why
they do what they do — they just do it,’’ he says. ‘‘So now
it’s on the poker player to fi gure out why.’’
The best players are able to reverse-engineer the A.I.’s
strategy and create heuristics that apply to hands and
situations similar to the one they’re studying. Even so,
they are working with immense amounts of informa-
tion. When I suggested to Koon that it was like endlessly
rereading a 10,000-page book in order to keep as much of
it in his head as possible, he immediately corrected me:
‘‘100,000-page book. The game is so damn hard.’’
In fact, the store of data Koon draws on is even larger
than that. He rents nearly 200 terabytes of cloud storage
for the game trees he has developed since he started
working with solvers. Players sitting down to in-person
games have no way to access all that information at the
table, but that limitation does not necessarily apply to
poker played over the internet. Automated bots, especial-
ly in low-stakes games, have been a problem for internet
poker since before the rise of solvers, but now human
players willing to skirt the rules can look up A.I. strategies42 1.23.22
an injury at West Virginia Wesley-
an College, where he was a sprinter
on the track team. He made a good
living from cards, but he struggled
to win consistently in the high-
est-stakes games. ‘‘I was a pretty
mediocre player pre-solver,’’ he
says, ‘‘but the second solvers came
out, I just buried myself in this
thing, and I started to improve like
rapidly, rapidly, rapidly, rapidly.’’
In a home office decorated
mostly with trophies from poker
tournaments he has won, Koon
turned to his computer and pulled
up a hand on PioSOLVER. After
specifying the size of the players’
chip stacks and the range of hands
they would play from their partic-
ular seats at the table, he entered a
random three-card fl op that both
players would see. A 13-by-13 grid
illustrated all the possible hands
one of the players could hold. Koon
hovered his mouse over the square
for an ace and queen of diff erent
suits. The solver indicated that
Koon should check 39 percent of
the time; make a bet equivalent to
30 percent the size of the pot 51
percent of the time; and bet 70 per-
cent of the pot the rest of the time.
This von Neumann-esque mixed
strategy would simultaneously
maximize his profi t and disguise
the strength of his hand.
Thanks to tools like PioSOLVER,
Koon has remade his approach to
the game, learning what size bets
work best in diff erent situations.
Sometimes tiny ones, one-fi fth or
even one-tenth the size of the pot,
are ideal; other times, giant bets
two or three times the size of the
pot are correct. And, while good
poker players have always known
that they need to maintain a bal-
ance between bluffi ng and playing
it straight, solvers defi ne the precise
frequency with which Koon should
employ one tactic or the other and
identify the (sometimes surprising)
best and worst hands to bluff with,
depending on the cards in play.
Erik Seidel, a pro who learned
the game in the 1980s, told me that
if players like Koon traveled back
in time just 15 years with today’s
knowledge, they would crush the
best players of that era. ‘‘I think
also that all the people in the game
would think that they were fi sh,’’
Seidel said, using the poker argot