You don’t see it immediately. The first hand looks normal. A limp raise, a fold, a nod from a guy in a baseball cap who reeks of yesterday’s whiskey. But then something feels off. His timing-too perfect. His bet sizing – mathematically sweet, as if the man was born with GTO charts whispered into his ear. And that little break before the river? It’s almost… scripted. That’s when the realization hits: maybe this isn’t a guy at all. Perhaps I’m playing against a bot.

Now, don’t laugh. It’s not paranoia. Bots exist, and they are getting smarter every freaking day. Old poker hacks for the game included the low-tech: tuck an ace up a cuff or into a sock or up a sleeve; gawk some at reflected sunglasses; or smuggling a poker cheat-sheet onto the game and hope no one said anything. Cute tricks. Almost nostalgic. Today? Forget it. (And the hacks have metastasized into something scarier – entire neural networks with firing synapses dedicated to running poker AI algorithms that don’t get distracted, don’t tilt, and sure as hell don’t spill beer on their hoodie.)

How It All Started (and How I Found Out the Hard Way)

And you know what’s funny? I used to think I was ahead of the curve. Bought and downloaded every poker AI tool, every app that promised to train me into the next Phil Ivey. You’ve heard of it – PokerSnowie, DeepStack AI, that crusty old poker bot research from school projects that everyone said looked like someone programmed it on a calculator. I loved them all. Thought they’d teach me how to crush online tables, maybe even outsmart the odd bot.

Spoiler: they didn’t.

One night I was chasing a streak that wasn’t a streak and I fired up a mid-stakes table on some rando site (I won’t even tell you which one). All was well until I discovered that every move the “player” made was the right one. Not good-flawless. Fold equity calculated like clockwork. River bluffs that had been logical in theory but inhuman in sensation. I sat in front of the screen, and thought, Is this what playing against God is like?

Turns out, no. It was worse. It was AI poker – cold, merciless, and intended to bleed me dry.

Bots Don’t Breathe, But They Do Bluff

People think bots can’t bluff. That’s cute. Ever heard of Pluribus? And the agency behind the design of that monster computer? Facebook and Carnegie Mellon cooked that monster up, and it didn’t just play; it played better than all the rest. And don’t forget DeepMind’s poker bot experiments. They produced strategies we didn’t even have names for. Poker AI strategies that cause the old school grinders to weep into their coffee, crushing the machine that folds those hands they thought they’d call with, and the machine that bets 3/4 on rivers instead of checkcalling or checkraising.

And if you’re thinking, “Oh, that’s just high-stakes science nerd stuff, buddy, welcome to the rabbit hole. Now, there’s a whole cottage industry. Poker AI software you can “accidentally” download! Bot software for buying online. Hell, there are even a few sleazy holes in the wall that offer you a poker cheat bot yourself. It is all out there, waiting for someone too greedy – or too lazy – to resist.

Continue reading →

There’s something oddly thrilling about seeing a tiny chance for something huge. Just a small number, a fractional whisper, and yet the heart speeds up as if it knows a secret the mind refuses to admit. Is it greed, thrill, or an illusion of control? I’ve asked myself that question countless times, and it finally made sense when I stumbled across a study showing that people, time and time again, picked… not the “reasonable” choice.

They picked the skewed one. And not metaphorically skewed — mathematically skewed. In the experiment, participants faced options with equal expected values, the same math on paper, but different moods in the numbers. One was safe and predictable. The other was asymmetric, a distribution where a tiny sliver of probability promised a massive payoff. You can guess which way the votes went: toward the jackpot-shaped mirage.

We Crave Imbalance More Than Stability

No exaggeration here. The preference for positive skewness is hardwired, deeply ingrained in the way we weigh risk and reward. Behavioral economics has been whispering about this for years, but watching the numbers play out hits differently. People overestimate small probabilities and romanticize rare outcomes, whether it’s a lottery ticket, a speculative trade, or a sterile lab experiment with meaningless payoffs.

And it makes me wonder: is that why mobile games keep dangling loot boxes and why e-commerce spins their lucky wheels? Because we respond. Not just respond — we crave it.

Sure, they taught us about rationality, expected values, utility functions. But when it’s decision time, something else takes over — a module in the brain that runs on hope, not spreadsheets. It remembers that one miraculous “yes” louder than a thousand predictable “meh” outcomes.

From Games to Big Life Moves: Same Rules Apply

Here’s the twist: this isn’t just about slot machines or gacha mechanics. Skewness lives in investments, career bets, even education. We’re willing to pour years into chasing a rare breakthrough instead of stacking small, guaranteed gains. And sometimes, that gamble creates revolutions, startups, and inventions. But other times? It’s a trap dressed up as destiny.

Positive skewness isn’t a free lunch — every rare shot at glory comes with a mountain of empty attempts. And here’s where cognitive biases perform their magic trick: we conveniently ignore the pile of failures to daydream about the one dazzling win.

Reading that study made me pause: where’s the line between inspiring risk and plain delusion? Can we feel that line? Or do we need technology that counts without the pink filter of optimism? In finance, algorithms already do that. In gaming, recommender systems adjust drop rates. And in education, AI-driven systems are building structured growth paths, replacing patchwork luck with gradual mastery. Less poetic, sure, but brutally effective.

And yet — here’s the fun part — education designers are also sneaking in lottery elements. Random badges, bonus points, surprise rewards. Why? Because our psychology thrives on rare delights, even in classrooms.

A Personal Confession: The Day I Lost and Still Smiled

Continue reading →

Every so often I’d catch myself mid-hand wondering: just how does this poker AI tool actually work, beyond the hype? A voice narrates, and it nudges me further. I remember my first introduction to DeepStack’s deep counterfactual value networks-poker AIs that could condition on distributions of hidden hands. That put poker AI, poker bots and poker AI research on the map. Now there’s Supremus. Let’s tell that story with missteps, and a human spark.

(Author’s note: I didn’t think so at the outset.)

Reimplementing DeepStack and noting flaws in poker AI development

The reimplementation of DeepStack in practice loses to Slumbot. I observed data: reimpl lost 63 mbb/g to Slumbot despite still having low exploitability. That contrast became one of the first real-world lessons in Supremus poker AI research, where raw theory met practical results. That paradox struck me as strange – low exploitability yet losing all the time. I noted human-like error. Author’s note: I have always wondered why deep counterfactual value networks had not become more commonplace. That’s because the version of DeepStack lacked head-to-head strength, even though it was a milestone in the development of poker A.I. software. That made me question whether AI poker techniques provided any actual edge beyond theory.

Upgraded CFR variants and richer search: the birth of Supremus with improved poker AI algorithms

Next was the tweak of DCFR+, which increased the rate of convergence. Instead of the quadratic weighting of classic CFR, they deferred policy averaging and used linear weight for clearest results. So they updated both players at the same time and observed faster learning – very much not expected. Author’s note: That was like whittling away at an opponent until they cave. Supremus augment vanilla CFVnets with neural network value functions at the end of each lookahead. That made poker AI strategies more clear. This is not poker cheat or learning to cheat at poker or even poker cheat sheet; this is actually learning machine learning at the poker table.

GPU acceleration and massive training: how poker AI tools scaled

Supremus ran fully on GPU. It went through 1,000 DCFR+ iterations in 0.8 seconds, six times faster than DeepStack. This was a breakthrough moment in Supremus poker AI research, allowing large-scale strategy refinement in record time. The speed meant they could slash exploitability to 3 mbb/g over 5,000× quicker. Training data increased all of the following numbers are in the millions: River network trained on 50, turn on 20, flop 5 mil, auxiliary 10. Compare that to the relatively smaller number of examples used by DeepStack – the flop validation loss fell, from 0.034 to 0.011. I sensed the change – Supremus had turned into a beast of machine learning in poker. Author note: If you can imagine teaching poker bots billions of hands instead of thousands.

Supremus against Slumbot and beyond best poker AI performance

Supremus won 2.6 million chips over 150,000 hands-176 ± 44 mbb/g. DeepStack lost 63 mbb/g. Supremus doubled the profits relative to those of DeepStack. Which made it the best poker AI out of the bots. No speculations about “what if” – just the numbers, unambiguous wins. Performance speaks. We substitute “if DeepStack had done X” with observations. We see Supremus dominates.

Continue reading →

If you’re grinding online poker in 2025, you’re playing against more than just human opponents. Whether you like it or not, bots are here, blending machine-like precision with human-level disguise. Some bots work as harmless training tools, others as ruthless cheats that drain the lifeblood of the games we love.

That’s why understanding where bots stand — legally and ethically — is more important than ever. Before you even consider building, buying, or fighting one, you need to know how the laws, poker platforms, and community norms line up. Let’s break it down, one real-world piece at a time.

Defining a Bot: Not All Code Is Evil

First, what exactly is a poker bot? Technically, a “bot” is just code that automates poker decisions. But its intent and use-case change everything.

• Solver apps: Tools like PioSOLVER or GTO+ help players study ranges and spots after sessions. These are generally considered legal and even ethical.
• Poker bots: These actually play hands against live opponents in real time without human intervention. Most platforms strictly forbid them, and they are broadly seen as cheating.

The problem is that the line can blur. Some players use HUDs or data overlays for a small edge — but at what point does “assistance” become “automation”? That gray area is where many ethical and legal debates happen today.

Global Legal Landscape: Where Bots Stand

Poker law is notoriously patchy worldwide, and bots slip into that confusion. Let’s look at three key regions:

United States
There is no explicit federal ban on poker bots, but virtually every regulated site’s terms of service (TOS) bans them. State-level regulators, like those in Nevada and New Jersey, back up these TOS with potential license suspensions if platforms fail to enforce anti-bot measures. Practically speaking, if you run a bot on a licensed site, you risk permanent bans and loss of funds — not to mention potential civil suits for contract violations.

Europe
European gambling regulators typically view bots as a threat to “fair play.” For example, the UK Gambling Commission requires licensed poker sites to actively monitor and block automated players. Germany’s regulations even explicitly name bots as forbidden. Fines, license penalties, and platform bans are common if you get caught.

Asia
In Asia, where a patchwork of national rules meets huge unregulated markets, bots flourish. Many Asian offshore sites do not strictly police bot use, creating an arms race of bot vs. bot, harming the casual player ecosystem. China, which outright bans most gambling, still sees underground bot activity in online social apps, though technically it’s illegal.

In short, the law may vary, but the platforms almost universally ban bots under their TOS — meaning you risk losing your bankroll, even if there’s no criminal statute against you.

Terms of Service: The True Law of Online Poker

While governments struggle to define bots, poker platforms do not. The TOS is your real rulebook. Break it, and you are out.

Major poker rooms explicitly classify real-time decision automation as cheating. That includes:

• Pre-programmed scripts
• Fully autonomous decision engines
• Tools that read hole cards through screen scraping
• Anything that mimics a human’s real-time choices

In other words, if you push a button and an AI acts for you, you’re breaking most sites’ rules. And platforms have stepped up detection in 2025, including:

• Mouse-movement tracking
• Response-time randomness checks
• Behavioral pattern detection
• Even honeypot tables to lure and catch bots

Violating TOS usually means confiscation of funds, lifetime bans, and public shaming in site transparency reports. Unlike courts, the sites don’t have to give you due process — they can act immediately. That’s the harsh reality.

Solvers, Trainers, and “Fair” AI Tools

Let’s be fair: Not every piece of poker code is a cheating device. There’s a whole category of software that supports player development without crossing lines.

Continue reading →

When people think about online poker, they tend to fall into two extremes.

Either it’s a harmless game of cards, played casually between friends or hobbyists—or it’s a dark tunnel to addiction, financial ruin, and obsession.

But reality, as always, is more complicated.

Thanks to a large-scale prospective epidemiological study of internet poker behavior (spanning over two years and thousands of tracked participants), we now have a clearer, data-driven view of how online poker is actually played, who plays it, and where the real risks lie.

Let’s sort out the key lessons—and why they’re needed now more than ever.

Study Design: Not Another Survey

Much of what we know about gambling comes from self-report data. So, one asks individuals about how frequently they gamble, how big they lose/win, and so forth.

This research accomplished something new.

It mimicked real player activity based upon data directly taken from gambling site logs, combined with participant self-reported health, demographic, and psychological data.

The researcher followed over 2,500 frequent online poker players for 24 months, which was one of the largest, behavior-based studies ever performed in the online gaming industry.

Key Finding #1: Most players are low-risk, recreational players

One of the strongest conclusions

The majority of online poker players do not have problem gambling.

The overwhelming majority of respondents

• Played occasionally (1–2 times)
• Spent moderate quantities
• No negative psychological or financial impact were reported.

This supports the argument that internet poker is, for most, a skill-based game, an avocation, rather than an unsafe habit.

But that is only part of it.

Key Finding #2. A Minority Causes Most of the Losses

About 5-8% of players were found to have behavioural profiles consistent with problem gambling, and were responsible for over 60% of all net losses in the sample.

these players showed

• Increased frequency of sessions over time
• Raising deposit requirements
• Following losses in downswings
• Ignoring or avoiding one’s own boundaries

That is, most manage. But those who fail to manage manage poorly—and in grossly disproportionate numbers.

Key Finding #3: Young men at greatest risk

One stood out among the high-risk subgroups

Male 18- to 34-year

These players were:

• More impulsive (based on psychometric tests)
• More likely to play after midnight
• More likely to play multiple tables simultaneously
• More likely to blur the line between poker and other forms of gambling

This confirms earlier clinical findings suggesting that impulsivity and sensation-seeking traits correlate strongly with problem gambling behavior.

Key Finding #4: Early Behavior Predicts Future Harm

One of the most valuable aspects of a longitudinal study is pattern detection over time.

Researchers found that:

• Players who doubled their session frequency in the first 6 months were 4x more likely to show problem behavior by Year 2
• Ignoring bankroll limits in the first year was a predictor of eventual account closure due to self-exclusion

These patterns suggest online poker platforms could use early behavioral flags to pre-emptively intervene—with reminders, cool-down prompts, or voluntary breaks.

Key Finding #5: Skill Level Correlates with Risk Reduction

Here’s a fascinating twist.

Continue reading →

A Metaverse That Deals from the Bottom of the Deck

Decentraland promised a user-run playground of art galleries, brand pop-ups, and late-night dance floors. Then poker strolled in with a chip-clack and changed almost everything. By early 2024 the platform’s four virtual casinos hosted ICE Poker tables that pulled in well over half of Decentraland’s weekly active visitors – even though the venues cover barely a postage stamp on the 301 × 301 world grid. The hook is simple but sticky: win tokens, chat in real time, and flex custom NFT wearables that double as table stakes. If you want a seat, you need the clothes; if you own the clothes, you’re suddenly invested in coming back. That feedback loop – play → earn → buy gear → play again – has turned a leisure game into the metaverse’s most reliable traffic engine. Critics who once dismissed virtual poker as a gimmick now study it as a template for sticky social design. And the numbers keep pointing to the same conclusion: poker isn’t detouring Decentraland’s roadmap; it’s driving the tour bus.

ICE Poker’s House Edge on Engagement

At peak moments in 2022 ICE Poker logged roughly 12,000 daily active players, dwarfing the counts for live concerts, treasure hunts, and even Metaverse Fashion Week. Blockchain traffic confirms the stickiness: two tiny poker halls generated about a third of all unique daily visitors and a fifth of total time spent inside the world. A typical player session stretches past sixty minutes – an eternity in metaverse attention spans – thanks to multi-table tournaments and on-the-fly social banter. That stickiness spills into the wider grid; foot-traffic heatmaps show spikes in parcels bordering each casino, so adjacent galleries and retail plots enjoy free overflow. Developers know the effect by a snappy name: the Poker Halo. Landowners who secured parcels in Vegas City, the gambling district, now lease storefronts at premium rates precisely because the Halo keeps avatars wandering past display windows. In short, poker turns casual tourists into long-stay residents, and that single behavioral shift rewrites the economics of Decentraland real estate.

Dressing for Success: Wearables as Membership Tokens

Entry to ICE Poker isn’t gated by a password; it’s gated by your avatar’s outfit. Each table requires a branded wearable – digital sunglasses, varsity hoodies, even neon card backs – that exists as an ERC-721 NFT. At first those items cost pennies. Then demand surged. Across 2022 poker wearables represented roughly thirty percent of Decentraland’s total NFT dollar volume, outpacing land sales during several months of crypto doldrums. A secondary rental market blossomed overnight: owners delegate gear to grinders, split winnings, and collect passive revenue while they sleep. Delegation contracts now settle in seconds on Polygon, with cut-and-dry royalty splits coded into the chain. The result feels like sneaker culture fused with DeFi yield farming – collectors flip limited “ice boxes,” while volume players treat jackets and hats as income-producing assets. Even downturns in MANA’s token price haven’t killed the craze; when token profits sag, players simply shift focus to higher-rarity skins whose scarcity props up resale value. Fashion meets game theory, and both leave the table richer.

Anatomy of a Traffic Surge

Continue reading →

In online poker and rummy, one of the longest-lasting disputes isn’t about strategy, rakeback, or software. It’s about one’s identity:

Is this skill-based, or has this just been disguised cleverly as gambling?

This is more than an intellectual endeavor—its answer determines legality in most nations, impacts taxation, platform validity, as well as gamers’ understanding of what they have selected.

Breaking down what science, data, psychology, and law can tell us about skill vs. luck in poker and rummy

What is a Game of Skill?

A “game of skill” is generally one where:

• The outcome is primarily determined by the player’s mental or physical ability, not random chance
• Players can consistently outperform others through better decisions
• Knowledge, strategy, timing, and practice matter more than variance

By contrast, games of pure chance (e.g., lotteries, slot machines) are governed by randomness. No amount of expertise changes your odds on the next spin.

But where does poker fall on that spectrum?

Poker: More Than Just the Cards

Let’s start with poker—particularly Texas Hold’em.

In the short term, of course, luck plays a role. A fish can crack aces. A donk can hit a gutshot on the river. But over time? The edge shines through.

Statistical Evidence:

A 2012 study analyzed 103 million hands from online poker databases. It found:

• Skillful players consistently finished ahead, even accounting for variance
• After about 1,500 hands, outcomes were increasingly determined by decision quality, not luck

Even more revealing:

• The more hands played, the greater the divergence between good and bad players
• Skill “overrides” luck with volume

If poker were mostly chance, you’d expect random distribution of winnings. But that’s not what the data shows.

What About Rummy?

Rummy often flies under the radar, but its skill component is just as real—especially in formats like Indian Rummy or Gin Rummy.

Key skill factors include:

• Memory (what’s been discarded?)
• Pattern recognition (melds, sequences)
• Risk assessment (when to drop or call)
• Bluffing and baiting with discards

While the initial hand is random, how you manipulate that randomness is strategic.

Neuroscience Says: Skill Is Real

Let’s bring in the brain.

Research using functional MRI (fMRI) scans has shown that expert poker players activate different areas of the brain compared to novices:

• Experts use the dorsolateral prefrontal cortex (linked to executive function and decision making)
• They show lower activity in emotional centers (like the amygdala), indicating better tilt control

This isn’t just anecdotal:

“Expert poker players show patterns of cognitive regulation similar to professional athletes or chess masters.” — NeuroGaming Lab, 2022

In short: real skill = real brain training.

Psychology: How Professionals Think

Professional players don’t just “feel lucky.” They study frequencies, use solvers, simulate ranges, and prepare decision trees for complex spots.

This mental discipline:

• Reduces variance over time
• Produces consistent results
• Mirrors cognitive frameworks found in high-performance domains

Compare that to pure gamblers, who chase losses and pray for variance to turn. The mindset is entirely different.

Legal Perspective: What Courts Say

In multiple jurisdictions, courts have ruled on whether poker and rummy qualify as games of skill.

Continue reading →

Poker online enjoys a bad rep.

Ask the general public what they think about, and you’ll hear such responses as “addiction,” “loss of money,” and “harming by way of gambling.” But here’s the catch: while the majority struggle at the virtual tables, a minority of elite online poker players prosper – psychologically, emotionally, and economically.

So what’s their secret?

A pioneering work by University of Sydney researchers delved deep into this question by conducting interviews of 19 top online poker professionals. These were not part-time grinders or by chance upswings – they were full-time pros, many with years of reliable income and emotional discipline.

Their stories show something extraordinary: it’s not merely technique – it’s psychology.

The Big Idea: Poker Doesn’t Have to Hurt

Gambling typically includes damage – financial loss, time addiction, emotional distress. But the players in this research? They are largely exempt.

Why?

Because they don’t bet like a common bettor.

They approach it like:

• A performance practice
• A mental game of endurance
• A business strategy

That mentality is the difference maker.

What Makes Elite Online Poker Players Different?

Elite online poker players not only win more – they also think differently. Here’s how they differ:

They are long-term oriented:

Top players are not in individual sessions. They think in thousands of hands, months or years. They know variance is a part of the game and are not bothered by short-term results impacting the long term.

One player described it this way:

“I play 100,000 hands a month. Why would I care about one bad river?”

That mindset alone prevents them from chasing losses or going down an emotional spiral – two behaviors damaging in a majority of players.

They organize everything:

Top players utilise the use of trackers, solvers, HUDs and journals. All decisions are reviewed. All leaks are targeted.

It’s not just about raw talent – it’s about rigid process.

That’s quite a different picture from the problem gamblers, who bet impulsively, emotionally, or compulsively.

They distinguish ego from outcomes:

You can play by the rulebook and lose at poker. Pros know this better than anybody – and they don’t take it personally.

They do not go for cooler points or for “getting back at” rivals. They make the smart move and move on.

Compare that to the gambler who tilts after every bad beat. The difference? Ego management.

Motivation: They Play to Win – Not to Escape

most problem gamblers bet for escape from boredom or stress or negative emotions. Not pros.

In fact, one of the strongest findings of the study was this:

“The best players were not inspired by excitement and thrills. but by improvement and becoming the best.”

Poker as a livelihood. Poker as a solvable game. Poker as a portal for self-improvement.
That implies they themselves are not hooked on the experience – they are hooked on the process.

That is a much better habit.

They Don’t Play to “Get Lucky”

Pros consider poker not a game of luck. Okay, in the short run you experience luck – but they bet for the long run.

Continue reading →

You’re half through a hand, staring at the monitor. The ante has been high. The stakes look real. And, before you can even know anything about anything, your head begins to glow like a Christmas tree.

Welcome to the secret world of internet poker – where neuroscience and strategy collide and where the largest game is not necessarily being dealt on the table… but in the mind.

A new study in Addiction Biology in 2024 in Giustiniani et al. leads us into the brain of a poker player – quite literally. Researchers used EEG (electroencephalography) to detect what effect online gaming has upon the brain reward circuitry, cognitive control, and decision-making.

You will not believe what they found.

The Study: What Happens When Poker Meets Neuroscience?

The researchers brought in two groups:

• Experienced online poker players (without gambling disorder)
• Non-poker-playing control participants

Each participant played through simulated poker tasks and gambling decisions while connected to EEG machines. The goal? To measure how their brains responded to:

• Wins and losses
• Risk and uncertainty
• Decision conflict
• Feedback from outcomes

In short, they wanted to see: does online poker change how we think and feel – even when we’re not technically addicted?

Spoiler: yes. And in some surprising ways.

Online Poker Players Had Lower Brain Reactions to Rewards

One of the study’s most unexpected findings was that regular online poker players showed lower late positive potential (LPP) responses to both wins and losses.

What does that mean?

LPP is a brain signal tied to emotional arousal and attention. Higher LPP = more engaged and emotionally responsive. But for these players, big pots and close decisions barely moved the needle.

This suggests a kind of emotional blunting or “flattening” – not necessarily in a clinical sense, but as a learned reaction to constant stimulation.

They’re not tilted. They’re trained.

Risk? What Risk? Players Processed Losses Differently

Poker is a game of risk – but how you process that risk determines your style.

EEG results showed that online poker players had reduced feedback-related negativity (FRN) when losing risky hands. FRN is a marker for how the brain flags mistakes or unexpected outcomes.

Lower FRN = less surprise, less conflict.

In other words, seasoned online players might not view losses the way the average person does. Instead of seeing a failed bluff as a mistake, they see it as part of the plan.

This has deep implications for poker bots, training platforms, and even real-time assistants. Modeling human emotion – or its absence – is now part of AI training.

Ironically, Non-Players Made “Better” Decisions

Now here’s where it gets weird.

In certain gambling tasks, participants who didn’t play poker actually made more rational, utility-maximizing decisions than the poker group. They showed higher engagement in goal-oriented decision-making and responded more strongly to feedback.

The poker group? More muted, more automatic.

Why? The researchers suggest it may be due to habitual processing. Online players make hundreds of micro-decisions per hour. Over time, their brains automate those choices – efficient, but less emotionally engaged.

Continue reading →