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Mobile F2P: How Small Studios Rewrote the Rules

Published by Paolo Gambardella on 22/10/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

I asked Claude to study 2.5 Gamers, the podcast by Jakob and Matej that dissects a mobile game every week. Here’s the prompt I used:
I want you to browse the most-viewed videos from the past five years, pull the transcripts, and go through the content carefully, word by word. Then write me a breakdown of mobile F2P trends over recent years, with a particular focus on games coming from lesser-known developers.

What makes this podcast compelling is that the main characters are often two-, seven-, twenty-person teams — frequently based in Turkey, Vietnam, China, or some small town in Northern Ireland — pulling in numbers that turn heads.

A note on figures: almost all the revenue numbers I mention are estimates made by Jakob and Matej using market intelligence tools. Treat them as ballpark figures.

2023: the rise of hybrid games

After Apple’s privacy changes, user acquisition costs climbed to the point where the hyper-casual model — masses of downloads at a few cents each, monetised purely through ads — stopped making sense. In early 2024, a Voodoo executive said it plainly: hyper-casual is dead as a business, and their entire prototype pipeline is now aimed at hybrid casual.

Hybrid casual means starting with an ultra-simple hyper-casual core and layering a meta on top of it: currencies, progression, events, battle passes, some in-app purchases.

  • SayGames set the template with My Perfect Hotel: over a year in soft launch, then 13 million installs in the first month.
  • Habby did the same with their action RPGs, building a template they reuse from one game to the next.

User acquisition has increasingly spilled over into design, to the point of actually driving it. Whiteout Survival by Century Games, for the first thirty minutes, is indistinguishable from an idle game. Then a “World” button appears and you realise you’ve been playing a 4X all along. They grafted a low-CPI idle game onto a deep strategy title, because idle sells cheaply in ads while the 4X monetises for years. You’re essentially building two games in one.

2024: core mechanics start to matter again

2024 is the year small studios prove that a fresh mechanic is worth more than any amount of market research.

  • Lessmore is a remote team of 17 people. The prototype for We Are Warriors was built by two people in two weeks, with zero market research: they believed in a moment-to-moment gameplay loop that nobody had done yet. Estimated result: around $200k a day from ads alone, with rewarded ads capped at two per day to stop ads from becoming the main strategy.
  • Quokka Games, based in Turkey, has about ten people. Screw Jam is a mash-up of three existing games: the nuts and bolts from Nuts & Bolts, the exit limit from Bus Jam, and the bench mechanic from tile-match games. Within four months it goes from zero to roughly $130k a day in in-app purchases.
  • Hexa Sort, published by Lion Studios, invents a new puzzle mechanic and builds the whole thing around advertising: ten-minute levels, rewarded ads that appear directly on the board when you need space, and an interstitial when you reopen the app.
  • Offline Games — a collection of solitaire, chess, minesweeper, and clones of well-known games, apparently made by a single person. Zero progression, zero currencies, virtually no ads. By July 2024 it’s hitting one and a half million downloads a day. No paid acquisition campaigns found anywhere: just solid ASO, a YouTube channel with trailers racking up tens of millions of views, and cross-promotion across its own apps. One line from the episode stuck with me: “maybe the answer to monetisation is not having one.”
  • The WeChat mini-game market has around 400,000 developers, 70% of teams have between 5 and 30 people, and a game takes two or three months to make. China is running one to two years ahead of the West.

2025: execution and speed

By 2025 the mood shifts. The ideas are out there, but the trend is to copy them fast — and in mobile, it looks like the winners are whoever executes that best.

  • Rollic becomes the master of “deterministic puzzlers” — levels that play out identically every time — and bets on many games doing one or two million a month rather than chasing a single Royal Match.
  • Block Blast by Hungry Studio, a small Chinese team obsessed with A/B testing, takes Tetris, strips out the time pressure, and turns it into a three-piece puzzle with dynamic difficulty tuned to each player. It becomes the second most downloaded game in the world.
  • Grand Games, a Turkish studio, takes water sort — a genre packed with low-effort games coasting on ads — and applies the same level of polish as Royal Match: effects, animations, the game immediately telling you when you’re stuck and offering you an extra bottle.
  • Falcon Games, in Vietnam, copies Royal Match’s events system wholesale and drops it onto a puzzle about sorting objects onto shelves, overtaking the games that had invented the genre.

To me, this is a clear sign of dwindling originality and creative ambition. The sector is increasingly being taken over by a pure-greed mindset.

2026: the sort genre and the 30-day window

By 2026, execution is optimised even further — the winning teams are the ones who move fastest.

  • Pixel Flow comes out of Loom, a Turkish team of 7–8 people who left Rollic. No investor wanted to back them — according to Akin Babayigit, because they hadn’t come from Peak, the “right” studio. They put in all their own money, ran UA themselves, and went from zero to over a million dollars a day in ninety days. Then Scopely acquired them. A one-to-one Vietnamese clone reached $140k a day within two months, before being pulled from the stores.
  • Cube Land by RotateLab, a remote Turkish team, arrives after about twenty released games with nothing to show for it. Idea in November, retention test in March, $300k a day six months later. They call it “volume of shots”: keep taking them.
  • Smash Fest takes its core from a mini-game inside a Toon Blast live event — a cannon shooting balls at physical structures — and turns it into a full game. It scales to 300k downloads a day. Within a month, seven clones drop from established studios. Pixel Flow had three months before being copied; Smash Fest had one. The advice they keep repeating is blunt: you’ve got 30 days to scale.

The creative arms race

If there’s one thread running through all four years, it’s the weight of ad creatives. Since 2023, the volume of videos a game needs to produce has kept growing. Vita Mahjong, a simplified mahjong game aimed at older players, produced 23,000 creatives in a single month. One example cited: 1,800 creatives tested to find one capable of sustaining a million in spend. The takeaway is “whoever executes at volume wins.”

AI has accelerated everything. Some Chinese studios claim to produce 70% of their videos with AI. But the same technology has reached smaller players too: Matej, who’s developing his own game solo, generated ten videos starting from nothing but the store link, in an hour and a half. His conclusion is that ten imperfect videos beat one perfect one.

And there’s a darker side to this, which needs saying. Ads featuring fake neurologists, falsified gameplay, Marvel and Nintendo characters used without permission, families freezing to death to sell a gardening game. The platforms do very little about it. It’s one of the reasons I look at this market with a mixture of admiration and unease.

What I take away from all this

Laid out in order, these three years tell me a few things I want to keep in mind.

The barriers to entry are low; the barriers to excellence are sky-high. Akin says it, and the facts back him up. Anyone can clone Pixel Flow in two weeks. Meowdoku copied an almost note-for-note sudoku that had been sitting on the market for eighteen months going nowhere, and made it explode. Block Out took the same mechanics as Color Block Jam with a completely opposite level design philosophy and surpassed the original. The idea gets you through the door; execution decides who stays.

Specialisation pays off. Loom came from years of deterministic puzzles. Malpa Games spent ten years sharpening their production on word games and sudoku before hunting for new ideas, because “ideas get copied immediately if you’re not precise and fast.” People who know a genre inside out spot where it can be pushed before anyone else does.

Fresh ideas come from outside the genre. A mini-game from a live ops event becomes a full game. A game for older players becomes a monetisation model. Township, after seventeen years, is a match-3 with a tycoon layer in front of it that exists purely to lower the cost of acquisition. “People don’t look outside their own genre,” they say in the Township episode. Those who do find room to move.

Organic reach still exists, but it’s the exception. For most small studios, distribution remains the core problem — and with AI bringing production costs down, it’s only going to become more so.

What reassures me, in all of this, is that the game still matters. Akin says the industry has swung too far towards data, and that today the ad networks’ algorithms raise the quality bar at launch: you can’t ship a half-finished game and test your way to success anymore. You need instinct. You need someone who knows what to keep and what to cut.

And for anyone working in design, that’s good news.

Miyamoto Didn’t Write Code

Published by Paolo Gambardella on 14/10/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

From what I can gather, Miyamoto never wrote a single line of code for his games. In an old Iwata Asks, lead programmer Toshihiko Nakago describes how it worked. Miyamoto and Takashi Tezuka would sketch levels in pencil on enormous sheets of graph paper. Nakago’s team would then enter the data by hand, block by block. In the evenings they’d burn everything onto a ROM, and the two designers would play through the level. Corrections would come back as pencilled notes on the sheet. “We could barely manage one round of changes a day,” Nakago recalls.

One cycle a day. Today, with a language model open alongside the editor, I can run one every two minutes.

I’ve written before that the lone genius is a myth and I still believe it: without Nakago there would have been no Super Mario. Today, a language model handles part of what used to be that dialogue between designer and programmer. But the dialogue itself remains — and the way Miyamoto engaged with it says a lot about what our craft is becoming.

The designer as director

I like to picture Miyamoto as someone who knew what he wanted and knew how to communicate it. His job was to describe: where the block goes, how high the jump reaches, what the Goomba does when it hits a pipe. Translating that into assembly was someone else’s problem.

Something similar happens with AI. I can describe a mechanic, a character’s states, the edge cases, and get back a script for Godot or Unity that implements them. The quality of the output depends on how clearly I’ve thought things through — the more precise I am, the fewer tokens I’ll burn.

The designer decides what should happen, why, and what it should feel like. Writing the code becomes a part of the process you can hand off. I touched on this recently when writing about junior devs: the job has always been to instruct a machine until it becomes a game.

The limits of today

The NES had 2 KB of working RAM. Nakago describes hunting for free memory: with twenty bytes to spare, you could add ten more blocks to a level. Some of the most enduring solutions in gaming came out of those constraints — like the clouds and bushes in Super Mario Bros., which are the same sprite recoloured.

Miyamoto didn’t program, but he knew those limits inside out. That’s why what he asked for was always actually buildable.

AI has its own limits, and anyone using it needs to know them just as well. The model forgets whatever falls outside its context window. It gives different answers to the same question. If it’s running in real time inside a game, every call has a latency and a cost. Knowing these things lets you design around them — and sometimes turn them into mechanics: a character with a faulty memory, an unpredictable oracle, a world that shifts every time you visit it.

Ten cycles a day, then a hundred

In the same interview, Nakago mentions that with later tools they could iterate on a level ten or twenty times a day. Today the loop between idea, implementation, and playtest is compressed almost to nothing. I tweak the gravity, play it, tweak it again.

That shifts the bottleneck. Before, the constraint was how fast someone could implement a change. Now, the constraint is how fast I can tell whether the change actually works. And to figure that out, I have to play, feel, compare. The feel of a jump only reveals itself with a controller in your hands.

What AI doesn’t know

AI can write code, sketch dialogue, generate assets in seconds. But it doesn’t know when a level has gone on too long, when an enemy arrives at exactly the wrong moment, when a jump tips over from challenging into frustrating. It doesn’t experience joy, boredom, or tension. It’s good at imitating solutions others have already found; deciding which ones to keep is down to whoever’s actually playing.

That’s why I think the game designer’s value is shifting even further towards taste, pacing, and an understanding of player psychology. Those are the qualities that made Miyamoto’s work timeless — and he honed them with a pencil and a sheet of squared paper.

Three things to practise starting tomorrow

  1. Write specs that someone else can execute. Take a mechanic you know well — Mario’s jump, say — and describe it in ten lines. Then hand it to a model and see what comes out. Every gap between what you had in your head and what you see on screen is a place where your description was fuzzy.
  2. Learn your tool’s limits. Miyamoto knew the NES’s memory; learn what the model gets wrong — where it gets confused, what it forgets, when it starts making things up. That’s design-relevant information.
  3. Play more than you generate. When an iteration takes two minutes, it’s easy to crank out fifty of them without actually playing any. Set aside time to pick up the controller and make a proper judgement — and write down what you felt before you ask for the next change.

Games Like Toys

Published by Paolo Gambardella on 07/10/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

In the last few days I’ve written about what retains value when copying a game becomes trivial and about prototypes built by mixing existing games together. Today I’m coming back to the same territory, but from the audience’s side — prompted by two pieces of content that landed in my inbox almost simultaneously.

Taking apart and putting back together

The first is a video from the AI Search channel that explains how to use AI to decompile games and remix them with each other. Mirror’s Edge parkour inside Skyrim, Minecraft inside GTA, Spider-Man in a Batman game. The parkour port, the author says, took roughly a day. His summary:

“Think of it as like copying and pasting various elements from different games to create a completely new game.”

A lot of these mashups are rough around the edges — collisions and physics breaking all over the place. Almost none of them will ever become a product. And yet loads of people watch them, recreate them, and share them.

That’s what I find fascinating. For a certain segment of the audience, the game has become a toy. The objectives we design become optional; the fun is in opening the box, pulling out the pieces, and building something nobody planned for.

What they’re really after

The second is a long post by Seth Godin on which AI businesses are actually worth building. Godin writes that at the root of almost every purchase there are three drives: status, belonging, and freedom from fear. On top of that he places an intermediate layer made up of things like control, transformation, and belonging to a narrative.

The people making mashups live exactly in that layer. They want control over the game they love, they want to transform it, and they want to show it to people like them. Every community swapping these experiments around is a network effect that nobody had to pay for.

Then there’s the line that hit me hardest as a game designer:

“Mechanics without a story is the race to the bottom.”

Godin is talking about startups. It applies to us just as well. If a mechanic can be lifted out in a day, on its own it’s worth very little. The player is looking for the story that mechanic lets them tell.

Fallout’s dog in Human: Fall Flat

I tried looking at my own game, Pawtners Case, through this lens.

Take Dogmeat, the dog from Fallout 4 — the one you order to fetch items and bring them back. Drop him into Human: Fall Flat, the physics puzzle game from No Brakes Games where Bob wanders through his own dreams grabbing everything with floppy arms. The result looks a lot like Pawtners Case: a police dog in a dreamlike city, two buttons, objects picked up with the mouth, and physics that makes you laugh.

Today I could watch that mashup running before I’d written a single line of code. It would be a private, unpublishable prototype — and it would be enough to tell whether the feel works.

There’s also a detail I now read differently. In Pawtners Case every case can be solved three ways: noob, pro, and hacker. The hacker route is designed for players who exploit the physics and the levels to reach the end in their own way — in other words, for people who treat the game like a toy. I’d designed it as a bonus feature. Maybe it’s actually speaking to the most interesting audience of all.

So, is it a business?

For the people making mashups, almost never. For whoever gives them a legal sandbox, pieces to combine, and a stage to perform on — very likely yes. Roblox, Mario Maker, and Fortnite Creative have been proving that for years.

For game designers, the question shifts: how easy is it to take my game apart, and what will players build with the pieces?

Digital Eye Strain: The Hidden Cost of Hyper-Focus

Published by Paolo Gambardella on 08/06/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

These days I’ve been getting to grips with the agents from Anthropic and OpenAI, and I can genuinely see improvements in the speed and quality of my work. Where I used to need developers to build prototypes, it’s now much more straightforward to tackle that kind of challenge with Claude or Codex guiding me along.

That said, I’ve recently been noticing a strange heaviness in my eyes by the end of the working day. This weekend was rough, especially Saturday. It felt as though something was stopping my eyes from even moving properly, on top of a general dryness and fatigue.

I found out — credit to Gemini, actually — that this is a pretty common problem. Using AI tends to pull us into states of hyper-focus.

Having to think forces us to take breaks

The first thing worth noticing is that before, I had to take lots of breaks to think things through, look up tutorials, or browse various forums. Everything was slower, obviously, but that also meant natural breaks for my eyes and spending longer on the same block of text.

Now, though, working with an agent means receiving large chunks of text in quick succession. You have to skim through them fast to separate the useful parts from the hallucinated ones, and more often than not you fire off another question straight away — which brings you back to reading another big wall of text.

AI interfaces with dark mode, VS Code with light mode

I’ve moved to using VS Code to manage my agents, since I find it better for keeping an eye on token usage and for other perks like keyboard shortcuts and so on.

The thing is, AI interfaces generally use a dark background, whereas in VS Code I prefer a light one. So I’m constantly switching between the two, which puts extra strain on my eyes.

It seems that parsing text-heavy content like code also makes us blink around 60% less than normal. Medicine calls it Digital Eye Strain, and it’s an increasingly widespread issue.

I hope this post helps others identify the problem. The fix comes down to:

  1. Sync your fonts and sizes so everything is large enough to read comfortably
  2. Reduce contrast where possible, and avoid constantly ping-ponging between different windows
  3. When the AI gives you a long explanation, close your eyes and listen to it instead
  4. Use CLAUDE.md or instructions.md files rather than chatting back and forth with the agent, to cut down on the number of iterations

What if students made the video game instead of studying it?

Published by Paolo Gambardella on 29/05/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

You’re studying how Hitler’s rise to fame unfolded in the Weimar Republic. Economic causes, political crisis, beer hall rallies, cut-price populism. Serious stuff, but heavy going.

Then someone in class says: what if we made a beer hall simulator?

You pour pints, serve customers, make money. Hitler wanders between the tables spouting nonsense. Get him drunk enough and his popularity drops. Let him talk and it rises.

That’s a video game. You just designed it. And an AI builds it while you’re talking.

This is the after-school activity I have in mind.

AI applied to video games applied to the humanities. History, philosophy, literature, economics. Games have always been rule systems built on real-world activities and events.

The format is simple. The class sits down. The teacher is the only interface with the AI agent. The students focus on coming up with ideas and coordinating to give it instructions. The teacher types, guides, asks questions. The agent builds. At the end, everyone plays together.

No one needs to know how to code. No one needs to know how to use Godot. What you need is the ability to reason about a problem and work together.

History translated into mechanics.

When a student decides that “drunk equals less popularity”, they’re reasoning about how political consent actually worked in Weimar Germany. They’re building a causal model. They’re doing what historians do, with a tool that feels like their own.

The game forces you to simplify — deliberately. You have to choose what matters, what to measure, what to leave out. That choice is the critical thinking you’re trying to teach. And then you play. And you have a laugh. And that laugh is memory.

New frontiers

Today, anyone with a clear idea and an AI agent can have a working prototype in hours. Not a masterpiece — a prototype. Something that runs, that you can touch, that responds.

Access to creation is no longer filtered by technical ability. It’s filtered by clarity of thought.

Schools already have after-school activities like robotics, drama, and computing. All perfectly valid. But there’s a new territory I think is worth exploring: using AI to turn humanities subjects into interactive experiences that students design together.

Someone will do it, sooner or later. It could start in any classroom.

The present is cross-functional, the future is high-quality

Published by Paolo Gambardella on 25/05/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

The stores are drowning in games. We can all see it, and it’s a direct consequence of something obvious: making a simple game has become quickly accessible. Godot, Unity, asset stores, AI for code, YouTube tutorials for absolutely anything. Development time has compressed brutally. So everyone’s doing it.

The result is a flood. Clones, games generated through semi-automated pipelines, products assembled rather than designed. Each of these games shares the same trait: made quickly, with a small team or solo, with little investment per unit.

When everyone does the easy thing, the hard thing gains value. Even Roblox is proving this, with its new strategy of focusing on HD games. If simple games become a commodity, then games that require something you can’t auto-generate become rare.

The thing that’s been living rent-free in my head

Teams in the near future have one new defining characteristic: they can be genuinely cross-functional. Not in the buzzword sense we’ve been hearing from companies for years. In a concrete, practical, radical way.

Some examples.

  • A designer no longer has to wait for a programmer to build a prototype. With today’s tools, they can build something functional in hours. They can test their game loop before the meeting with the tech team is even in the calendar.

  • An artist can implement directly in-engine without waiting for tech artist support. An artist who understands a bit of how the engine works is no longer an exotic exception — they’re just a normal asset to the team.

  • A writer can test their narrative in a working build without filing a ticket and waiting for the gameplay team to “find a slot in the next sprint”.

The roles don’t disappear. The excuse does.

To be clear: I’m not saying specialised roles will become useless. An experienced tech artist does things a shader-graph artist will never pull off. A senior programmer solves problems that a designer with an AI coding assistant can’t even properly articulate.

That’s not the point. The handoff barrier between roles gets lower. And with that barrier goes the industry’s most comfortable excuse: that’s not my responsibility.

If in the past you could sit in a cosy niche and wait for someone else to do their bit, today that’s changing. A team that sits around waiting for the “programmer bottleneck” when the designer could prototype, or the “tech artist bottleneck” when the artist could implement, is a team wasting time artificially.

Agility isn’t an abstract value. It’s the concrete ability to take an idea from your head to a working screen in as little time as possible, with the resources available. Today, those available resources have changed.

The thing that’s hard to copy

There’s an underlying question running through all of this: what can’t be automated or cloned?

The answer I’ve landed on is: a team that genuinely works well together, around a vision that’s actually worth building, with enough overlapping skills that no single bottleneck can hold everything up.

That combination can’t be copied. You can’t prompt your way to it.

And it’s exactly that combination you need to make the games that, in the coming years, will have the best shot. What does that mean in practice? That the smartest companies will be the ones capable of retaining talent — not working people into the ground and then laying everyone off the moment the title ships.

A Brief Survival Guide for Junior Designers

Published by Paolo Gambardella on 22/05/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

Last night I was at BCN Gamedev Society, a monthly meetup for game developers in Barcelona. I spoke to a lot of people and caught up with old friends. I also got to chat with several juniors who want to break into the industry — concept artists and game designers. All of them dealing with the same problem: how do you get experience when nobody will hire you because you don’t have experience?

It’s not a new question. But today the answer looks very different from what it did ten years ago.

The code is being rewritten

I read a LinkedIn post that got me thinking. The core idea was this: money has always been the code society uses to translate human time into something its systems can read.

  • You have a skill → it becomes a salary.
  • You have a need → it becomes a price.
  • You have a future → it becomes credit.

The trouble is that money is a low-resolution system. It can measure output, not meaning. It can reward production, not direction.

And now AI is dismantling exactly that promise: if generating asset variants, doing QA on repetitive scenarios, or brokering information between teams are all things machines do better — what’s left for the junior who was learning by doing precisely those things?

The actual problem

Let’s take a concrete example. A junior concept artist wants to break into the industry. They’ve studied, they have a decent portfolio. But a studio will often choose a senior who, with ComfyUI and well-crafted prompts, produces in under a day what the junior would take a week to deliver.

You build experience by doing things — but if the entry-level work gets automated, where do you actually learn? How do you learn by osmosis in this new reality?

Survival tips

Here are some concrete ideas.

1. Stop competing where AI wins.

AI wins on production speed, consistency, and infinite variation. It doesn’t win on taste, art direction, or the intuition for what will land with that player, in that cultural context, with that emotional tone.

If your portfolio says “I can write a GDD quickly”, you’re on the wrong turf. If it says “I know how to choose, curate, direct, and explain why a decision works”, you’re in a much better position, in my view.

2. Ship small things, made with other people.

A junior game designer with five small games on itch.io, all made as part of a team, is worth more than someone with a portfolio full of unrealised concepts. What you’ve shipped shows you’ve been through the full cycle: idea → prototype → feedback → release.

That’s real experience, in a team context. And it doesn’t require anyone to hire you first.

  • Game jams to work directly with other people
  • Personal projects you then put in front of players and take notes on how they actually behave
  • Weekend experiments you publish on a public Discord to collect feedback

The goal is to build a track record of decisions made in response to real interactions — and real player behaviour you’ve observed yourself.

3. Look for a senior to work alongside, not a junior position.

The “senior + AI” dynamic that’s becoming the norm paradoxically creates room for informal collaborations. A senior with too much on their plate and too few reliable juniors is often open to accepting help in exchange for a small financial arrangement and some mentorship. It’s not ideal, but it’s how things work. And when you’re around someone with experience and judgement, you learn by osmosis. That’s always been true.

BCN Gamedev Society, online communities, indie studio Discords — those are the places where those conversations happen.

4. Use AI to develop your taste.

The trap for juniors is using AI to produce more stuff, and ending up just producing more mediocrity, faster.

  • Generate 50 variants with AI.
  • Then pick one.
  • Know why you picked it.
  • Then iterate.

Your ability to judge — what to keep, what to cut, and why — is the one thing that won’t be automated any time soon.

5. Short-term survival is a separate problem.

I’m not going to tell you universal basic income is just around the corner and everything will be fine. Maybe it arrives, maybe it doesn’t. In the meantime, there are bills to pay.

Some concrete options for keeping the lights on while you build your track record:
– Gamification freelance work for clients outside the industry (startups, corporate training, educational)
– Content creation: if you can explain game design in an interesting way, there’s an audience for it
– Tutoring, online courses, local workshops
– UX writing, product design, narrative design for non-game clients

None of these are the career you want, but they’re bridges.

Conclusion

The experience paradox isn’t new, but it’s getting sharper. And the answer is to build real experience in unconventional ways:

  1. make small things and use them as a way to connect with others
  2. stay close to people who know more than you
  3. develop taste, and learn to communicate it.

Money measures what the system can already read. What systems can’t read yet is taste. Intuition. A sense of what’s actually worth making.

For now, that’s still our territory.

A Good Era for Game Design

Published by Paolo Gambardella on 19/05/2026

This post was originally written in Italian by Paolo Gambardella. It has been translated by an AI agent and may contain inaccuracies.

During the free-to-play mobile boom, this corner of the games industry was quickly taken over by product management. In a way, that makes sense — the free-to-play business revolves around player acquisition, which means performance marketing. If the cost to acquire a player is lower than what that player spends on average in the game, you’ve got a working business.

So, working as a game designer in those companies very often became a conversation purely grounded in data. I myself repeatedly found myself arguing with my bosses over things we could summarise as: my taste VS the current state of the market. A practical example: if I — as a designer — felt that a certain fantasy needed to be expressed through specific mechanics because I had a hunch it would work, the default response was: “okay, and where have you seen this? In which other games?” Very often it was something that came from a personal experience, or something else entirely. Maybe an app, a film, or an exhibition I’d visited had sparked an idea. Nothing doing — I had to find a way, absolutely, to back it up with data.

And in fact, the designers who built the most successful careers in that sector very often describe themselves as data-driven. They know how to research what’s already there in successful games and reapply it like a mathematical formula to whatever they’re working on. Fair enough — it’s a real skill. But to me, a designer must have both a sense of the business context and a genuine taste. If we’re going to put this much energy into something, we might as well create something new — put something of ourselves into it. People notice when you actually have something to say.

I think this is our moment, right now. This is the moment for designers who want to express their own taste. In my opinion, data-driven design is dead. It was always repetitive, not particularly creative work, but I believe that in the age of AI — where an agent does all of that in far less time and at a fraction of the cost — we’re better off cultivating our own taste instead. Better to watch films, study different kinds of apps, go to exhibitions. Better, in short, to genuinely search for our own voice.

What I believe in — and have always believed in — is data-informed design. Any self-respecting designer should always try to empathise with their players, and there’s no better way to do that than collecting data. But that data shouldn’t dictate our taste, or else we’ll always be offering horses to people who need something new — like selling carriages in Henry Ford’s time. A designer can’t fly blind.

But a game designer needs to have the freedom to create, to bring something genuinely new into the world. Hiring people to repeat formulas is absurd, and I think that practice is destined to disappear — because now we have the right algorithm. One that lets us focus on what truly matters.

AI Slop vs. Creative Power: A Love-Hate Relationship

Published by Paolo on 15/05/2026

I have mixed feelings about the integration of artificial intelligence into our digital spaces. On one hand, public platforms are deteriorating. From YouTube recommendations to mobile game ads, our feeds are flooded with low-quality “AI slop”—clickbait thumbnails and content engineered purely around anger and hyperbole. The internet has become a frantic, exhausting battle for our attention.

Yet, as a creator, generative AI has completely revolutionized my workflow by automating the tedious parts of development:

  • Game Design Documents (GDD): I can instantly generate a robust structural base for 15 GDDs a month without redoing the groundwork every time.
  • Workflow Automation: Converting a massive document into actionable JIRA tasks used to take days; now, it takes an hour of high-level planning and bulk creation.

Beyond productivity, experimenting with these tools has brought back a sense of community. Sharing new AI discoveries in private chats feels exactly like the early days of the internet—reminiscent of finding an old IRC channel or a niche forum.

While the public internet grows increasingly dull due to algorithmic noise, the backend world of AI innovation has unlocked a frontier that is just as exciting as the internet itself once was.

Leveling up in Claude

Published by Paolo on 28/04/2026

Yesterday I was speaking with a design director about how I use Claude AI, and he made me discover a new method of using it, I was totally ignoring. In fact, all this AI hype makes me resistant to novelties, and I have to admit that I often lose opportunities because of that.

I think that it’s a very powerful tool, and my feelings are that I will save up lots of time from repetitive tasks such as creating subtasks in JIRA, setting up pages, summarize information that is already there, and so on. It’s definitely a level up in my career.

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