Game economies are more complex than ever, and traditional tools are struggling to keep up. In this podcast, Mihai Gheza, CEO of Machinations, joins the folks at Deconstructor of Fun to explain why designing balanced systems requires more than just guesswork.

“You’re not just balancing numbers; you’re balancing the player’s entire experience,” Mihai says, as he explores how Machinations enables designers to simulate and predict outcomes with precision.

He also shares how AI is redefining the way designers approach challenges like progression, randomness, and player engagement

Read the highlights from our conversation or listen to the full podcast below

Phillip: Hey, we’re here at Slush. It’s Phil from DeconstructorFun, and I’m joined today by the CEO of Machinations, Mihai Gheza. Hi Mihai.

Mihai: Hi Phillip, thank you so much for the invitation. It’s an honor to be here.

Phillip: And Slush has been true to its name—it’s been snowing and slushy outside.

Mihai: It has, and it’s freezing. Exactly what you’d expect!

Phillip: So, how many VC vests have you seen at Slush? Is that a real thing?

Mihai: Oh, very real. But now it’s not just the VCs, founders are wearing Patagonia vests too. I guess to blend in.

Phillip: [laughs] Tell me about Machinations. What does it solve?

Mihai: Complexity. Chaos. Machinations solves the lack of understanding we have when dealing with complex systems—especially when we think we can manage them with spreadsheets. These systems are far more chaotic than we often realize. Machinations does three fundamental things: First, it helps you visualize systems. Second, it lets you simulate how they unfold in real-time. And third, it helps balance them—that’s the cherry on top.

Phillip: Interesting. So, let’s say I’m designing the economy for Monopoly Go, a popular mobile game. How would Machinations help me become a better designer? What could it do that Excel can’t?

Mihai: Great question. There are two parts to this. One, how does Machinations compare to spreadsheets? And two, how does it help specifically with Monopoly Go?

When comparing to spreadsheets, let’s be honest: most designers only use basic spreadsheet functions, without much programming. Advanced features like VBA or Python scripts are incredibly rare—probably less than 0.1%. So, most designers are stuck with static, idealized views of their game economies in Excel. Machinations, on the other hand, brings that to life. With a click, you can run simulations that take into account randomness and other factors, without needing to write a single line of code.

Now, for Monopoly Go, Machinations can help you understand how randomness affects progression. You’ve got player decisions, boosts, attacks, friends, shields—the game has tons of emergent possibilities. Machinations can help you explore all those interactions and find the best balance for engagement, retention, and monetization.

Phillip: Wow, so it seems like Machinations really gives economy designers a clearer picture of what’s happening under the hood. Do you find that most designers build all their game logic in Machinations?

Mihai: Not necessarily all of it, but a lot of designers use Machinations to model key decisions and player behavior. You’re simulating choices and outcomes, but the game engine itself—like the jumping and shooting mechanics in action games—usually isn’t modeled. Machinations is more focused on game economy, progression, and systems, not the physical gameplay.

Phillip: Got it. You mentioned earlier that you were a game designer yourself. Did you ever run into a situation where you thought, “I wish I had a tool like this?”

Mihai: Oh, absolutely! I remember one specific project where I was responsible for a drop rate system. We had a 50% chance of an item dropping, but I just had this gut feeling that the odds were too harsh and players would rage-quit. Four other team members, including senior designers, were convinced it was fine. We shipped it, and my worst fears came true—players churned, and we wasted a huge amount of the user acquisition budget. I knew something was wrong, but I didn’t have the tools to prove it at the time. That frustration is what led me to discover Machinations. I wanted a tool that could scientifically back up those gut feelings.

Phillip: I can see how that would be frustrating. So, what about Monte Carlo simulations? Can you walk me through how designers use that with Machinations?

Mihai: Sure! Let’s stick with your endless runner example, like Subway Surfers. Monte Carlo simulations help by simulating many different player journeys. Instead of a simple “pass or fail” percentage, you can simulate how different players might perform under a variety of conditions. So, for example, if you have a 50% chance of failure at a certain point, instead of just saying “half the players fail,” Monte Carlo shows you a more nuanced picture. Some players might fail earlier, others later. You see the full spectrum of outcomes.

Phillip: That makes a lot of sense. So, once a designer has built their simulation in Machinations and the game launches, is there a way to feed live data back into Machinations to refine those simulations?

Mihai: Yes, exactly! We call this LiveOps data ingestion. You can sync data from a spreadsheet, or even pull in JSON telemetry data from your game’s analytics system, like Unity Analytics. Once you have real-time data coming in, the simulations turn into predictions. You start with assumptions pre-launch, but post-launch, as you feed in live player data, the assumptions evolve into actual predictions. Over time, you calibrate your system, which makes your simulation more accurate. Eventually, it becomes a sort of crystal ball for predicting player behavior.

Phillip: I saw on the Machinations website that you’ve made a lot of simulations public. What’s been the most successful one so far?

Mihai: The one that went viral was a Web3 game economy model for a game called Kaiju Kings. The developers put out a bounty to translate their tokenomics from the white paper into something tangible, and one of their community members built a Machinations diagram for it. That diagram blew up, reaching over 10,000 views in about a month. It was really cool because the community could interact with it and understand the game’s economy before any contracts were even written. We’ve also seen a lot of interest in diagrams for idle games and RPGs.

Phillip: Web3 and tokenomics—are those areas where Machinations has seen more adoption than traditional game development?

Mihai: Definitely. Web3 has embraced Machinations more quickly than Web2. We’ve seen a lot of traction in that space, especially since there are around 2,000 Web3 games in development. Developers are very eager to get their tokenomics right before printing smart contracts, and Machinations offers them the mathematical validation they need. The combination of public data, transparency, and blockchain makes it a great fit for what we do.

Phillip: That’s interesting because with Web3 economies, you have tradability, floating prices—things that MMOs have dealt with for years. Are game designers ready to handle that level of complexity?

Mihai: Not really. A lot of Web3 developers don’t have experience building traditional games, let alone dealing with complex economies. According to the Blockchain Gaming Alliance, 70% of teams building Web3 titles have never shipped a game before. That’s a huge red flag. Managing a sustainable economy is already challenging in traditional games, and Web3 adds another layer of complexity with tradable assets. Many developers just aren’t ready for it.

Phillip: That’s a scary statistic. What about EVE Online? They’ve managed an open economy for years. Are they diving into Web3?

Mihai: Yes, CCP, the developers of EVE Online, are working on a blockchain game called Project Awakening. It’s still early, but I have high hopes for them because they’ve been running one of the most stable virtual economies for 20 years. They’ve seen everything—player-driven marketplaces, inflation, deflation, crashes—and they have the tribal knowledge to pull it off. If anyone can prove that a Web3 economy can work, it’s CCP.

Phillip: That makes sense. EVE Online’s economy is driven by destructibility, though, which contrasts with the permanence of NFTs. How do you see that working?

Mihai: Funny you mention that! I think one of the biggest lessons from EVE is the importance of destruction in an economy. In Web3, one of the key reasons so many projects failed was inflation—too many assets accumulated without enough utility. In EVE, they’re unapologetic about destroying assets during massive battles, and that’s what keeps their economy healthy. I believe that Web3 games will need to embrace the idea of burning assets to avoid the same inflation issues.

Phillip: So what’s the point of Web3 for a game like EVE, then? Isn’t their current system working just fine?

Mihai: It’s a valid question, and one that CCP’s Hilmar has been thinking about too. From what I gather, it’s more of a natural evolution for them. They’ve always been pioneers in virtual economies, and now they want to see if they can push the boundaries even further with Web3. It’s less about fixing something that’s broken and more about taking on a new challenge—can we create a true open-world economy?

Phillip: Interesting. So, you’ve been building Machinations for five years now. How has the product evolved in that time?

Mihai: Oh, it’s come a long way. Machinations itself has been around for over ten years—it started as a flash prototype for a PhD thesis called “Engineering Emergence” and was later included in a book called Advanced Game Mechanics, which is kind of the Bible of game design. Five years ago, we started turning it into a full-fledged platform. We moved it from Flash to JavaScript and redesigned the UI. One of the biggest changes we made was adding a robust statistical analysis feature, which shows time series data, histograms, and indicators. That’s been a game-changer for designers.

Phillip: What are customers asking for now? What’s next for Machinations?

Mihai: Right now, the most exciting thing we’re working on is the Balancer. It’s an AI tool that helps automate the balancing process. Instead of manually tweaking parameters and running predictions over and over, you can tell the system, “I want my players to die only three times in the first 10 minutes,” and it will adjust the parameters accordingly. It’s like having a supercharged assistant doing all the heavy lifting for you.

Phillip: That’s incredible. But when you’re talking about balance, what’s the end goal? Is it to make the game fair, or is it to maximize something like lifetime value?

Mihai: It depends on what you’re optimizing for. For free-to-play games, it’s often about maximizing monetization—so, lifetime value. But for other games, it could be about optimizing player engagement or making sure certain characters are used evenly. The AI Balancer can adapt to whatever goal the designer sets, whether that’s financial or more aesthetic.

Phillip: And who’s using Machinations now? Is it mostly game designers, or are other roles getting involved?

Mihai: It started with game designers, but now it’s much broader. Developers use it for system logic, analysts use it to understand KPIs, and producers like it because they can see all the key metrics at a glance. We’re even building a feature that will allow producers to adjust key parameters on a simplified control panel, which should make life a bit easier for them.

Phillip: Do you have any concerns about Unity or other game engines building their own simulation tools that could compete with Machinations?

Mihai: Not really. Unity could definitely build something similar, but it wouldn’t be a feature—it would have to be an entire product. Machinations is more than just a simulation tool; it’s a whole language for understanding game systems. It’s been spreading organically within academia for a decade, and it’s being taught in over 130 universities. That kind of organic growth is hard to replicate, even for a big player like Unity.

Phillip: You’ve mentioned that game economies are getting more complex. Is that because the games themselves are more complicated, or are we just asking more detailed questions?

Mihai: I think it’s both. The games are definitely more complex, but I also think players are becoming more sophisticated. Gen Z grew up with free-to-play games, microtransactions, and complex progression systems, so designers have had to keep up with their expectations. It’s no longer enough to just create a fun game—you need layers of engagement, progression, and retention to keep people playing long-term.

Phillip: That makes sense. So is there a big skill gap for designers when it comes to statistical analysis and programming? Is that where Machinations comes in?

Mihai: Yes, exactly. A lot of designers don’t have the technical background to use advanced tools like SQL or Python, and that’s okay. Machinations fills that gap by giving them the ability to simulate complex systems without needing to code. It only takes about four hours to learn, and it puts the power of statistical analysis in the hands of designers.

Phillip: Do you think it’s easier to teach designers data science, or teach data scientists game design?

Mihai: That’s a tough one, but I think it’s easier to teach designers data science. They already understand game systems at a high level, and once they have the right tools, they can start making more data-driven decisions. Learning SQL or using analytics dashboards isn’t that hard once they see the value.

Phillip: Interesting perspective. Do you think data scientists should be more involved in game economy design?

Mihai: Absolutely. They should be at the table for every major design decision. Having someone who can analyze past data and run experiments is crucial for making informed decisions. We always recommend having a data scientist, a designer, and a developer working together.

Phillip: Where can people find out more about Machinations or sign up?

Mihai: Just head over to Machinations.io. It’s free to get started, and you can log in with Google in seconds. Feel free to reach out to me on LinkedIn as well!

Phillip: Awesome. My guest today has been Mihai Gheza. Thanks so much for your time!

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