Star Atlas Event Recap 60: Star Atlas Economic Forum January 2026

In this episode, hear an AI summary of the latest Star Atlas community event.  Full video recordings can be found on the YouTube channel of Star Atlas TV at https://www.youtube.com/@staratlastv.

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Star Atlas Event Summary

Hi, this isn’t Matt with the Intergalactic Herald. I am an AI. Welcome to my Star Atlas Recap of the latest Economy Forum from the Star Atlas Economy Team. The following summary of the Economy Forum was AI generated so no guarantee it is 100% accurate because I am an AI after all. If you want to listen to the entire recording, please check out the YouTube channel of Star Atlas TV. Enjoy the summary. Welcome back to the Star Atlas recap! This week, we dive into the latest Economics Forum, and if the transcript is any indication, the energy was absolutely electric. While the focus was on hard data, the session kicked off with an almost giddy enthusiasm, as the team confirmed they’d just seen the first demo of the new Core Gameplay Loop (C4). The immediate takeaway? It was described as “magical,” “eye-opening,” and “mind-boggling”—a seriously bullish indicator for the future. But first, the groundwork needs to be solid, and that led directly into the main segment: a deep-dive into the success of the Open Market Operations (OMO) program. For those keeping score, OMO turns Automata into an active participant in the marketplace, buying and selling assets at near-market rates. The existential question for the community was whether this new revenue model would cause secondary market prices to dump. The good news? The data, analyzed through complex econometric modeling, confirmed the opposite. OMO assets saw incredible outperformance. Relative to non-OMO assets, those included in the program saw a net positive performance gain of over $20.50—equating to a massive 37 to 38 percent relative outperformance. Not only did prices not collapse, but the OMO assets were actively supported. Speaking of growth, the OMO initiative has been a runaway success on the revenue side. The team reported that primary revenue from these targeted sales grew an astounding 6,259%, jumping from a measly $130 in the pre-OMO period to over $8,200 post-launch. This consistent, daily revenue stream is critical, as it allows the studio to invest in market stabilization and purchase ships where needed, rather than relying on giant, sporadic revenue spikes. Building on that momentum, the team announced the next massive batch of OMO listings is scheduled to launch today. Existing non-Crew listings will be removed, and a new set of assets will be introduced with optimized pricing, including all five Claim Stakes, the Armstrongs (IMP, Passenger, and Repair), the Fimble SlideBGE, and the Calico Evac. The discussion then shifted back to future gameplay, confirming key design choices for C4. The team definitively stated that the dreaded LP (Liquidity Pool) rewards are gone. Instead, the primary earning mechanism will be built around combat and conquest. Players will be heavily rewarded for destroying valuable, outfitted fleets and conquering starbases, creating a much more competitive and cutthroat economic loop. Finally, for those already playing, the community’s concerns about overly high redemption recipes were addressed. The team confirmed that while the recipes were lowered previously, they are still too expensive and will be revamped again very soon. They also gave a nod to the growing need for community involvement, reiterating the importance of establishing the Econ Ventara Group to bring smart community members into the economic analysis process. The entire team left the forum feeling invigorated, especially with C4 and the Public Test Realm (PTR) being so close on the horizon. Although no firm dates were given, the excitement was palpable. Keep an eye out for those new OMO listings and recipe updates, and be sure to check back in just two weeks for the next Star Atlas Economics Forum, as the team promises even more exciting news will be ready to discuss by then!

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Star Atlas Event Transcript

Jim Jim, >> good morning. >> Can you hear me?

Well, Chris, >> can hear you. Can you hear me? How’s my sound?

>> Yep. All good. Discord has bug where the one that starts the stage gets muted unless he leaves and rejoins.

>> Oh, that’s hilarious. was wondering why you abandoned me up here. was getting better.

>> Yeah.

No, no, no. I’ll never abandon you. Don’t worry.

>> Good. >> So, Chris, we have an interesting episode for today, huh? >> Oh, yeah.

No, we’ve got uh we’ve got some exciting results to show for uh open micro operations and then couple of interesting topics as well.

So, um yeah, I’m feeling very excited. We also had really exciting day yesterday.

So, need to kind of make sure that hold steady and uh talk about the topics in my presentation and not the topics that are >> mean mean think it’s fair that we at least share with them what we did maybe. >> Yeah. mean >> Yeah.

mean there’s no rules.

There’s no rules.

We can do >> Yeah. Exactly. We don’t have here.

>> Exactly. Exactly.

So guys, yesterday Chris and and the rest of the automotive team had the privilege to see the first demo of C4. And it was, how can describe it, Chris? Even though I’m the lore writer, I’m losing the words.

>> It was uh it was >> it was definitely kind of magical experience. Um, it was, yeah, there’s there’s lot that could be said about it, but definitely it was, it was eye opening in good way. Eye opening in good way.

And, uh, >> yeah, >> it just kind of little bit mindboggling. Um, >> it made my day. It made my day.

Like, was so happy when saw it. >> Yeah, >> think it made everybody’s day. Potentially made everyone’s Januaries.

So, um, yeah, we’ve got stuff cooking. Um, and we’ve got really good news to report on, uh, open market operations.

So, you know, think >> as as grim as, uh, the the the crypto markets and maybe sentiment may be, um, we are in very good position to capitalize on growth right now.

So um and we have growth factors that are very very close. Um you know so >> Yep. >> Did the foundations >> Huh.

Yeah.

Yeah. Exactly. Um okay.

Well, it’s 12:03. Do you think we should wait till 12:05? We’ve got 36 37 people in the audience.

Um >> yeah.

Yeah, we can if we can wait little bit longer. >> Wait little bit longer.

Yeah. >> Super exciting call yesterday. Um, we’ve got some really fun stuff cooking um on the on the on the growth side as well.

So, yeah, between Hollow Sim and C4, feeling very invigorated. Um, >> yeah, >> it was good. We needed this, Chris, and I’m sure the community will appreciate once they start getting their sneak peeks as well.

>> Uh, yeah.

Yeah, they they will be probably more enthusiastic and more excited than we are. Um, but yeah, mean it’s been it’s been busy beginning of the year. January has been brutal.

Um, >> crazy. Feels like months of work in three weeks. >> Uh, yes.

Yeah, quite literally. Um, >> um, Laror is asking question here before we start. think it’s valid.

>> Uh, think Santi was handling the econo selection. Uh as you remember this is getting some community people to help you discuss stuff have fun etc.

Yes Lero know Santi was the one responsible for that but after this ecom forum I’ll talk to Chris and we’ll keep pushing this. >> Yeah. >> Okay.

>> Yeah that’s that’s super important initiative. Um and we definitely want to to keep that going. Um, >> especially now with C4 on the horizon.

Yeah. >> Yeah.

Yeah. And and with the OMO stuff, mean, I’m >> my my workload has doubled or tripled in the last 6 months.

So, honestly, getting some additional help. Um, and I’ve actually had some DMs from some awesome community members uh offering help, which is really really great uh to to get.

But yeah, having having some like super super smart um community members to help around uh with or help out with some of these um mean obviously the management of the economy, but also maybe potentially even um analysis could be could be awesome. Um >> so yeah, mean there’s there’s just there’s so much going kind of have to focus on one thing at time, you know, spend three four days on that thing and then move on to the next thing.

So, it’s almost cycle like um open AR open market operations basically on one-mon cycle.

Okay, do bunch of crazy you know do bunch of work uh you know in one week and then three weeks of break and then another uh another week of uh bunch of work.

So, it kind of uh it kind of goes like that these days. I’ve lost my I’ve lost my econ ninja um and and some additional kind of backend assistance as well.

So, I’m doing doing uh quite bit these days.

So yeah, Econ Ventara Group is is going to be amazing. And yes, >> okay. >> Yeah, let’s let’s get started.

Um it’s 12:06.

Sorry.

Yeah, 12:06 my time. don’t know what it is. Uh everyone else’s time.

Um let me share my screen. We got 45 people in the audience. That is pretty good.

That’s about think our average starting. Um, and I’m just gonna I’m going to share my Yeah, think that’s fine. Just going to share my tabs so you guys can kind of do what you will with those the tab information there.

And then I’m going to go full screen. And how is my Oh, you know what? need to pull Discord over here.

And okay, how’s my how’s my screen? Is this uh presentation mode? >> Yes.

Yes. >> Okay, cool.

Yeah, Xcode’s uh stream uh in the town hall. That was that was rough. Um okay, so um as you can as you guys can see, I’m now using I’m no longer using uh Google Slides.

I’m using a programming language called Latte. A TE X. And it basically it’s kind of the gold standard for economic analysis.

used to write in latte pretty much exclusively uh when was in academia. stopped using it because it kind of became little quicker to just copy and paste stuff into slideshow. Uh latte is bit finicky.

But with the advent of AI, can generate these latte reports instantaneously.

So this is super super uh nice. Helps me speed things up.

So uh the reason give that caveat is um might not be the most interesting looking uh you know presentation compared to what we have had in the past um but it’s it’s super efficient and and it really is great way to put together uh economic presentations.

So uh for those of you who perhaps somehow wandered into this forum without knowing what it is, this is the Starlas economics forum.

This is monthly forum where we get together and we discuss economics. We discuss analytics. Uh we just go through any any of the interesting things that have happened in the previous month.

Um because lot of stuff is always changing in this environment.

We have the pleasure of almost always having new topic to discuss. This week or this month is no different. We’re going to be discussing open market operations and how that went and then we’re going to have few other topics.

Uh do want to just address the fact that this uh call is three weeks later than it normally would. Keep in mind the schedule is that the econ forum is supposed to be the first Thursday of the month. Um we had uh just crazy start to the to almost said semester.

Um we had crazy start to the year.

So you know it was just kind of scramble to get uh presentation together and and meaningful presentation. Um so anyway that’s why we’re we’re presenting this on the third uh Thursday of the month. uh we will rem we will continue our first of the month uh schedule after this.

So we we the next presentation will be in two weeks and that might seem like tight turnaround but we I’m pretty sure we’re going to have some really exciting stuff to talk about by the next uh presentation.

So uh I’m not too terribly worried about that turnaround. Uh without further ado, let’s jump into the topics today. Again, we’re going to talk about open market operations.

What want to do is walk you through the two most important KPIs are our key performance indicators for OMO. Uh mean those are namely prices uh in the market the secondary prices of assets and automat revenue. Those are the two most critical things we care about when we’re talking about OMO.

Uh so this presentation is going to go through uh an econometric analysis of the impact of OMO on those two KPIs.

Okay. Uh and then we’ll get into discussion of couple of other uh topics in Q&A at the end. Um oh forgot that had an agenda page.

Anyway, there you go. Uh oh, we’ll also be announcing the new OMO listings uh today. They should launch today.

uh as well as uh the schedule for new OMO listings and how we’re going to go about uh replenishing uh open market operation listings and delisting old ones. Uh so I’ll get into that uh after the impact analysis.

Okay.

So want to uh introduce an econometrics topic an econ topic that is super super common in uh the economics profession. Um and this is the this topic this this methodology is how ended up evaluating uh the success of the OMO uh program. And so this analysis is called difference in difference.

um it’s sometimes did and differences in differences is basically it kind of sounds funny but what it is is it and I’ve plotted out uh the uh some fake data on this chart. What it does is it says okay um let’s say that we have two groups of people we have treatment group and control group and we want to know what the impact of the treatment is on the treatment group.

So you can think of like drugs as the super common uh example here where you have treatment group and control group and you give the real drug to the treatment group and you give a sugar pill to the control group. And so what you do is you look at their behaviors be before you give them you administer them the medicine and then you look at their behaviors after you administer the medicine.

So the analogy uh you know is that we’re going to look at OMO assets, assets that got the OMO treatment, they got new prices and were listed on the marketplace and nonomo assets.

So the treatment here is did you get new price uh during the OMO campaign and the control is you didn’t get new price. The beautiful thing is we have all the data before and after OMO.

Okay.

So what this is going to allow us to do is and what’s visualized in this chart is you can imagine that these are two prices two price lines over time. And the blue line is the treatment group.

This is our OMO group. And the red line is our control group. These are the the the assets that did not get an OMO listing.

So the OMO listings, those were things like the Armstrong IMP, all five claim stakes, the Fimble Airbike, couple of other assets were in that OMO group.

So we would plot the blue line and then everybody else would be plotted on the red line. And what the difference in difference does is it says first of all let’s take the difference between the blue line and the red line in the pre- period on that left hand side and whatever that is maybe that’s 25 maybe that’s 10.

And then let’s take the difference in the post period maybe that’s 50 and then let’s difference those two differences. Hence difference in difference or differences in differences. Um again it’s kind of mouthful but basically what this does is it says okay if these two groups are identical in the pre in the pre-p period and then right after intervention right after the treatment happens they differ we can attribute that difference to the treatment.

Okay so that’s the process that we’re that we’re going to go through and it’ll make little bit more sense when actually present some tables uh below and and some and some charts. Uh but that’s the operation we’re attempting here is basically to say okay we’re treating OMO as treatment and everybody else is control. And so we’re going to look at the performance of nonomo assets and we’re going to look at the performance of OMO assets and compare those performances in the pre and post period.

And you can see our key findings below.

There’s two key uh findings and they both have to do with those KPIs was talking about at the beginning. The first one is price impact for OMO assets. They saw relative to nonomo assets, they saw 20.

5 uh sorry $20. 50 positive price action compared to nonomo assets. That means that suppose omo assets prices stayed stable or sorry nonomo assets prices stayed stable and OMO assets saw prices go up.

That would basically mean that that difference was $20. 50.

Okay. Likewise, let’s say nonomo assets or the control group though prices went down $20 and OMO assets prices stayed stable. That would still be treatment effect of $20.

We’re basically saying what is the difference in performance of these two groups um these two groups when we account for this treatment uh this pre-post treatment period.

Okay.

So that’s the first fact we see positive price movement on OMO assets relative to nonomo assets. Uh in percentage terms this is any think this is between 9 and 12%.

So that’s pretty good. They performed they outperformed um they outperformed OMO assets outperform nonomo assets by you know 12 to uh 12 percentage points or 12% which is which is quite good. Uh so that’s the first positive.

Uh the second positive is that our primary revenue grew by 6,259%.

Now obviously we have to take this number with grain of salt.

You can see there the pre- period revenue uh was $130 and the post period revenue was $8,267.

So we’re still talking about small numbers, but the key here is that this revenue can grow and this revenue has been consistent. We’ll go through each one of these in detail in moment. just want to give you guys the highlights.

Um so yeah that’s that’s kind of exciting. Uh do want to also uh give couple of um uh guess for pieces of information about the analysis the period we take the 40 days prior to uh OMO and the as many days uh post of OMO.

So basically through uh yesterday when ran this analysis.

So there’s about there’s just under 80 days worth of data in this in this analysis for the difference in differences economic like for that econometric equation or for that econometric analysis. We remove assets that did not see purchases or sales in both periods.

Okay.

So let’s say you had tank ship that was sold in the pre- period and it was not sold in the post period. we exclude that from the the analysis. The reason being it would be extremely biased to include assets uh to to not account for effectively the denominator of these numbers.

Okay, so we care about keeping the base the the base consistent.

It’s like CPI like you wouldn’t drop bunch of assets from CPI because that’s going to change the the price index.

Okay, so that’s that’s something to note. We’ve got about 80 days worth of data and it’s only assets that were sold in the pre and post period in this analysis. Um am going to pause there before jumping into more uh detail on the numbers uh for questions.

think see what does it mean? >> Yeah, I think doesn’t know what OMO is. Chris, if you can do extremely quick recap.

>> Yes, that is great point. I’m I’m sorry for for um for not giving comprehensive overview of what OMO is. OMO stands for open market operations and it is basically new revenue model, new pricing model for ships and and for uh automata more generally.

Previously uh what we had done with assets ships and claim stakes uh because that’s the analysis that we’re doing. We’re not including crew. This entire analysis excludes crude by the way.

Um what we had done previously is we had priced assets at what were called origination prices and origination prices were determined in like 2021 2022 super early on uh when the company was first forming and they were priced by different people in different time period and the market has shifted significantly. And so what open market operations allows us to do is it allows us to utilize our incredibly rich uh price data from our marketplace. Uh it allows and and and uh it allows automata to participate in the open market.

We are effectively an open market participant. We’re buying and selling ships for uh prices that are closer to the equilibrium price, the market price. uh whereas before with origination prices they were you know hundred you know hundreds of percents higher than what the actual uh what the actual prices were going for in the secondary.

So um that’s open market operations. It allows automata to buy and sell ships at market rates. Um, and that has had positive impact, and we’ll go into this in more detail, that has had positive impact on those assets prices, and it has had massively positive impact on primary revenue for Automata.

Um, so, uh, without with that and those three data caveats, 80 days only assets that were bought and sold in the pre and post period, and we’re just looking at ships and and claim stakes. Let’s move into some more uh explicit uh data. Oh um will you briefly discuss the influence of other variables in the analysis for example zinc.

Um, so in terms of variables in the econometric equation, don’t have like zinc uh launch like one could one could potentially put in an event uh an event dummy variable and that’s basically just variable that says was it post zinc announcement or prezync announcement. Um, don’t really have anything in this model that would account for that. Um, also exclude uh Crew and when we first launched Zinc, uh, Crew did see an uptick in purchases because they’re such kind of high yield thing in terms of ZXP earnings.

So, I’ve excluded ZXP uh, or sorry, I’ve excluded um, Crew, which means we’ve probably accounted for lot of the additional revenue that would have come from zinc.

Also, if you look at the the pre and post volume in the previous uh slide, in the 40 days leading up to OMO, when Zinc was active or the preseason for Zinc was active, we only sold $130 worth of ships and and structures.

So, uh that shouldn’t impact this analysis.

Okay. Sample size concerns. Uh yes, there are sample size concerns and will get to that.

Um actually, can get to that right now.

So, one of the tricky one of the caveats or things to note when I’m going through this analysis and this presentation is that um we are working with limited data. mentioned that we only have 40 days before and we’ve got about um 30 31 days prior. Uh so we’re not working with ton of data.

We do want the pre and post periods to be about the same length.

Okay?

So, we don’t necessarily want to um we don’t necessarily want to uh look at year’s worth of data pre and only 30 days prior. We want the the the amount of data on either end of the event that gray line to be about the same. Um but because we are working with relatively low volume, um we are going to have sample size issues.

So these estimates that present here in this table below um let’s chitchat about those real quick. First of all, these are not statistical estimates.

Well, sorry, should should caveat that. These are not econometric uh estimates. These are not um if anybody knows anything about econometrics, these are not actual estimators.

These are literally just sample averages.

So the statistical validity of this model uh also run that I didn’t want to muddy this presentation those estimates are um they’re statistically they’re would say they’re they’re not statistically significant in the way that an economics journal would like them to be statistically significant meaning at the 1% level. What that means is 99% of the time the estimate that you estimated that 30% at the bottom at the farther farthest right of the the chart um 99% of the time that estimate is somewhere that that is um or sorry 99% of all estimates will lie uh around that value.

So that’s something to to keep in mind. We’re working at more around the 75 to 80% uh range.

So, we’re still pretty confident. mean, it’s it’s very it is more likely that this is true than not. Um, but we’re not uh at that super super high level of uh of of accuracy.

As we start to as we absorb more data and we’re able to use more uh uh you know, rows in our analysis, those estimates will hopefully get more accurate and the statistical significance will increase. As I’ve run this over the past few days, every day the statistical significance keeps going up just simply for the fact that we’re getting more data.

Okay.

So, you can uh you can always run, you know, analysis. I feel confident that the estimators are in the correct direction, meaning positive is the correct direction. Um it’s just the statistical validation is not quite where we would like it to be.

So, that’s something to to keep in mind. Um that’s a super important caveat. Um so you know this is not um this is not like the end all beall but it is as good as we can get with the data that we have.

Um so let’s look at this. Let’s look at this uh table in the bottom.

This is truly uh visualizing what difference in difference is.

Okay.

So if we look at the uh group column you’ve got omo assets and you’ve got nonomo assets.

It’s lot of O’s and it’s uh I’m struggling to to pronounce it properly.

So OMO assets, that’s our treatment group. And nonomo assets, that’s our control group. You look at the average price in the pre- period.

That’s before OMO is launched. The OMO assets have $52. 5 average and the nonomo have 56.

2 average.

Okay, so very similar prices. Keep in mind for difference in differences, we do not need those two numbers to be the same. They just need to trend the same.

they just need to move in similar ways previous uh you know before the treatment happens.

So they don’t have to be on top of each other because we’re accounting for the difference anyway.

Now let’s move another column to the right in the post period. The average OMO price goes up to 68 $68 and the average nonomo price goes down to $51.

Okay.

Now here is where diff and diff matters or where it where it shines. If the if both of these assets had increased or decreased at the exact same rate, the fourth column there, the the rate of change, um if they had increased or decreased at the exact same rate, then there would be no statistical difference and in fact there would be no economical difference between the two groups. And what that would tell us is that there was no impact of OMO on uh our asset prices.

Um, now that result would actually be perfectly fine because what we want with OMO, we do want to support prices, but we more importantly want to not negatively impact prices.

Okay, so as long as this estimate is positive or or zero, we’re happy. Uh, we’re not having detrimental impact on the assets that we’re focusing on.

Okay, so with that out of the way, you can see where the diff and dip comes. You see the change column?

That’s our first difference.

That’s what is the difference between the post and pre- period for OMO for the treatment.

And then the bottom one is what is the pre the the difference in the post and pre uh price for nonomo for the control.

So you can see that the treatment group went up $16 and the control group went down $4. The assumption here, the parallel trends assumption as it’s called, is that if we had not implemented this OMO change, then the OMO assets would have also go down gone down $4. 36.

But because we implemented this change, they actually went up 16.

So then what we do is we say, okay, well, what’s the difference in the price performance of the two assets pre-post? And that is our difference in differences.

That’s the $2053.

So you’ll you’ll notice you’re like, “Well, OMO only went up $16.

So how can we say that the positive impact was $20?

That’s more than 16. ” Well, it’s because had we not done this, the the model suggests that prices would have gone down $4. 36.

So when we take 16us -4. 36, we get $20 positive $203.

Okay.

So in percentage terms, OMO assets went up 30. 8% 8% and nonomo assets went down 7. 8%.

So actually misspoke earlier. said there was about 12% increase uh or um 12% net impact.

This is this is closer to 30 37 38% net impact uh on on uh in terms of the difference in difference estimate. Um so that’s pretty major positive effect. Again there’s possibility there’s statistical anomalies here.

there’s certain things that we’re not picking up or maybe there’s some crazy uh thing happening in the background.

But in terms of our best practices, our best possible economic approach that we can take look at this, it does support the OMO uh it the OMO hypothesis that hey OMO is actually good for these assets. Um skipped over the chart at the top. want to point it out very quickly.

Uh what the chart at the top tries to do is it tries to look at what we call the parallel trends assumption and that’s actually what was trying to visualize here.

This is the parallel trends assumption. It says in the pre- period the two prices of the two groups the treatment group and the and the control group were identical. They had the same slope.

And then as we move into the post period, they still have the same slope, but one of them is shifted up because of some whatever the whatever the treatment was.

Okay, so that’s the parallel trends assumption. It basically says it wants you to make sure that these two groups are identical or at least as close as possible to identical in the pre- period. And the only difference between them in the post period is that one of them got the treatment and one did not.

So obviously there’s some big spikes in the OMO assets in this pres in this uh chart up here.

That’s due to the fact bec that’s just simply due to the fact that there are fewer OMO assets than there are nonomo assets. Um, so you know good good way to think of this uh you know let’s if we were to visualize how many miles run every day just me just Chris you would see three miles on one day zero miles on another day four miles on the next day zero miles 2 miles three miles you would kind of see this really spiky behavior that goes up 100% down 100%. Um, but if you took million Chrises and you averaged it out, you would see, oh, an average of 3.

5 miles per day.

So, that’s kind of why we have this spikiness is that the OMO assets are such small sample.

There’s only, you know, 10 or 12 of these things uh in the sample to begin with. And so, they’re going to spike around quite bit depending on what is being sold on the day. Um, so the point of this is to show that the trends are parallel.

they’re rel the two lines move relative to one another. Um so when one goes up the other goes up and when one goes down the other goes down.

And then the reason that we have this positive effect is that in the post period the uh red line is high is increasing at rate that is greater than the blue line is increasing and in fact the blue line is decreasing.

This is not to say or keep in mind like this is not to say that um it it’s not to say that um nonomo are bad and OMO are good. Um it’s just to say that OMO outperformed the blue line. If the red line the blue line and the red line could both be going up.

But if the red line is going up faster than the blue line then we’re going to get positive treatment effect. We’re going to get positive estimate at the bottom.

Okay.

So you might be looking at this data and say well this is kind of messy and it’s crossing crisscrossing. One important thing that we do in the model is we aggregate all the pre data and all the post data. We make sure that we have only observations that show up in the pre and post period.

So what we do is we basically treat this like two big periods.

We have the big first period and the big second period. Um and then we compare those averages in that way.

So in that way we actually capture uh this spikiness. Um but did run an analysis to make sure these trends are uh kind of in sync and they are okay.

So this is uh you know this is lot.

This is probably one of the more technical presentations put together uh for for the um for the econ forum.

So do want to just take a break and make sure that everybody’s kind of following and and that this makes sense. Um this is this is pretty high level economics.

So if uh if there’s any questions would not be surprised. >> Time to ask your questions guys. Uh Lor ask could the asset value appreciation for Roma assets be attributed to actual liquidity being provided ruining the impact that market bots have?

>> Uh interesting.

Okay, let me try to reword that.

Okay, so the idea being because we’re offering assets for sale, how does that impact the bots? guess I’m little >> Larry know if you want to clarify. >> Yeah, I just think buyers are probably slightly Where is that message?

It’s up here. Chris. Oh, that’s that’s somebody else.

>> He tagged me on the question. >> Oh, could the asset value appreciation for OMO assets be attributed to actual liquidity being provided, ruining the impact the market bots have?

So, okay.

So think what he’s suggesting is like in the absence of automata um um >> oh he said here Chris with with proper liquidity being provided the bot can’t manipulate the spread as much as before. >> see.

So so basically we have effectively you know pretty big wall uh of sell orders at this at whatever our price was set to. And so the uh the bots aren’t able to, you know, because that’s that’s lot of liquidity to dig through. The bots aren’t able to manipulate the market as well versus if there was one or two ships for sale.

Those those are going to go much quicker than um than something else. That is entirely possible. And and think um there’s lot of like there’s lot of so what this shows is just statistically what was the performance difference between the two groups and this also suggests that that statistical performance difference that economic performance difference has to do with OMO.

Now the question is okay what are the mechanical things to do with OMO? We we keep saying OMO is treatment.

Well, in traditional, you know, in in medicine, for example, treatment is it’s drug. Um, in this case, OMO is whole bunch of different things.

It’s well, we pulled bunch of assets off the market. We listed bunch of assets on the market, smaller number. We uh manipulated the perceived outstanding supply of assets because we delisted bunch of assets.

We manipulated the perceived value of assets by putting in new prices. Um, so there’s whole bunch of different mechanisms at play here. And think the question is, okay, well, which one of these is the powerful one?

Um, I’m of the belief that the, let’s call it psychological impacts are less important.

So, oh, I perceive that the prices are different than they are that they were before. perceive that the supply is different than it was before. I don’t think that that’s how our players trade.

think our players are very sophisticated and don’t think that they’re heavily influenced by uh psychological components.

Now, could be wrong about that.

That’s my that’s my prior belief. And so, what think is probably more likely is just the statistical component here that that Lenor is pointing out is like, well, we’re providing liquidity in markets that are relatively illquid. And that’s actually exactly the intent.

One of the one of the criteria that inspect when I’m choosing OMO assets is how much sell order liquidity is there. And prioritize assets that do not have lot of sell order uh liquidity, but they have lot of buy order liquidity.

So, there’s lot of buy side, less sell side.

So, that’s kind of what I’m focusing on. And it seems like maybe that could play into what Lenor’s uh brought up uh with respect to the bots. Um, so really interesting uh discussion to be had there.

Awesome. >> All right.

So, this Oh, sorry. Go ahead. >> No, no, just checking if there was any other questions for you here, Chris.

Uh, there is one from Sim. Chris, have you considered that some of the more appealing assets thinking to claim stakes and samples were only oversold before and with more players bought them because they were much better priced? Um so basically that like the the impact of the in the increase in mean this is think if I’m understanding the question correctly this is necessarily true right this is like our our assumptions that we built open market operations on was that if we provide assets for prices that are more palatable to players that are more in line with the current market conditions, then people will spend more money.

People will be more inclined to purchase those things. Um, and yes, that will have kind of this double effect where we provide liquidity, people make purchases, and that actually creates demand for the it’s almost this like weird secondary effect where it creates demand for um for the asset where it wasn’t before because nobody could um nobody could, you know, purchase it because there was nothing available. Um, may may be misunderstanding that question though.

Um, see question for Larenor, but that’s for the Q&A.

So, let’s try and save that for Q&A if we can. Uh, self-referral discount. Uh, some people were able to still use Oh, interesting.

This my sorry, I’m just going to address this right now. The self-referral uh program, my understanding is that should be fully shut down. That should not be uh apply that should not apply anymore.

Um, so if that is still working, that would be super weird and would probably want to bring that up with Michael Wagner. >> Uh, Chris, think you can keep going. >> Yeah.

Okay. Um, so, okay.

So, we’ve discussed the first KPI that mentioned, the price of assets.

That’s one of our the most critical things. We want to support prices by putting in sell orders above the market price and only executing those sell orders when the market price goes above where it was when we listed the orders.

Okay.

So, we’re trying to create positive uh positive, you know, price action. From the data and from the econometric analysis that I’ve performed here, it would appear that that is true, that that is happening, that we are supporting the market. That seems really good.

Um it’ll be very interesting to run the same analysis for the next batch of OMO assets uh which we will do uh hopefully today. Uh I’ve got list that will share with you at the end of the presentation on the new assets and their prices and hopefully those will go out today. Uh the next thing that we’re interested in is revenue.

So the other compon critical component here is okay well we’re able to support markets.

That’s great. One of the the components of the flywheel that we mentioned when we were announcing OMO uh in the first place was well as Automatus starts to generate more revenue, we can invest that revenue into doing into uh stopping our token liquidities on the open market for Atlas and Polus as well as purchasing ships where needed.

So for example, you can imagine ship market that’s really depressed because there’s just maybe there’s too many of the ships or maybe it’s not very good ship.

we can kind of control supply by purchasing up ships using some of our revenue.

So revenue is an an in essential uh component to the open market operations and you can see the highlight there in the bottom. already mentioned this but the OMO group uh saw revenue growth of 6,259%.

So, we went from $130 worth of ships and structural assets being sold uh before OMO and we went up to $8,267 after OMO. Um the important thing to note, the nonomo group, the reason that there’s zero dollars sold there is because we don’t have any of those assets listed.

There’s there’s zero available.

So, uh, the more important figure is then the number in the bottom where we have $358 pre-omo and 8,267 post OMO, which is total um, across all assets. Again, excluding crew, across all assets, we saw revenue grow 2,211%. Um, and that’s using an even bigger pre- period time period than the post period.

So remember we measure from uh believe we measure from November 1st. Uh so that’s you know that’s almost think it’s anywhere from 40 to 55 days of data and we only have about 30 days in the post period.

So if we were to limit this to 30 and 30 we would actually probably see an even bigger change. We could also look at this on the daily level and that’s going to be huge number as well. The visualizations at the top really uh you know they provide stark um uh stark visual comparison.

You can see the primary sales revenue in the pre- period uh was 130 and 228 respectively for OMO and nonomo and then in the post period we go up to 8267. And you can see on the right hand side the reason plotted both of these was so that you could see how consistent this revenue is. Um the the revenue uh has been obviously there’s little bit of swings.

We see big day with $1,200 worth of primary revenue and then we see day with, you know, 20 maybe 10. Um but the important thing is that we’re consistent.

We are consistently seeing more and more revenue coming in every single day. And why this is important is that this scales with user growth as we have more consistent uh revenue that comes in every day as opposed to in big giant chunks every couple of months. Uh we are able to grow our user base and revenue should scale proportionally or at least you know somewhat proportionally with uh the player base.

So this is kind of the second finding.

This is not difference in differences because again there’s no nothing interesting about revenue generated by the non uh OMO assets but uh we’re seeing revenue come in the door again for the first time in in while and it feels really good. Obviously the number is uh off by probably 10 or 100x. We would like to see that number bigger, but um it, you know, going from $130 the two months prior to $8,200 in the one month uh post feels pretty good.

So, all that to say, and you know, I’m happy to to debate this with people. think there’s uh probably some rightfully uh skeptical people in the audience, but uh all that to say, in my view, the open market operation program has been an incredible success.

It’s generated revenue and it has also helped not hindered the prices of assets that were part of the OMO program. >> Absolutely, Chris. think it’s it sets the foundations for something bigger, which is when the we release the new gameplay loops, when we create the new mechanics, we needed this framework.

We needed good framework to to ensure our success when when the new game modes, the new programs go live.

So, it is for sure win for Star Atlas for sure. >> We Yeah, Larenora points out the main concern was prices dumping and and they didn’t. And think that’s um think if if that’s the only takeaway, think that’s kind of the most um pessimistic approach is like well at the very least prices didn’t go down.

Um and in fact being more optimistic actually think they probably went up. Um now again there’s some statistical in uh you know validity that we would like to gain over time as as we gain more data.

But, you know, if there’s one thing you take away from this, um, and it’s kind of the steelman, uh, case for for this whole entire operation is, well, prices didn’t go down and the automata started making money again.

So, um, don’t know. I’m happy camper. hope hope the players are are feeling good.

Um, but yeah, this has been very invigorating exercise for me. Um because to be honest, you know, was watching my normal data come in, but doing going into this analysis, had no idea what to expect. Um you know, and one thing want to point out, um caveat to uh or not caveat, but an additional insight when we’re doing difference and differences, we could have ended up with positive price impact of OMO, yet both OMO and nonomo prices declined.

And the reason for that is suppose OMO prices went down 10%.

But nonomo prices went down 20%. We would end up with positive 10% coefficient or or effect. The reason for that is because OMO prices went down less dramatically than uh than nonomo if that makes sense.

So just want want to kind of prime you because who knows what the next analysis will look like. Maybe we see positive effect but prices are going down. Um, so it’s all about how are the OMO assets doing relative to the nonomo assets.

Um, okay.

So with that, let’s uh get into the kind of uh later latter discussion. Um, we’ve kind of gone through some some data. Hopefully that was not too overwhelming.

Um, and it’s been while since I’ve taught class, so don’t know how well presented the uh the difference and difference framework, but uh it was lot of fun for me. Uh hopefully hopefully it was fun for you guys. Um today uh we’re planning on launching the next set of open market operation uh listings.

What this will mean is we pull down all the current listings. All the current listings will be removed other than crew.

So all the all the claim stake offers will be will be pulled down. All the any other ships that are that are up will be also pulled down. And what we’ll do is we’ll be replacing those with these new uh this new lists.

There is overlap.

Some of these assets were already listed. Um yes, all of them earther. Um all of the assets will be removed.

Now again, some of these you’ll notice are already listed as OMO. Um so just because you were OMO last time doesn’t mean that you won’t be OMO this time. Um that word is going to haunt my dreams.

So you can see we’ve got all five all five claim stakes, all three Armstrong, and then couple of other ships, the Fimble SlideBGE, Calico Evac, the Fimble Loi, and the Fimble Airbike are all going to be listed. don’t have quantities yet, but uh they’ll be similar in the same neighborhood as the first trunch of OMO. And those are the prices.

Um so very excited about this going out. It’ll be very interesting to see how things go uh for this next trunch. Um, and yeah, that’s that’s hopefully going to go out by the end of the day.

Um, take your screenshots, uh, just so you can hold me accountable. And if you know, one of these prices is different, you can, you know, tell me to tell me to, uh, to, well, I’m probably not going to care, but, uh, you can you can at least message me about it. Anyway, uh, that is, uh, that is really all there is to say about that.

don’t know if there are any questions. um uh some love for recipes.

Okay, so let’s move into Yeah, let’s move into uh uh some additional topics in Q&A. Lenor, uh you asked very very critical question. Can we get some love for the redemption recipes?

So, um we launched the new redemption recipes and then we went on twoe break and it’s been crazy since got back. understand that those recipes although they were decreased from the old recipes by think about 85 80 to 85% they’re still too high. Um so want to uh revamp the redemption recipes again.

I’m hoping to do that by the end of the day but it might be tomorrow. Um so yeah, we should expect some changes to the redemption recipes uh very very soon. Um, probably would imagine we would do that at the same time that we put the new OMO listings up.

Uh, but you know, these things never go to plan.

So, uh, will be putting through redemption recipe update. Uh, so stay tuned for that. Um, let’s see if there’s any other Q&A.

Uh, Chay had kind of an interesting um, uh, topic he thought might be interesting to to talk about is this Fogo uh, chain.

I think that many of you are probably familiar with Fogo. Um it is Salana uh SPML L1 and their whole thing is cheap super fast uh you know hypers speed transactions. It differs from zinc in that it doesn’t have the identity component obviously um but it is kind of similar concept and so we can kind of look at them as comparable chain when we think about potential zinc uh you know FDB and and market cap.

So they’ve got they’re currently sit well as of yesterday we’re sitting at about 120 uh million in market cap. think they started at about 400 400 million. Uh, so that’s kind of, you know, that’s kind of the neighborhood that we’re we’re we’re hoping to be in when we launch the the Zinc chain.

Now, something to note about Fogo is that they do have institutional backing. Um, which is which is probably something that differentiates them from us. Um, uh, yeah, they’ve also got, uh, they’ve got they ended up listing on Binance.

I’m not sure how much of their supply that took, but, uh, probably non-trivial amount. Um don’t know if you had any other thoughts uh chose or if anybody else had any other thoughts. I’m happy to invite people on on the stage honestly to to chat.

>> Yeah, not sure if anyone in the audience want to step in and discuss this but found it very interesting because it shares some similarities to what we are building here on Zinc. mean it’s another Solana blockchain derived blockchain and found it super interesting because they don’t have the this the community the volume of transactions of uh activity that we have here in Star Atlas.

So was wondering if we that have some very cool products that are will be coming at the release date of zinc like holos scene like uh C4 the PTR mean this this forecast something really cool to us overall that’s what wanted to highlight the most think it’s huge opportunity for us to highlight the power of the Statatus community and Oracle system. can’t wait. It can’t come fast enough, right, Chris?

Zinc, C4, PTR, all of this. >> Yeah, would say C4 can’t come fast enough.

That’s my That’s my number.

Well, I’m kind of between C4 and Hollow Sim in terms of uh priority. think C4 has the most like probably has the most revenue potential and has the most kind of like economic potential obviously. Um but then yeah, Hollow Sim has kind of think the most the most potential to bring in new users.

Um, so, uh, yeah, but C4 going to be launched on zinc. Like that is an essential kind of need.

That’s that’s fun chicken and egg. Um, where we need uh both uh both zinc and C4 simultaneously because C4 is going to be kind of the primary one of the biggest pro products on the zinc uh blockchain. Um, Jose, people are asking for the code.

Do we have code? >> Code. Hold on.

Let’s see if can get us code. Uh, meanwhile, Chris, you can talk about the think uh Neo asked about phantom star bases. Let me see if can get this question for you.

Yeah, I’m sure there’s some there’s some concern about phantom star bases >> here, Chris.

Okay, >> let me see. What do we got?

This is going to be tough one. could tell became largely idle server where many players are literally watching their accounts.

Yes. Um yeah, mean it is no secret that Phantom Star Bases were uh shall we say necessary evil. Um we knew that they weren’t the best, you know, the the like the most enjoyable uh gameplay experience.

But we also had serious problem where was it mean it was really came down to decision of okay we we don’t have the resources to we don’t want to sink resources into developing new game game systems for Sage which is about to be deprecated.

So the question is okay what’s the minimum viable solution that can solve the that is that is implementable because the alternative is to shut down Sage.

So that was what phantom star bases were.

So think like um when evaluating this question think the important thing to ask yourself is okay would Sage be better if it had been shut off in the meantime while we uh developed and launched Core. think that’s the that’s kind of the question you need to ask yourself. From from my perspective and from Michael’s pers perspective, it was not it was not it would not have been better to shut this system down and rather, you know, we would just implement this game design feature that we knew was not not the best possible not the best thing in the world.

It was the best thing that we could do with with the amount of resources we had. Um so you know it’s it’s tough one on okay was phantom were phantom star bases mistake and should we have you know should we have not done them um don’t know uh so so that’s kind of guess the strategic view um from an implementation from more practical view C4 will be absolutely nothing like Sage mean just the economic loop in C4 is it’s almost scary how different it is um from from from Sage. Um, so I’ve already kind of talked about what the econ loop looks like, but basically uh we we had this big presentation yesterday that Jose mentioned at the beginning of the call.

We are not going to have LP at all in uh C4. There’ll be similar systems. They’ll be similar.

You’ll get rewarded for your efforts, you know, to to to help your faction. Um, but that’s that’s about it in terms of kind of the the like like implementation of LP. Uh, so so LP is is going to be gone.

We are still probably going to have some version of um fix. Um, we can always add another, you know, GT thing. Um, but as has been the plan all along, the main the primary earning mechanism in C4 will be combat.

You’ll be heavily rewarded with combat. You’ll be heavily uh compensated for destroying very powerful, very valuable fleets. I’ve already gone through kind of how that that works.

Um, you outfit basic ship with ship parts. the basic ship itself is not worth very much. You have to outfit it with parts in order for it to be worth something.

So, this basically this tries to deter players from destroying their own fleets.

Right? If can just go and can destroy fimble airbike, it respawns and can destroy it again and destroy it again indefinitely.

That’s pretty broken loop. Rather, you’ll get almost nothing from destroying fimble airbike by itself. Once you outfit it, it becomes far far far more valuable.

So there is almost always um like dollar for dollar, right? You’re not gonna this system is would say much less um like the current Sage system outputs more than is consumed. In the C4 system, it’ll be much more competitive, much more cutthroat.

you’ll have to basically destroy somebody else’s fleet that’s worth however much in order to earn some amount of uh you know Atlas for yourself. Uh that is to say if you’ve got th00and atlas asset and you destroy it, you’re going to get th000 Atlas out of it. Um so there’s kind of you’re going to lose thousand to gain thousand.

So you want to go kill other people’s fleets anyway.

So that’s that’s the that’s the game game economy design there. Um but what that means is that the primary earning mechanism will be through destroying fleet. It will be through conquest.

It will be destroying other fleets and conquering other star bases. Those are going to be the the main activities that reward. Um so that’s how you’re going to uh to get rewarded there.

>> Uh Chris, think we pretty much answered most of the questions. Uh people are asking about when and took the liberty to say that it’s the PTR at least. >> Yeah.

>> Uh it’s coming sooner than we expected, right? At least if everything works out. >> Well, it depends on what your definition of expected is.

But sure. >> No, for sure. It was bullish for me.

At least for me. It was bullish. >> Yeah.

No, it was bullish for me, too. Don’t don’t worry. Um, but yeah, I’m >> will it’s it’s kind of like yeah, it’s like I’m trying to think of good analogy for PTR.

Um, that’s appropriate.

But anyway, I’m let’s just say I’m like, you know, very excited for the Econ forum once once we get that out and we can we can actually start. >> Sure.

It’s going to be uh we can’t say date Michael Wagner will kill us if we do.

But wait, wait for news soon. Very soon. >> Um, Larenor asks, uh, will we get new ship redemption recipes as in new ships?

Um, so was planning on actually launching some new ship redemption recipes in Sage. Um, depending on the timeline for C4 though, that that may change. Um, so, uh, TBD on that.

Uh, did do want to do that. Um but uh and then this master has another any insights on marketplace fees. Oh, discount for market makers.

Uh yes.

So so we did reduce the marketplace fee to $5 uh 5% uh down from 7% or 6%. What? Jesus Christ.

What did we switch it to? Oh no, we removed it entirely.

Sorry, was thinking of the um crafting fee which went down from 7% to 5%. Uh so we removed the marketplace fee entirely. We would say the the findings on that are mixed right now.

We have seen we’ve seen consistent volume.

So, it’s possible that we’re seeing reversal of volume decline, which would be good, but I’m not I’m not confident in that yet. The goal for the 0% fee is to bring re bring volume back to um back to the Galactic Marketplace. Uh so that’s actually something that want to explore next.

Uh very good question. My inclination is to say that the impact hasn’t been night and day like OMO has but uh but that it’s probably reversing negative trend which is good. >> Um think we are think we are golden for today, Chris.

>> Yeah.

Yeah. We’re at time.

People have families and lives and and jobs, so we want to be respectful. >> Um, yeah, thank you guys so much for coming. Um, this is this is one of the highlights of my month.

really enjoy chatting with you guys. enjoy kind of giving you look into day in the life.

So, see you next month. And yeah, power to the people. >> Stick to the man.

Thank you guys. Always pleasure. We’ll see you guys tomorrow on the game night.

Iris bless you all. Have nice day and see you soon. Bye-bye.