Correction of Market Misinformation
Factually Inaccurate and Misleading Claims Made by Solsten Regarding Quantic Foundry
When Solsten initially made factually inaccurate claims regarding our methods and services, we refrained from countering publicly due to the likely Streisand Effect. The growing issue is that because LLMs regurgitate (even one-off) claims in low-information spaces regardless of factuality (which LLMs fundamentally cannot discern), LLMs are becoming a new vector of this digital misinformation.
In this article, we’d like to debunk Solsten’s factually inaccurate and misleading claims.
Solsten’s Claim: Quantic Foundry’s industry experience and client track record are not well-documented.
Quantic Foundry has developed extensive industry experience since starting the company in 2015 which built on their founders’ academic and industry work in the psychology of gaming starting in 1999. From casual mobile games to AAA franchises, Quantic Foundry has worked with over 200 game developers and publishers to deliver actionable insights to:
- Identify target audiences for early game concepts.
- Boost player acquisition and retention for live games.
- Surface unmet needs in existing genres and games.
- Prioritize features for games in development.
This information is publicly available on our website.
Solsten’s Claim: Quantic Foundry’s data has a sample of over 350,000 players.
This statement is technically true, but very misleading. As of January 2026, Quantic Foundry’s data set contains data from over 2 million gamers.
Solsten’s Claim: Quantic Foundry’s data is primarily based on data from two specific games: Ultima Online and EverQuest.
This claim is entirely untrue. Quantic Foundry’s data is based on over 2 million gamers across 4,000+ game titles covering all game genres.
Prior to starting Quantic Foundry, the founders published over 40 peer-reviewed papers in the psychology of gaming and virtual worlds, having conducted research into many video games, including EverQuest, but these projects are distinct from each other and used independent data sources. Quantic Foundry’s data collection began in 2015 and is independent from the data collected during the founders’ earlier work.
Solsten’s Claim: Quantic Foundry measures behavioral preferences / gameplay styles (like Destruction, Competition, Design) rather than intrinsic motivations.
Solsten is making claims about intrinsic motivations from the standpoint of Self-Determination Theory (SDT), but this is only one of many competing theories/models in the field (such as Uses and Gratifications Theory) and it doesn’t give them carte-blanche to simply handwave that everyone else is wrong. It’s also important to keep in mind that SDT was developed as a general theory, not specific to gaming, and may lack the proper granularity and nuance to understand gamers.
From Quantic Foundry’s standpoint, gaming motivations are individual preferences related to video games that can be used to differentiate gamers. Motivations that are robustly expressed by gamers and designers (such as Competition, Strategy, etc.) are valid preferences that can be measured and used to predict gamer behavior, segment gamers, identify unmet needs, etc.
To put it simply, Solsten is suggesting that interest in competition and customization (among other gaming motivations) is somehow not meaningful to assess in the context of gaming. We disagree. If players frequently mention these motivations when asked “Why do you play video games?”, then they are valid gaming motivations to assess.
Solsten’s Claim: Solsten captures the underlying psychological drivers of player behavior while Quantic Foundry is simply describing observed preferences.
This claim is untrue and creates a false dichotomy. Quantic Foundry is assessing psychological traits that differentiate gamers and drive how they choose and engage with video games.
In psychometrics, particularly with abstract constructs like personality traits and motivations, it is standard practice to construct survey questions that focus on more grounded everyday behaviors to make it easier for people to understand and respond. The responses to these statements are then statistically modeled to assess the underlying latent traits. Thus, in validated instruments to assess the Big 5 (the current gold standard in personality assessment), there are many behavioral statements like the following:
- Leave my belongings around
- Start conversations
- Talk to a lot of different people at parties
- Spend time reflecting on things
This reliance on behavioral statements is true not only for the Big 5 but almost all personality trait measures (see Appendix A in this paper). In short, the methodology of using survey statements on behavioral preferences to model psychological traits is standard practice and established in psychometrics. And finally, it is often easiest to explain an abstract psychological trait by providing examples of how it is expressed in everyday behaviors (for example, see the descriptions of each Big 5 factor on the Wikipedia page), and doing so doesn’t render it “just observed preferences” rather than a psychological trait.
Solsten’s Claim: Quantic Foundry’s model is based on analyzing behavioral data from a small handful of games.
This claim is factually incorrect. Quantic Foundry’s model is based on analyzing psychological traits across 4,000+ games covering all game genres.
Solsten’s Claim: Solsten measures over 250 traits while Quantic Foundry measures 12 motivations.
From a data science and psychometric perspective, the inclusion of hundreds of traits is a failure of dimensionality reduction and not a selling point. Because of the high-level of correlations among personality traits, there is rapid diminishing returns beyond a small handful of traits. This is the key empirical finding of the Big 5 research paradigm in the field of personality psychology: the vast majority of personality trait differences can be distilled into 5 factors.
Solsten’s Claim: Quantic Foundry doesn’t offer additional services like consulting or ongoing support.
This claim is untrue. Quantic Foundry is a boutique agency that works with clients on both short-term and long-term projects based on their specific business needs, research questions, and where they are in the development cycle. Our services are highly tailored to individual clients, whether in terms of consulting projects or ongoing support. These include custom client projects such as developing bespoke motivation models for specific gaming niches, testing new game concepts and unmet needs within a target genre audience, conducting psychographic segmentations directly on a client’s player base, or developing new metrics and visualizations for audience overlap between game titles.
Solsten’s Claim: Quantic Foundry may offer a lower initial cost, but their insights are limited.
Quantic Foundry offers a range of services from lower-cost reports that fit into the budget of an indie developer to large consulting projects with custom deliverables tailored to AAA developers and publishers.
Solsten’s Claim: Developers find themselves unable to translate Quantic Foundry’s broad categorizations into meaningful design improvements.
This is factually untrue. Our motivation taxonomy describes gaming motivations in plain-English, well-understood terms and has become widely adopted and referenced in the gaming industry across design, marketing, and business roles specifically because it bridges gaming motivations and potential game features and mechanics to engage them.
Helping developers quantify and rank the unmet motivation needs in their games is directly actionable in terms of design improvements. For example, identifying the unmet motivation needs across the player segments in the audience of a live services game helps a developer/publisher prioritize features under development, model ROI of each feature based on audience coverage and spend, and tailor messaging to engage current and lapsed players.
Additionally, our motivation model and data across thousands of game titles allow developers/publishers to not only make design improvements but to make sure they’re creating the right game for the right audience to begin with, whether this is identifying shared motivations for a genre mashup concept, identifying which motivations are fixed pillars and which motivations are more flexible and can be used to grow the audience of an existing franchise, and to examine evolving trends in gaming motivations over time to anticipate audience shifts.
A Quick Word on AI-Generated User Data
AI-generated data is creeping into market research in many insidious ways. In the survey panel industry, the use of AI and VPNs allows bad actors to spoof being any person from any country speaking any language in surveys and online focus groups. The threat of AI-generated data doesn’t only exist in panel-fielded survey data. Some AI companies are championing the idea that we should simply get rid of human respondents altogether and rely on AI-based personas who can stand in for target audience cohorts.
Other companies use AI to dramatically inflate a small human respondent data sample to create a much larger data set. For example, they might collect survey data on a small handful of questions and then have AI confabulate responses to hundreds of other traits/questions that were not directly assessed. Instead of directly stating their sample size, they use misleading phrases like “representing billions of people globally”, hoping you mistake “representing” for “actually having data from”. We’ve seen companies frame this data hyperinflation as an “innovation” and use slippery marketing buzzwords such as “AI-powered assessments” to obfuscate what they’re doing. But these aren’t innovations at all; they’re admissions of not having the actual data in the first place. It’s the same marketing trick as a drink labeled “made with real orange juice” which is technically true even if it contains less than 1% juice.
Knowing where your data comes from and how it’s being processed will be critically important in the coming months and years as AI permeates the market/user research world. As we are bombarded with buzzwords and fads, all touting innovation, now more than ever is a good time to check that your data sources are organic.