The cryptocurrency market has turned bearish, just like the tide receding, revealing the 'hollow shell' essence of many projects: maintaining appearances through narrative, hype, or subsidies. Once liquidity tightens, projects lacking a real business closed loop will collapse rapidly.
At this time, projects like KGEN highlight their value. It does not survive by telling stories, but rather has built a 'verification distribution protocol' with a continuous cash flow—providing real users and high-quality data for gaming, AI, DeFi, and other sectors through strict human identity verification.
The two main revenue engines of KGEN
The 'real user intermediary' of the gaming industry
The biggest pain point for game developers is the influx of many bots and opportunists after advertising, making the acquisition cost of real players extremely high.
KGEN uses blockchain + multiple verification mechanisms (such as VeriFi products) to ensure that distributed users/rewards go to real people. Vendors pay for activating (UA), retaining, and paying real users, and KGEN takes a service fee from it, forming continuous cash flow rather than one-time transactions.
The 'human data factory' in the AI era
Large model training requires massive, high-quality human-labeled data (language, images, voice, etc.).
KGEN's tens of millions of verified users can participate in simple tasks (such as data labeling, behavior recording), and the platform sells the processed structured data to AI companies to earn the price difference. During the AI demand explosion period, this type of 'verifiable human data' is extremely scarce.
You might ask: can't others replicate this model?
This inevitably brings up the moat built by KGEN.
Scale barrier: about 48.9 million verified users, each user has undergone multidimensional identity/behavior verification. This is not a scale that can be generated in the short term.
Data network effect: the more users, the richer the data, the more accurate the models → attracting more AI/game customers to pay → revenue increases → rewards improve → attracting more users, forming a positive cycle.
First-mover advantage and stickiness: early accumulated users have earned rewards on the platform, showing a certain degree of loyalty; later entrants need huge investments in verification + incentives to catch up.
After discussing KGEN's business model and moat, let’s refocus on $KGEN .
I believe that $KGEN is a severely undervalued 'gold mine'.
Let's do the math:
KGEN's annual revenue: 80 million dollars
Current FDV valuation: about 322 million dollars
Valuation multiple: 320 million ÷ 79 million = about 4 times
What does this concept mean?
In the traditional tech industry, companies like KGEN with stable income typically have valuation multiples between 10-15 times. This means KGEN's theoretical valuation should be between 800 million to 1.2 billion, which is 2.5-4 times the current valuation!
In a bear market, the market often indiscriminately sells off regardless of quality, leading to quality cash flow projects being mistakenly sold off in the short term. This is actually a window for positioning: projects supported by real income have a bottom during declines, and greater elasticity when value returns.
The value of $KGEN has gradually emerged in the cold market, starting from about $0.19 in early January 2026 to now about $0.26, with a monthly increase of over 60%. The current price is fluctuating between $0.25 and $0.3, most likely in a consolidation phase, washing out floating chips, and reserving momentum for the subsequent market.
Conclusion
During a bull market, everyone makes money, hard to distinguish between true and false; the bear market is the touchstone, capable of leaving behind truly commercially viable projects.
KGEN, with real human verification, is positioned in two high-demand tracks of game distribution + AI data, and has verified tens of millions of users and tens of millions of dollars in revenue, forming a relatively solid moat.
For investors looking for 'cash flow, barriers' targets in Web3, KGEN provides a relatively low-risk observation sample: in a bear market, it is not afraid of the cold; when the market comes, projects supported by real business often rebound the fastest.
