KQED61%

What Does It Take to Get a H-1B Visa? This Video Game Shows Just How Complicated It Is 0%

By Azul Dahlstrom-Eckman29%

3/19/2026, 2:00:15 PM

BS Summary: This article contains 10 faulty reasoning types, including Anecdotal, Politically Left Leaning Bias, and Appeal to Authority, with Appeal to Emotion as the most egregious example at 13.8% saturation with 101 hits. Analysis detected 408 faulty-reasoning hits from 734 analyzed words, generating a BS Score of 0% and a BS Rank of 0% (0 of 21,176 articles). This article is better (less manipulative) than 100.00% of the article peer group.

For Allison Yang, the founder of the video game studio Reality Reload, the H-1B visa process has all the basic elements of a game. 
Time, skill, strategy and a lot of rules. 
Players have a certain degree of control, but other aspects are pure luck  similar to the roll of dice. 
“The player is usually the one who has the least power, but they are the one who has to play through. 
So, that tension is something we wanted to focus on,” Yang said. 
These elements  along with a desire to highlight the United States’ shifting immigration policies and their impact  inspired Yang to release a prototype of h1b.life, which aims to simulate the H-1B visa application process. 
“We wanted to build a life simulation of people who are going through this process,” said Yang, who recently showcased the game at a game developers conference in San Francisco. 
The H-1B visa allows immigrants in a number of professional fields to legally work in the country. 
Tech companies in Silicon Valley have long used the program to recruit top talent from around the world. 
Every year, hundreds of thousands of people apply to the lottery, which is capped at 85,000. 
In h-1b.life, players take the role of an immigrant trying to get and maintain H-1B status. 
Playing on a smartphone, the top half of the screen has life scenarios, and the bottom half shows a series of choices. 
“You are a 20-year-old exchange student from Shanghai, and this is your first time in the United States,” reads the opening lines of the prototype. 
“During high school, you spent hours and hours on your laptop, binging Gilmore Girls on shady, unauthorized streaming websites. 
Everything in your drowsy new town reminds you of the show.” 
These storylines are drawn from around 20 interviews with H-1B applicants, according to Yang. 
She said h1b.life aims to show the uncertainty of immigrants trying to keep their visa status. 
In the game, players succeed by maintaining four core attributes: intelligence, wealth, social support and burnout rate. 
If any of these run out, it triggers a “roll the dice” feature where different gods decide players’ fates. 
One of these characters, known as “orange god,” bears a strong resemblance to President Donald Trump. 
The Trump administration’s ongoing immigration crackdown has imposed several new rules on the H-1B visa lottery. 
Under the latest regulations, employers seeking to sponsor an H-1B applicant could be subject to a $100,000 fee, as well as more selection factors, such as salaries, and limitations on visa appointment locations. 
“Getting selected is a feat that some individuals spend years hoping to achieve, and many are disappointed and they are not able to successfully make it through, and they have to leave even after putting down roots in this country,” said Sophie Alcorn, an immigration lawyer based in Palo Alto, whose clients primarily include H-1B applicants seeking to gain authorization to work in Silicon Valley. 
Alcorn said a video game representation of the H-1B process makes sense. 
“There’s randomness, there’s luck, there’s skill, there’s strategy, there’s positioning yourself, there’s trying to go around and collect badges and items to upskill to be able to get to the next level, just like in a game,” she said. 
At a recent showcase of the h1b.life demo at San Francisco’s Asian Art Museum, Donduk Dovdon, a H-1B recipient from China who gained U.S. citizenship two years ago, tried the game. 
He said it was too accurate, even triggering. 
“It’s a very long path, and you sacrifice so much personal time, especially with your family,” said Dovdon, adding that he didn’t go home to see his parents for ten years while he was pursuing citizenship. 
Dovdon said he thought the game was “too niche” to have widespread commercial success, but thought it could be useful in other applications, like corporate diversity training. 
Software engineer Krish Chowdhary also played the game. 
He immigrated to San Francisco from Canada on a different work visa, and said what the game does get right is the way it depicts immigration status as a series of choices. 
“When I meet other folks who are on a visa, it’s like one of the first things people talk about. 
Because it weighs on a lot of your other decisions,” he said. 
Article reasoning-pattern comparisonThis article: 5.4%Azul Dahlstrom-Eckman: 1.6%CalMatters: 1.9%Confirmation Bias5.4%This article: 0.0%Azul Dahlstrom-Eckman: 1.2%CalMatters: 0.9%Anchoring Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 3.0%CalMatters: 3.0%Availability Heuristic0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.8%CalMatters: 1.0%Representativeness Heuristic0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.5%Hindsight Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.2%Overconfidence Bias0.0%This article: 1.2%Azul Dahlstrom-Eckman: 5.2%CalMatters: 6.3%Framing Effect1.2%This article: 0.0%Azul Dahlstrom-Eckman: 1.4%CalMatters: 1.0%Loss Aversion0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 0.7%Status Quo Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.2%Sunk Cost Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 3.1%CalMatters: 3.5%Optimism Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.0%CalMatters: 1.4%Pessimism Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 4.6%CalMatters: 6.4%Negativity Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.3%CalMatters: 1.7%Self-Serving Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.7%CalMatters: 0.7%Fundamental Attribution Error0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.2%Actor-Observer Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.7%In-Group Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.4%Out-Group Homogeneity Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 2.7%Halo Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.1%Horn Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.7%CalMatters: 0.9%Recency Bias0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.3%Primacy Effect0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.0%Blind-Spot Bias0.0%This article: 2.2%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.6%Ad Hominem2.2%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.2%Straw Man0.0%This article: 8.9%Azul Dahlstrom-Eckman: 2.5%CalMatters: 3.1%Appeal to Authority8.9%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.1%False Dilemma0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.4%CalMatters: 0.8%Slippery Slope0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.1%Circular Reasoning0.0%This article: 1.9%Azul Dahlstrom-Eckman: 2.5%CalMatters: 3.6%Hasty Generalization1.9%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.2%Red Herring0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.3%CalMatters: 0.7%Bandwagon0.0%This article: 13.8%Azul Dahlstrom-Eckman: 4.6%CalMatters: 5.3%Appeal to Emotion13.8%This article: 0.0%Azul Dahlstrom-Eckman: 0.5%CalMatters: 0.6%Begging the Question0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.5%CalMatters: 2.0%Post Hoc (False Cause)0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.1%Tu Quoque0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.3%Burden of Proof0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.2%Appeal to Nature0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.2%Composition/Division0.0%This article: 9.5%Azul Dahlstrom-Eckman: 4.5%CalMatters: 3.1%Anecdotal9.5%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.0%No True Scotsman0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.0%CalMatters: 1.2%Ambiguity (Equivocation)0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.2%CalMatters: 0.1%Middle Ground0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.1%Personal Incredulity0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.1%CalMatters: 0.1%Special Pleading0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.1%Genetic Fallacy0.0%This article: 0.0%Azul Dahlstrom-Eckman: 1.4%CalMatters: 0.8%Unattributed Quote0.0%This article: 0.0%Azul Dahlstrom-Eckman: 0.7%CalMatters: 0.6%Quote-first Misdirection0.0%This article: 2.2%Azul Dahlstrom-Eckman: 2.5%CalMatters: 3.1%Biased Writer Voice2.2%This article: 0.0%Azul Dahlstrom-Eckman: 1.1%CalMatters: 1.9%Indoctrination0.0%This article: 9.3%Azul Dahlstrom-Eckman: 0.4%CalMatters: 1.1%Politically Left Leaning Bias9.3%This article: 0.0%Azul Dahlstrom-Eckman: 0.0%CalMatters: 0.1%Politically Right Leaning Bias0.0%This article: 1.2%Azul Dahlstrom-Eckman: 1.0%CalMatters: 1.2%Attempt to Sell a Product or S…1.2%

734 words analyzed.

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