Human Value

Design Starts Where Perspective Changes

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When you travel with food allergies, it is natural to become focused on yourself.

Will I be able to explain my allergies?

Will they understand me?

Will I be able to eat safely?

These are important questions, but they only consider one side of the conversation.

The perspective changed for me when I stopped asking how I would navigate the situation and started asking what another person would need in order to safely help me.

If someone handed me a card in a language I barely understood, what information would I need to confidently serve them?

That single question shaped every decision that followed.

An allergy card should do more than explain a medical condition. It should make it easier for another person to safely help you.

Without clear communication, a restaurant may decide your request is too uncertain to accommodate. Even worse, someone may misunderstand your needs while genuinely trying to help. Neither outcome is good for anyone involved.

This guide is not meant to be the perfect allergy card. It is an example of what can happen when you begin designing from the perspective of the person on the serving side of the interaction.

My hope is that it helps you communicate your own needs in a way that feels clear, respectful, and practical for everyone involved.

Designing From the Serving Side

Every country has different ingredients, cooking techniques, dining customs, and hidden sources of allergens. Rather than creating a generic allergy card, I wanted to design one around the situations I was most likely to encounter while traveling in China.

1. Communicate urgency

The first thing someone should understand is that this is important.

The card immediately identifies itself as a food allergy card, asks that it be read before preparing my meal, and clearly states that I have celiac disease with zero gluten tolerance.

My goal was to remove any uncertainty about whether the information should be taken seriously.

2. Reduce uncertainty

Simply saying “gluten free” leaves room for interpretation.

Instead, I identified common ingredients that frequently contain gluten in Chinese cuisine, including soy sauce and oyster sauce, because these are practical decisions someone preparing a meal may need to make.

I also requested clean cookware, fresh oil, and a clean preparation area to help reduce the risk of cross-contamination.

The more uncertainty I could remove, the easier it becomes for someone else to confidently help.

3. Make saying “yes” easier

One of my favorite design choices is the green section.

Rather than only listing foods I cannot eat, I also included examples of ingredients that are safe, such as plain meat, seafood, eggs, vegetables, rice, garlic, ginger, salt, pepper, cooking oil, and gluten-free sauces.

Instead of creating a list of restrictions, I wanted to provide a starting point for a safe meal.

Good communication should make solutions easier to see.

4. Prioritize what matters most

Not every dietary restriction carries the same level of risk.

For me, gluten is the highest priority because of celiac disease. Dairy is a separate medical restriction.

Keeping these sections separate helps communicate that priority while still asking that dairy not be included.

5. Leave the interaction with confidence

The last two messages may be the most important part of the entire card.

“If you are unsure, please do not serve this meal.”

Those words give someone permission to choose safety over guessing.

The final sentence thanks them for their care and attention.

I wanted the interaction to end with appreciation because helping someone eat safely takes extra thought and effort.

6. Communicate in more than one way

The reverse side reinforces the same message through simple visuals.

The images provide examples of foods that commonly contain gluten or dairy while repeating the most important safety instructions.

Whether someone prefers reading text or quickly recognizing pictures, the goal is the same. Important information should be easy to understand.

Could You Make Your Own?

Absolutely.

Whenever possible, I recommend working with a trusted translation service or professional medical translator. Your health and safety are worth the additional confidence that comes from having medical information translated accurately.

I also recognize that professional translation is not always accessible or affordable.

If that is your situation, I hope this project serves as a design guide rather than simply a template.

A translation tells someone what your condition is.

A thoughtfully designed card helps them understand what to do about it.

As you design your own, think beyond the diagnosis.

Ask yourself questions like:

  • Which sauces, seasonings, or ingredients commonly contain my allergen where I am traveling?
  • What cooking methods or shared equipment could increase the risk of cross-contamination?
  • Can I include examples of foods that are safe instead of only listing restrictions?
  • How can I encourage someone to choose safety if they are uncertain?
  • Would simple images reinforce the message if there is still a language barrier?

The goal is not simply to translate your condition.

The goal is to make it easier for another human being to safely help you.

Is This Card Right for You?

This version was designed specifically for my own dietary needs:

  • Strict gluten-free due to celiac disease
  • Dairy-free due to a medical allergy

If your dietary needs are different, I hope the design process is still useful. Whether you purchase this card or create your own, my goal is the same. I want to make it easier for people to care for one another through thoughtful communication.

Important: Please review all translated medical information yourself before relying on it. Every person’s dietary needs are different, and you are responsible for deciding whether this card accurately represents your own requirements.

Download: Printable PDF containing three wallet-sized allergy cards per page.

AI Can Replace Jobs… In Theory

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Exploring the gap between AI’s “theoretical capability” and its real-world performance.

AI has quickly become one of the most discussed technologies in everyday life. It shows up in conversations with coworkers, friends, and family. You overhear people discussing it while eating out, and it fills social media feeds with predictions about how it will reshape work and society. Much of the conversation centers on whether AI will replace human jobs, but my experience using these tools has led me to see the situation somewhat differently.

In my own work with AI systems, I’ve found them to be incredibly useful in certain areas. They excel at brainstorming ideas, expanding knowledge, summarizing large amounts of information, and organizing complex topics into more understandable formats. Used well, these capabilities can save a significant amount of time and allow people to process information much more efficiently. In that sense, AI clearly has the ability to expand human productivity and make many tasks easier.

At the same time, regular use of these tools also makes their limitations visible. One example that stands out is computation. AI systems may correctly identify the formula needed to solve a problem but still produce an incorrect result when executing the math itself. In situations where precision matters, that creates a clear need for verification. While highly specialized systems designed only for a narrow type of calculation could address this issue, doing so sacrifices the flexibility that makes general AI tools appealing in the first place.

Another limitation appears in the way AI approaches problem solving. AI often presents the first reasonable solution it generates and then continues expanding on that approach. Humans, by contrast, frequently question whether an entirely different method might produce a better result. Much of human progress comes from this instinct to challenge existing systems and reinvent processes, even when current solutions appear to work well. AI systems are trained on existing knowledge and historical patterns, but they do not independently generate the motivation to push beyond those patterns unless they are explicitly guided to do so.

Recently I encountered a visual chart circulating online that attempts to map which occupational categories AI could theoretically cover. The chart separates “theoretical capability” from “observed usage,” suggesting that AI already has the potential to perform many tasks in fields such as management, business and finance, computer and mathematical work, architecture and engineering, legal services, arts and media, and office administration. In practice, the observed use of AI appears to mirror these categories, but at a smaller scale.

Discussions surrounding the chart often emphasize that AI will not necessarily replace entire jobs, but instead automate specific tasks within them. Some commentators suggest that companies able to automate 20 to 40 percent of knowledge work will significantly outperform organizations that do not adopt these tools. Others argue that the real shift will occur as workers learn how to direct, audit, and integrate AI systems into existing workflows.

There is likely truth in parts of this perspective. AI clearly has the ability to assist with many tasks inside existing roles. However, interpreting this as evidence that AI will broadly replace workers oversimplifies how organizations and systems actually evolve.

From my perspective, the chart itself also overlooks something important. If anything, AI’s potential usefulness may be more broadly distributed across occupational categories than the model suggests. Fields such as management, architecture and engineering, life and social sciences, legal work, education, personal care, and office administration all share characteristics that make AI particularly helpful as a supporting tool. These areas often involve large quantities of information, historical knowledge, and complex interpretation, which are environments where AI’s ability to synthesize information can provide real advantages.

Office and administrative work provides a good example. Many small repetitive tasks within these roles can be automated, allowing workflows to move faster and reducing time spent on routine work. At the same time, automation tends to push human involvement toward the more complicated or unusual situations that fall outside predictable patterns. Rather than removing people from the process entirely, AI often shifts their role toward solving problems that require judgment and creativity.

Legal work presents a similar dynamic. The field contains enormous volumes of information, and AI tools can dramatically accelerate research, document review, and information retrieval. Yet small nuances in wording can determine the outcome of a case, meaning human interpretation remains essential. Education follows a related pattern as well. Many foundational tasks help people build expertise over time, and removing too many of these steps could ultimately weaken how professionals develop their skills.

One of the most important differences between humans and AI systems comes from experience. People move between organizations, industries, and social environments. Along the way they encounter ideas that have nothing to do with their immediate work but later become the source of an important breakthrough. Sometimes the insight that saves a company or creates an entirely new opportunity comes from something learned in a completely unrelated context.

AI systems, by comparison, often operate within more confined boundaries. A system trained heavily on a single organization’s data may become extremely efficient at performing tasks within that environment, but it also risks becoming limited by it. Humans introduce the unexpected connections that allow systems to adapt and evolve over time.

Because of this, I see AI not as a replacement for human workers, but as a powerful tool that works best when paired with human judgment. AI can process information, summarize knowledge, and automate repetitive digital tasks at remarkable speed. Humans contribute creativity, curiosity, and the ability to question existing approaches. Together, those capabilities can strengthen organizations far more than either could alone.

It is possible that some companies will attempt to remove large numbers of roles in pursuit of automation. If that happens, they may discover that systems relying too heavily on automation risk becoming stagnant over time. Growth often depends on experimentation, unexpected insights, and new ways of thinking that emerge from human interaction.

AI will almost certainly reshape many aspects of work, but the most meaningful changes are likely to come from how humans learn to use these systems as tools rather than substitutes. Machines can accelerate processes, but the curiosity and imagination that drive progress still belong to people.

by Mads