The Night Shift Job That Changed How I Build

I've always noticed that founders tend to build solutions for problems they’ve lived through. That’s why we’re seeing so many AI-powered SaaS tools for coding, sales, marketing, and customer service. Founders spot a pain point in their own workflow and then build a product to fix it 1.

But my path has been a bit different.

I grew up helping my parents run their businesses. That exposed me early to the messy, fragmented nature of the industrial and manufacturing world. These industries often get ignored by the tech crowd. They're seen as unglamorous or hard to scale. But I’ve always believed they hold a lot of untapped potential.

This past spring, between March and July, I decided to do something unusual. After getting four wrong deliveries in a row from a popular meal kit service, I wanted to know how such avoidable mistakes kept happening. These are companies that depend on tight logistics and precise execution. So I got curious.

I took a job working night shifts at a local meal kit fulfillment center. Most people around me thought I was being ridiculous. Why would I do that? Why take a hard physical job when I didn’t need to? But I took the job because I was curious. I wanted to experience it firsthand, not just read about it from the outside. I was on a mission to understand the root causes of the recurring mistakes I kept seeing in my own orders and maybe find patterns others had overlooked.

During orientation, I found out the company was losing close to a million dollars from packing errors. Wrong orders meant credits had to be issued, and the food couldn’t be reused. The training director even admitted he’d personally received multiple incorrect boxes. That shocked me.

Once I got on the floor, I saw exactly how these errors happened. It was frustrating to witness. I kept thinking, over and over, how AI could step in. Not to replace people, but to help them do their jobs better. I imagined what computer vision 2 or physical AI 3 could do to make sure every box was packed correctly.

I later found Reddit threads 4 full of customer complaints. People were frustrated. They were missing ingredients. They were getting the wrong meals. It messed up their planning and caused real stress 5. These were not isolated complaints. They reflected a systemic issue.

Around that time, I also came across a thread on r/ycombinator titled “Why are you working in a space with 0 domain expertise?” The person who posted it made a great point. They said if you don’t have experience in an industry, go get some. Even if it means going undercover. That way, you don’t just show up with software and assumptions. You build from empathy and context. That stuck with me.

Then Garry Tan, the CEO of Y Combinator, tweeted something similar. He said one of the fastest ways to gain deep expertise today is to work a non-technical job where you think LLMs or AI could make a difference. He called it “going undercover.” I smiled when I saw that tweet. I was already doing it.

So what did I learn?

I worked the third shift. My schedule was 10:00 pm to 6:30 am, Thursday through Monday. Each night started with a team huddle. We reviewed our goals: around 27,000 meal bags and 5,000 portions on the busiest days, and about 4,000 to 5,000 boxes to ship over the weekend. The operation ran like a fast-moving orchestra, with everyone playing a specific part.

There were Production Associates like me who labeled and portioned ingredients, bagged meals, and helped with shipping. Gatekeepers submitted inventory and tagged items to keep counts accurate. Warehouse Dropzone Gatekeepers coordinated product requests between the warehouse and the production floor. Supervisors managed plans and setups. FSQA teams, short for Food Safety and Quality Assurance, did checks to make sure we met product standards.

Each role was designed to prevent mistakes. Still, the errors kept happening.

The biggest challenge was communication. Supervisors sometimes didn’t know how many shipping lines to run or how many bags we needed to pack. Shifts handed off information poorly. Team Leads got mixed messages. We would inherit the dropzone or shipping line as it was, with no context. This confusion created chaos.

There were also constant mistakes in how meals were bagged. Some associates would forget an ingredient or include only one packet when the recipe needed two. A lack of clear guidance made it hard to decide whether to discard questionable items. Under pressure to meet high numbers, mistakes slipped through.

On the shipping line, the problems got worse. We packed entire boxes with proteins, bagged meals, recipe cards, and other items. Sometimes people would put the wrong protein in the box. Or forget it entirely. They might grab based on memory instead of checking the current setup. This happened because the protein shelf layout changed often. If you weren’t careful, you could mess up without realizing it.

There were also errors from the runners, the people who brought products from the warehouse to the shipping floor. I worked this role too. I saw how easy it was to mix things up. Items had similar names or packaging. Ingredient sizes were close, like 5 oz versus 7 oz. If you weren’t double-checking IDs, you could pull the wrong thing.

Tagging errors were another pain point. Gatekeepers sometimes mislabeled stacks of meal bags. Team Leads sometimes set up tables or shipping lines with the wrong meals or proteins. One week we had over 32 different protein types, each varying in size and cut. There were 8, 10, or 20 oz portions. Some were cutlets, others diced, and some were full breasts. That complexity alone made it easy to mix things up, especially when the labeling or table setup was off.

To give you a sense of what a correct box should contain, here's what I would pack if I were working the shipping line for a Southwest-Style Chicken Enchilada Bowl:

• Ice
• Recipe card
• 20 oz. diced boneless skinless chicken breasts
• 8 oz. black beans
• 7½ oz. minute rice
• 5 fl. oz. enchilada sauce
• 5 oz. corn kernels
• 3 oz. sour cream
• 3 oz. shredded cheddar cheese
• 1 oz. tortilla strips
• 4 tsp. chicken broth concentrate
• 3 tsp. fajita seasoning

Everything had to match the customer's order. But when stacks were mislabeled or someone set up the line with the wrong meal bags or proteins, associates would unknowingly grab the wrong items. Most of us trusted what was already laid out. If the mistake came from an earlier shift, it just kept going.

One thing I want to clarify. While these were common mistakes, I did not personally contribute to them. I worked carefully. My team knew me for my accuracy. Even on nights when I was tired, they could tell. I was the kind of worker who didn’t need to speak for people to notice something was off.

This also shaped how I was seen at work. Some teammates noticed I stopped wearing my glasses after the first month. One of the toughest girls on the shipping line used to joke about it. She turned out to be one of the kindest people I met. Her jokes helped break tension on stressful nights.

But not all interactions were lighthearted. One moment I’ll never forget came from Mr. Ricky.

When I told Mr. Ricky what I was building, he asked a question that stayed with me: “What will happen to our jobs?” Mr. Ricky was on my team. We worked together on the shipping line, running supplies and replenishing tables. I told him the truth. I wasn’t building something to replace people like him. In fact, every time I think of a new AI application, I ask myself a simple question first: Will this help the worker and their family?

Mr. Ricky was someone who had been given a second chance. He didn’t talk about it much, but I knew this job meant something deeper to him. And I saw how seriously he took it. People like him are the reason I want to build technology that supports people, not sidelines them.

These mistakes weren’t just numbers on a screen. They led to real consequences.

Customers lost trust. The company lost money. Workers got stressed. It created a cycle. When morale dropped, performance did too. People rushed more. Quality dipped. The environment got worse.

Culturally, the place was rough. There was a lot of fear and favoritism. Some workers were treated without dignity. Others abused the system by taking long breaks, chatting through the night, or manipulating time logs. But none of that justified the culture of intimidation. Coming from a software background, I had never experienced a workplace like that.

And yet, there were good people in that building. People doing their best. People who needed this job to survive.

This whole experience shaped how I lead today. It taught me that no matter how advanced your technology is, culture matters. Fairness matters. Respect matters. If I ever lose sight of that, I’ll have failed the people who raised me, mentored me, and believed in me.

I also began to imagine what AI could do if used thoughtfully. Not to cut costs, but to improve outcomes. Not to replace workers, but to support them.

Imagine cameras at the bagging station that check every item and alert you if something’s missing. Or sensors on the shipping line that tell you if the protein is wrong. Or dashboards for supervisors that show real-time data about what’s running and what’s not.

You could have RFID tags on ingredient bins that track movement and prevent wrong items from reaching the floor. You could use augmented reality headsets to show new associates exactly where each item goes. You could give FSQA teams tools to automate routine checks so they can focus on deeper issues.

I’m currently working on tools like these. The goal is to catch mistakes early, reduce pressure on workers, and improve consistency across shifts. These ideas aren’t just theoretical. Big companies are already exploring similar systems.

Sam’s Club, for example, has started using AI-powered exit technology that checks your cart at the door using computer vision 6. It speeds up the process and frees up staff for other work. Amazon’s VAPR system 7 lets delivery drivers scan packages visually to avoid grabbing the wrong item. These examples show what’s possible.

But none of this matters if we lose sight of people.

The reason I want to build AI for warehouses and manufacturing is not because I think they’re inefficient. It’s because I’ve worked alongside people like Mr. Ricky and the toughest girl on the line. I’ve seen what it takes to keep things running. I’ve seen what breaks when we stop caring.

I believe technology should make life better, not harder. And I believe the future of work should include people like the ones I met in that fulfillment center.

It’s not always about chasing what’s trendy. Sometimes, the most powerful ideas come from the quiet corners of the world. The ones we overlook. The ones where no one else is paying attention.

That’s where I want to build. And that’s where I believe the future is waiting.