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When I drive from my southern Florida home to Orlando, Port Canaveral, or even Tampa, I’ll happily stop at a 7-Eleven for gas, something to drink, and maybe even a bathroom break, but never a meal.

I’ll buy packaged snacks from the chain, but I’ll opt for Wawa or even make a secondary stop at Starbucks if I want something more substantial to eat.

That’s a problem 7-Eleven has been addressing in recent years as it attempts to change how consumers perceive its hot food lineup. It has been a slow process, with the chain adding a new sandwich line nationwide in July.

“Customers are looking for craveable, great-tasting meals that fit seamlessly into their day, and this lineup delivers exactly that,” 7-Eleven Senior Vice President Brandon Brown said in a press release.

Now, the chain, which has been famous for its roller dogs, is taking a major step to fix a staple of its menu. 7-Eleven will roll out a new pizza crust nationwide beginning in September.

7-Eleven rolls out a new pizza crust

While 7-Eleven may be best-known for its hot dogs, taquitos, and other roller-based products, pizza has been a menu staple for decades. Now, the chain has decided to revamp its pizza crust across all of its U.S. locations.

“The new crust has a crispy, bubbly exterior and a light, airy interior, and will be available on several pizza varieties, including cheese, pepperoni and Extreme Meat, the spokesperson said. It will roll out Sept. 3 at participating 7-Eleven, Speedway and Stripes convenience stores,” C-Store Dive reported.

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The convenience store giant called the new crust “part of 7-Eleven’s ongoing commitment to enhancing its fresh food offerings and delivering an even better pizza experience for customers” in a statement to C-Store Dive.

“As pizza continues to be a key part of 7-Eleven’s fresh food lineup, the brand is always looking for opportunities to elevate quality, flavor and value,” 7-Eleven’s spokesperson said.

Food is considerably more important to convenience-store profitability than its share of sales would suggest.Shutterstock

Food sales drive convenience store profits

7-Eleven has been working to improve its menu because higher-quality food doesn’t just drive repeat visits. Food sales also add meaningfully to the bottom line.

Foodservice accounted for 28.0% of in-store sales in 2025, according to the NACS State of the Industry Report of 2025 Data. The impact of foodservice on profitability is even greater, accounting for 38.9% of in-store gross profit dollars in 2025.

“What I want you to take away is that every single customer that visits your c-store and goes up to the pump — that is a foodservice opportunity for you,” said Caitlin Root, executive vice president, customer experience at Datassential, on stage during the 2026 NACS State of the Industry Summit.

Foodservice includes prepared food, commissary, and hot, cold, and frozen dispensed beverages.

If all five foodservice categories were ranked within the top 10 merchandise categories, all except for commissary would fall into the total in-store top 10 in terms of sales, according to NACS Magazine.

“Foodservice delivers two of the three need states, ‘I’m thirsty, I’m hungry,’” Andrew Baill, Wawa’s senior category manager of fresh beverages, told the magazine. (The third need state is fueling/charging.)

Consumers want hot food

Consumers are also consistently opting for prepared over packaged food, especially when it comes to impulse purchases.

“Nearly four out of five consumers are already purchasing prepared food at c-stores at least occasionally. Packaged goods make up 47% of impulse purchases at c-stores, down 6 points compared to three years ago, while prepared food purchases (38% of impulse purchases) are up 9 points,” according to research from Datassential shared with NACS Magazine.

“Consumers are going into your store and instead of buying a bag of chips, they’re leaving your store with a sandwich, a specialty beverage or a slice of pizza,” said Root. “Top performing operators are recognizing the incredible profit margins that can be obtained from foodservice.”

7-Eleven has certainly been trying to embrace that trend, but it has been a slow evolution.

“At 7-Eleven, our commitment to customers goes beyond just convenience — we’re focused on building upon the innovation and quality that keeps our customers coming back every single day,” the chain’s former Senior Vice President Dave Strachan said in a 2024 press release.

“We are hyper-focused on curating our fresh food options to give our customers what they crave at any time of day.”

Fixing its pizza, assuming consumers like the change, will be a major step on the road to changing how consumers see 7-Eleven’s menu and whether they will stop there for prepared food.

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Why we love this deal

Small spaces need storage, too. In fact, they’re the areas in a home that might need them the most. With limited room, there’s only so much space you can place your belongings, which means there’s a good chance that it can get cluttered quickly. Luckily, there are small storage cabinets that can fit into said spaces to keep you organized, and there are plenty that you can get for under $100.

The Shintenchi 2-Drawer Farmhouse Storage Cabinet is an affordable Amazon organizer at only $63. With both open and closed storage, it’s an incredible deal for the organizational potential.

Shintenchi 2-Drawer Farmhouse Storage Cabinet, $63 at Amazon

Courtesy of Amazon

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Why do shoppers love it?

Measuring 11.9 inches long by 11.9 inches wide by 34.9 inches high, this storage cabinet is just what you need to declutter a small space. At less than a foot long, it can fit into compact corners and areas, whether it’s in a bathroom, an entryway, or a hallway.

With both open and closed storage, you get the best of both worlds. Its tabletop and open shelf can be used to hold decorative items. In an entryway, you can place a small lamp there or a bowl for your keys, and in a bathroom, you can place a scented candle, your favorite lotions, or skincare. The open shelf can be great for rolled face or hand towels in a bathroom or mail in an entryway or hallway. Then, there are two drawers with cutout pulls that can hide away smaller items, like toiletries, dog-walking essentials, and more. 

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Details to know

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The Shintenchi 2-Drawer Storage Cabinet is only $63 at Amazon, and it’s a great addition to any home. It’s especially great for small spaces, as its compact footprint adds extra storage without taking up too much floor space.

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If you look at the commit histories of modern data platforms, something profound has shifted over the last two years. The friction of writing syntax has collapsed. With Cursor, Claude Code, and agentic workflows now living inside our Docker containers and IDEs, generating the first implementation of a distributed streaming pipeline or a complex API integration is no longer the central bottleneck.Agents can navigate repositories, write test coverage, inspect stack traces, and propose refactors. Describe a Kafka-to-Iceberg sink mapping in plain English, and an agent can produce a credible starting point before the engineer has opened every relevant file.That changes the question for software engineers.If the agent is becoming the primary author of local system logic, what exactly is left for the engineer to do? Are we headed toward an industry of reviewers rubber-stamping an endless stream of plausible pull requests? Or has the work shifted away from constructing logic and toward something more abstract?To answer that, it helps to borrow a lens from thermodynamics, which gives us a language for directed work, feedback, loss, and the boundaries that keep a complex system coherent.The agent as a heat engineWhen we strip away the anthropomorphic illusion of AI, what remains is a computational engine. It takes direction and turns it into action.An LLM sitting in a data center has immense capacity, but it does no useful work until it is given intent. A prompt, a business requirement, a system instruction, or a failing test gives the agent a direction. It turns that direction into code, tool calls, queries, tests, and changes to a running system.Every engine has losses. Every agent loop does, too.Anyone who has left an agent running against a difficult repository has seen this. It starts with a clear task. Then it follows a stale assumption, fixes a symptom rather than a cause, treats an old migration as current behavior, and begins to accumulate its own history. A few tool calls later, the context contains enough plausible but conflicting detail that the next step is less certain than the first.Call this operational entropy: the buildup of stale assumptions, branching context, and unresolved dependencies inside a loop that is still trying to move forward.A human interruption helps because it introduces new information. So does a failing test, a precise data contract, a deterministic tool, or an evaluation that tells the agent exactly what it got wrong. Without that signal, an agent can keep generating output while drifting further from a correct outcome.Agents clearly generate motion. The real question is whether the system around them turns that motion into useful work.The infinite monkey and the accelerating search spaceThe infinite monkey theorem gives us a useful picture of what follows: repeated attempts, finite constraints, and feedback.The theorem says that a monkey hitting keys at random for an infinite amount of time will almost surely type the complete works of Shakespeare. Modern agents are much smarter monkeys. They have compilers, tools, repositories, test suites, and feedback loops. Their work is not random — the feedback steers the next attempt — but the dynamic is familiar: propose, execute, observe, correct, and try again.In a bounded task, that loop is remarkably effective.Give an agent a known input schema, a known target schema, a small codebase, and tests that catch the relevant failures. It can inspect the code, make a change, run the tests, absorb the result, and try again. The definition of done is visible. The search space is narrow. The loop has a chance to converge.But enterprise systems rarely offer that kind of stillness. A real-time pricing engine may depend on mutable operational state, third-party APIs, late-arriving events, regional policy, and business rules that exist partly in code and partly in someone’s head. A data lakehouse can be physically consistent and semantically wrong. A pipeline can pass its tests and still produce numbers that finance does not recognize.The environment is changing while the monkey is typing.The three-body problem of enterprise logicThis is why the three-body problem is such a useful image for enterprise software.With two bodies — a planet and a star — you can predict the motion with a clean mathematical description. Add a third body and the problem becomes much harder to solve. There is no general closed-form solution, and some configurations exhibit chaotic behavior. Small changes in one place can produce very different trajectories elsewhere.Modern data platforms have the same shape. Clickstream data changes with product behavior. Operational databases mutate under customer activity. APIs impose rate limits and change versions. Schemas evolve. Security policies shift. Legacy systems carry rules that no one has written down because they have been buried in exception handling for years.Each system exerts pressure on the others. A change in one place alters the meaning or behavior of another. What begins as a local feature request starts to pull on the entire system.Consider a hypothetical: an agent is asked to add a customer_tier field to a revenue model. It finds a field called status in the operational database, maps it into the transformation, and passes the existing type and nullability tests. The code is clean. The pipeline is green. The answer is still wrong.A semantic data contract says that customer_tier is derived from trailing twelve-month spend, has an assigned business owner, and cannot be populated from account status. The contract rejects the change before it reaches the dashboard. The engineer’s contribution wasn’t the transformation — it was the boundary that made the agent’s mistake visible, specific, and recoverable.The new mandate: Designing equilibriumThe software engineer’s job is no longer to write every piece of micro-logic. Agents will increasingly do that work, often faster. The new mandate — designing equilibrium — is to create the conditions in which generated logic can be trusted.When a business requirement changes faster than an agent can absorb feedback, the engineer has to build containment fields. Strict semantic layers, immutable event logs, data contracts, idempotent APIs, and deterministic state machines are not just good platform hygiene. They reduce the number of assumptions an agent has to make at once.They turn a coupled problem into a bounded domain with clear inputs, explicit rules, and reliable feedback.Once that domain exists, the agent becomes genuinely powerful. It can write the transformation, execute the tests, repair the failures, and ship the change without needing to infer the unwritten history behind every table and service.The value of software engineering doesn’t disappear as code generation gets cheaper — it becomes more visible, which is the shift that actually matters.Autonomous systems will increasingly generate software. But the contracts, feedback loops, and boundaries that determine whether that software succeeds or spirals into chaos will still be designed by software engineers.Ananth Packkildurai is a data engineering leader, writer, and author of Data Engineering Weekly, sharing insights on modern data platforms, large-scale pipelines, and AI-driven architectures.

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