AI Needs Its Heinz Ketchup Moment
What does tomato sauce have to do with AI? Governance leads to the creation of a better product.
At the turn of the 20th century, the United States had no comprehensive federal food-and-drug law and no Food and Drug Administration (FDA) as we know it today. Back then, many ketchup brands were made from tomato scraps stretched with pumpkin rinds, apple pomace or cornstarch, dyed red and kept on the shelf with chemical preservatives. These additives left shoppers without a reliable way to distinguish a good product from a harmful or mediocre one.
Enter Henry J. Heinz.
An American entrepreneur and food manufacturer, Heinz founded the company best known today for its ketchup. When he entered the then-chaotic food market in 1869, he did so with a simple belief: a better product required transparency. He would then spend the next several decades proving that high standards and commercial success could coexist, actively shaping the rules of his industry long before the law required it.
His efforts offer a useful lesson for the AI industry today.
How Heinz Helped Write the Rules
Heinz's first commercial food product was prepared horseradish, which competitors often sold in green and brown bottles that hid fillers like turnips and wood fibers. Unlike his rivals, Heinz used clear glass, despite the higher production costs, so that his customers could see exactly what they were buying. He carried that practice into the manufacturing of his ketchup.
Seeing inside the ketchup bottle wasn't the only safety concern surrounding these products. The ingredient lists also faced scrutiny when government chemist Harvey Wiley raised questions about chemical preservatives. To address the concerns Wiley raised, Heinz decided to create a preservative-free ketchup. His own general manager warned him that doing so would not only be expensive, but that the product would spoil easily. Heinz succeeded by creating a recipe made from ripe tomatoes and nearly twice the sugar and vinegar of other brands. This established the type of thick ketchup Americans still enjoy today.
Having created a product that met his own visual and chemical transparency standards, Heinz went on to influence how his industry would be regulated. His company lobbied alongside food-safety advocates in 1905, and the Pure Food and Drugs Act passed the following year. This act laid the groundwork for the modern FDA. But Heinz's advocacy was not purely selfless. He believed that transparency would lead to commercial success. Regulation accomplished two things that worked in his favor: first, it protected consumers from potentially harmful products, and second, it empowered Heinz to become a trusted brand and the largest global ketchup manufacturer.
AI's Ketchup Moment
Like ketchup at the turn of the 20th century, AI is facing its own demands for regulation. Standards are emerging globally that will shape and guide the field for years to come. Frameworks and laws like the EU AI Act exist, but agreement on how the industry should handle oversight remains largely open. Much of today's regulatory attention is targeted toward the companies that build AI models. Meanwhile, the companies that use those models inside the products they sell to consumers often escape a similar level of scrutiny. Yet these are the companies deciding how AI technology actually shows up in everyday life.
The value of Heinz's glass bottles wasn't in showcasing the tomatoes his company grew and processed, but in revealing the finished product his customers consumed. AI product governance needs to address both the underlying models and the impact the finished products have on consumers.
We need to govern both the tomatoes and the ketchup.
The industry needs a champion to demonstrate that governance and oversight are not anchors that suppress innovation, but rather accelerators that both protect the public and drive commercial success. And perhaps, as with Heinz, that voice has to come from inside the industry itself.
Those inside the industry can follow the same three steps Heinz took:
Make the product visible. AI is harder to inspect than a bottle of horseradish, and a model card that describes the underlying model only covers the tomatoes. Product makers can show people what the product uses AI for, how it was tested in that setting and where its limits are. That helps consumers of AI judge what they're purchasing, just as Heinz's clear glass bottles did for his customers.
Show that it works. Heinz proved that a preservative-free ketchup could be both manufactured and commercially successful. Product makers must challenge themselves to demonstrate that well-governed AI systems can perform better by producing fewer failures and more consistent results. That is how they will earn the trust of businesses and consumers alike.
Build the standard together. Heinz didn't operate in a vacuum but worked alongside scientists, advocates and policymakers. AI standards will need that same diversity of perspectives, with developers, organizations that use AI, researchers and regulators all working toward a shared goal.
AI's Moment
The law Heinz supported did more than make better ketchup. It set national standards for food and medicine, and those protections have long outlasted the original glass bottles. AI has a similar opportunity not only to standardize how its own systems are built, distributed and consumed, but also to help shape how other emerging technologies, like quantum computing, are regulated as they enter the market. Companies are already carrying governance practices across technologies, as IBM does by governing its AI and quantum work under a single responsible technology framework.
But every story needs a hero, and the AI industry is still waiting for its Heinz moment. When that moment arrives, perhaps it will convince the industry of what Heinz knew over a century ago: governance isn't the enemy of innovation, but the recipe that makes innovation last.
Govern both the tomatoes and the ketchup.
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About the Author
Sophia Banton is an AI leader working at the intersection of AI strategy, communication, and human impact. With a background in bioinformatics, public health, and data science, she brings a grounded, cross-disciplinary perspective to the adoption of emerging technologies.
Beyond technical applications, she explores GenAI’s creative potential through storytelling and short-form video, using experimentation to understand how generative models are reshaping narrative, communication, and visual expression.
