Daniel Gebler, CTO at Picnic Technologies, talks to The Ortus Club about the evolution of the online grocery model from proof of demand to robotised fulfilment. Daniel argues that the next phase of AI disruption isn’t digital, but physical, utilising computer vision and motion control to manage thousands of robots per warehouse. He emphasises that even in a tech-first organisation, the most successful leaders are those who seek high-level peer dialogue to unlearn old skills and refine the human “taste” required to hit the customer’s nerve.
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Executive Summary: Key Takeaways
- The Three Steps of Scale: Growth moves from proving demand through free delivery to geographic expansion and, finally, to the end-to-end profitability of an autonomous supply chain.
- Less is More: Bloated products fail; success comes from focusing on the essence of the product and well-crafted features rather than a high volume of functionalities.
- Physical vs. Digital AI: The real AI step change is happening in the physical world, where AI vision and motion control increase the accuracy and safety of warehouse robotics.
- The Hybrid Warehouse: Humans remain superior at fine motor skills and sensory quality control (e.g., identifying a bruised cucumber), while robots handle the boring, heavy lifting.
- “You Build It, You Run It, You Love It”: True engineering ownership requires developers to be responsible for the deployment and operational health of their own software.
Since its launch in 2015, Picnic Technologies has transformed from a Dutch startup into a global benchmark for the online grocery sector. For Daniel Gebler, the role of a CTO is simple yet profound: creating customer happiness through technology. By reinventing the delivery proposition, removing delivery fees and waiting times, Picnic broke decades-old physical shopping habits. Today, Daniel oversees a complex ecosystem where farm-to-fork automation meets human-centric quality control. He believes that as AI-assisted coding drives the cost of building software toward zero, the ultimate competitive advantage becomes “taste,” the ability to know exactly what a customer needs before they do.
How do you break a 20-year shopping habit?
Daniel explains the psychological and logistical hurdles of moving busy families from physical stores to a digital app.
“If our core customer base is busy families, the parents have typically shopped for fifteen or twenty years with a physical supermarket. To break that behaviour, you must offer something significantly new. The first step was proving demand by saying: customers don’t need to pay for deliveries, and they don’t have to wait. Once you transform that behaviour to online, the challenge becomes keeping them happy through incremental improvements. Eventually, you move toward a robotised supply chain where a large part of the journey from farm to fork is automated. It is about proving that digital convenience can be more reliable than the physical ritual.”
Why is 98% completeness a failure in the grocery sector?
Addressing a common industry misunderstanding, Daniel highlights the mathematical challenge of order accuracy.
“People assume it is all about shopping and delivering, but the real challenge is order completeness. If you deliver thirty items, and each has a 98% completeness rate from the supplier, you end up with less than 20% completeness for the entire delivery. No customer would accept that. One out of three deliveries being incomplete is not good enough. We have built a supplier ecosystem that is so reliable it matches the extremely high expectations of our customers. They want products that are always fresh, on time, and of the best possible quality, and that requires 100% precision across a massive item count.”
Where is the real AI disruption actually happening?
While LLMs are changing software engineering, Daniel identifies a 100x bigger impact in the physical world of warehousing.
“The LLM disruption affects everybody, but the real disruption, easily ten to a hundred times bigger than the digital one, is the physical disruption. In our warehouses, we typically have between one and two thousand robots. Applying AI for vision, control, and motion has a massive impact on accuracy, speed, and safety. This is the step change the industry has been waiting for. It allows robots to be orders of magnitude better at autonomous behaviour. However, we aren’t looking for a purely robotic warehouse. A human can still identify a ripening avocado or a bruised cucumber better than any AI vision system can today.”
Why should every engineer spend time picking orders?
Daniel discusses his leadership philosophy of deep operational immersion and industry benchmarking.
“The most important principle we apply is: ‘You build it, you run it, you love it.’ Every engineer is responsible for the deployment and monitoring of their own functionality. Furthermore, we expect every engineer to understand the operational environment. They regularly do fulfilment tasks, such as order picking in a warehouse or last-mile delivery. You cannot innovate if you don’t know the pain points of the person using the software. We also encourage everyone to figure out what the best picking stations in the world are doing. Bringing that benchmark into our own labs is how we stay better than the global standard.”
What is the “Taste” requirement for the next phase of AI?
As coding becomes commoditised, Daniel argues that leadership instincts and “unlearning” are the new priorities.
“If building software goes close to zero because of AI-assisted coding, the difference for a successful product is if you have fantastic ‘taste’ and can really hit the nerve of the customer. We also look for the ability to reinvent yourself. You need to be able to unlearn your skills. What robotic engineers learned over the last ten years will not bring them through the next ten. You need to distil the essence from the noise. Leaders must ask themselves: ‘Am I reinventing myself faster than my team?’ We are in a unique situation where everything is changing so fast that we need to be very directive in guiding what our teams should learn.”
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Across Daniel’s insights on autonomous supply chains, the unlearning mandate, and the value of human taste, one pattern is clear: these challenges aren’t solved in isolation. They require a peer-level perspective and the kind of high-trust dialogue that transcends the warehouse walls.
His vision of the “Leader as Role Model” reflects a broader reality: today’s technology and logistics heads cannot rely on old engineering skills alone to lead a physical AI revolution. The most effective executives, especially those managing the hybrid intersection of robotics and human talent, actively seek out peer dialogue as a strategic necessity to lift the tide for everybody.
At The Ortus Club, we host curated executive roundtables that bring together senior leaders facing these exact challenges. Step away from the noise and engage in the kind of open, high-value conversations that help you reinvent your leadership for the next ten years.
Frequently Asked Questions
Q: What is an Autonomous Supply Chain?
A: It is a system where the flow of goods from the supplier to the warehouse to the customer is managed by automated systems and robotics with minimal manual intervention.
Q: Why is “Human Taste” still relevant in a robotic warehouse?
A: Because humans possess superior fine motor skills and sensory nuances. A robot can lift heavy crates, but a human can better judge the freshness, ripeness, and quality of fresh produce like avocados or cucumbers.
Q: What does “You Build It, You Run It, You Love It” mean?
A: It is a DevOps philosophy where the engineers who write the code are also responsible for its deployment, maintenance, and long-term performance in the real-world operational environment.
Q: How is AI disrupting the physical side of grocery?
A: By improving the computer vision and motion control of warehouse robots, allowing them to navigate complex spaces, pick items with higher accuracy, and operate more safely alongside human workers.
Q: Why is unlearning a critical skill for 2026?
A: Because the pace of technological change (especially in AI and robotics) is so high that old methodologies and technical skills quickly become obsolete. Leaders must let go of outdated habits to make room for new, more efficient frameworks.
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