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In Good Company with Nicolai Tangen

John Deere CEO: Farming's Future, Autonomous Tractors and AI in the Field

38 min episode · 2 min read
·
John Deere Ceo

Episode

38 min

Read time

2 min

Topics

Productivity, Health & Wellness, Fundraising & VC

AI-Generated Summary

Key Takeaways

  • Precision Spraying ROI: John Deere's computer vision system distinguishes individual weeds from healthy crops and targets herbicide application exclusively to weeds, reducing overall herbicide use by 60%. Companies seeking to justify AI adoption should lead with direct cost savings to the customer's income statement, as demonstrated value drives technology uptake — John Deere saw adoption rates double year-over-year.
  • Autonomous Farming Timeline: John Deere's stated goal is a fully autonomous corn production system in the US by 2030, covering tillage, planting, spraying, and harvesting with zero operator involvement. The underlying technology stack is portable across tractors, combines, and sprayers, meaning breakthroughs in one machine accelerate deployment across the entire equipment lineup simultaneously.
  • SaaS Model for Hardware Companies: John Deere introduced pay-per-acre and pay-per-application pricing to extend expensive precision technology to farmers who cannot capitalize large upfront equipment costs. This model spreads technology across more acres, grows recurring revenue, and shifts the company's value proposition from selling the best machine to measurably improving the customer's profitability per acre.
  • Organizational Restructuring for Speed: To accelerate decision-making, John Deere reduced management layers from 11 to 7 and replaced a traditional product-line and regional matrix with production-system teams — corn, soybean, wheat, cotton, sugar. Each team focuses exclusively on one crop's full equipment and technology stack, creating deeper customer proximity and a competitive structure that is difficult for rivals to replicate.
  • Precision Nitrogen Application: Using computer vision during planting, John Deere equipment identifies exact seed placement and doses nitrogen only at each seed location rather than free-flowing it across the entire trench. This targeted approach cuts nitrogen fertilizer costs by approximately two-thirds while simultaneously reducing environmental runoff, demonstrating that sustainability outcomes and cost reduction can be achieved through the same technological intervention.

What It Covers

John May, CEO of John Deere, explains how the 190-year-old equipment manufacturer is transforming into a precision agriculture technology company, deploying computer vision, AI-driven autonomous tractors, and subscription-based software to reduce input costs and target full autonomous corn production in the US by 2030.

Key Questions Answered

  • Precision Spraying ROI: John Deere's computer vision system distinguishes individual weeds from healthy crops and targets herbicide application exclusively to weeds, reducing overall herbicide use by 60%. Companies seeking to justify AI adoption should lead with direct cost savings to the customer's income statement, as demonstrated value drives technology uptake — John Deere saw adoption rates double year-over-year.
  • Autonomous Farming Timeline: John Deere's stated goal is a fully autonomous corn production system in the US by 2030, covering tillage, planting, spraying, and harvesting with zero operator involvement. The underlying technology stack is portable across tractors, combines, and sprayers, meaning breakthroughs in one machine accelerate deployment across the entire equipment lineup simultaneously.
  • SaaS Model for Hardware Companies: John Deere introduced pay-per-acre and pay-per-application pricing to extend expensive precision technology to farmers who cannot capitalize large upfront equipment costs. This model spreads technology across more acres, grows recurring revenue, and shifts the company's value proposition from selling the best machine to measurably improving the customer's profitability per acre.
  • Organizational Restructuring for Speed: To accelerate decision-making, John Deere reduced management layers from 11 to 7 and replaced a traditional product-line and regional matrix with production-system teams — corn, soybean, wheat, cotton, sugar. Each team focuses exclusively on one crop's full equipment and technology stack, creating deeper customer proximity and a competitive structure that is difficult for rivals to replicate.
  • Precision Nitrogen Application: Using computer vision during planting, John Deere equipment identifies exact seed placement and doses nitrogen only at each seed location rather than free-flowing it across the entire trench. This targeted approach cuts nitrogen fertilizer costs by approximately two-thirds while simultaneously reducing environmental runoff, demonstrating that sustainability outcomes and cost reduction can be achieved through the same technological intervention.

Notable Moment

May, a working farmer himself, describes how the quality of hay bales he produces is limited entirely by his own experience and judgment. He uses this personal example to argue that removing farmers from the cab and redirecting their expertise toward planning and monitoring multiple autonomous machines unlocks significantly higher value from their knowledge.

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Episode Transcript

I have a farm myself and I bale hay. And the quality of the bale that I can produce is only as good as my experience set. So, you know, when I think the hay is at a point where it can be harvested, I get in the moco, I cut it, and then I sit in the tractor and I'm baling it. I'm listening for things. I'm looking at the computer, but the end product is going to be as only as good as I am. Hi, everybody. I'm Nikolat Tangeen, the CEO of the Norwegian sovereign wealth fund. And today, I'm joined by John May, the CEO of John Deere. Now, when you think of John Deere, you may think of green tractors. And indeed, there is a green tractor standing behind John here on the screen. But the company has become one of the most advanced technology companies in farming. Tractors that dry themselves, AI that tells a weed from a crop. And John is the man behind the shift. He spent almost his entire career at Diere and now he is trying to turn the maker of farm machines into a real technology company. John, warm welcome. Thank you. It's great to be here and thanks for including me on your podcast. Fantastic. Now John, you became CEO in 2019 and then very quickly after that you launched what you called smart industrial strategy. What is that? Well, really what we wanted to do is get more focused on the customer itself and the jobs that our customer do. You know, in the past, what we would do is focus on building the best planter. And we wanted to shift to helping our customers plant better than they ever had in the past by leveraging technology. So we realigned the customer around production systems and then really invested heavily in technology that would make our customers more productive, more profitable and more sustainable. And how is it different from how DARE used to run? You know, in the past we didn't focus on necessarily the biggest challenges that our customer had. So for example, when you're planting, your biggest expense is actually the seed, the cost of the seed. So the importance of getting seed placed exactly in the proper spot at the proper depth ensures the highest yield and helps our customers manage their costs. So we really focused on understanding what are our customers biggest pain points and what's the biggest cost to them and how can we help them reduce their costs. So you say that the goal is to help customers to do more with less. You rather help the farmer plant better than than to sell the best planter. Why? Why is that important? Yeah, let me give you an example and I'll talk about spraying. So everywhere around the world after the crop comes up and it's it's it's standing crop, one of the biggest challenges is manning, managing weeds, …

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