Welcome to the June 22, 2025 edition of SFTW. Today's edition will cover two topics.
- Most of LLM implementations today are chatbots, which are typically passive in nature. What we need is active experiences, which anticipate our needs, and know when to interject, just like Donna Paulsen on Suits.
- Some quick but important lessons from AgVenture Alliance's Building Bridges series.
Programming Notes:
- SFTW Convos will return on Wednesday with a conversation with Christine Gould, founder of GIGA, and previously founder of the international community Thought for Food. I am expanding the type of SFTW Convo topics by talking about agriculture policy and regulation with Emma Kovak (Breakthrough Institute), genetics & topics related to higher education with Dr. Channa Prakash (Dean of Tuskegee University), genetic breeding with Dr. Brad Zamft (Heritage Agriculture), agriculture economics with Prof. Terry Griffin (Kansas State University), and AgTech experimentation and adoption with wheat farmer Andrew Nelson.
- The second part of the GenAI in Agriculture white paper is available for free to all members
Scaling Regen Ag Starts with Better Data
Farmers Edge is building the digital backbone for regenerative agriculture—designed to simplify Scope 3 reporting and deliver credible results, fast. With Managed Technology Services built for agribusiness, Farmers Edge connects real-time field data to your sustainability goals across every acre and every grower in your network. From in-season tracking to end-of-year proof, Farmers Edge makes regen ag measurable and manageable. If you're serious about scaling impact and reporting, this is the infrastructure that makes it possible.
We need Donnas, not Amelia Bedelias
Not a day goes by without some company introducing a ChatBot agent on top of their existing data. For example, Bushel recently launched an AI assistant (Buddy) for their CRM system. It helps agribusiness teams ask Buddy questions in a natural language like “What customers haven’t been contacted in the last week?” or “How many customers have past due invoices?”
This is good use of the existing technology and makes agribusiness team members more efficient by making access to information more easily accessible through a natural human language interface.
Today, within and outside of food and ag, most LLM based applications are bolt-ons to existing data, where teams have spent time to get the LLM to ingest, and synthesize proprietary datasets combined with public data sets to deliver a different user experience to users.
None of these interfaces have fundamentally changed how we do business or even have made big changes to workflows or created new workflows which help us unlock value in new and different ways.
This is expected, as we are in the early phases of large language model adoption and understanding for business use cases, even though ChatGPT is the fastest adopted product in the history of the world from a consumer side.
The CPO of Microsoft, Aparna Chennapragada, wrote an interesting LinkedIn post titled, “On Horseless Carriages and Horses with Steering Wheels”
We are clearly in a tweener phase, with this split-screen experience where the UI hasn’t fully committed to the model, and the user hasn’t either.
And this is not a model issue.
What we need are interfaces fully native to AI. where the model is in the loop from the start, not added after the fact. That means rethinking control, context, and interaction from the ground up.
Until then, I suspect most users will keep reaching for the reins.
She also says,
ChatGPT knows a lot about your work, remembers your preferences, and could easily check in on things you care about. But it just sort of sits there, waiting, like a super polite guest who won't speak until spoken to.
I believe this is one of the most interesting design challenges right now, and it is not a model issue. The current LLMs are like a shy teenager who is hesitant to make the first move.
The AIs we have are incredibly capable and they could proactively help us, but they are not designed for it. The human has to make the first move.
We need Donnas, not Amelia Bedelias
I have to admit I am a big fan of the show “Suits.” There are many interesting characters in the show, but I am particularly fond of Donna, played by Sarah Raferty.
Donna is one of the best executive assistants in the world. She anticipates Harvey’s needs, is a strategic thinker, has exceptional memory, is a problem solver, has strong communication skills, shows loyalty and discretion, goes the extra mile, is funny, and most importantly knows when to interject with Harvey.
I used to love reading the Amelia Bedelia books (books from the 1960s) to my daughters when they were younger. Amelia Bedelia is a series of stories from the 1960s.
The stories follow Amelia Bedelia, a maid who repeatedly misunderstands various commands of her employer by taking figures of speech and various terminology literally, causing her to perform incorrect actions with a comical effect.
Imagine if the model knows the history of your interaction with a particular grower. This is feasible today, though it might be scary due to privacy and control of data issues.
But imagine if the model comes back and says,
Hey remember the fungicide strip trial you did with the grower on their Back 40? I have received new data, which changes the main conclusion of the trial completely.
or
Hey, remember last year, the grower had told you that they do not like to work with this product in your phone conversation with them? Your current prescription includes this product.
You might say,”is this just more notifications coming from my device or computer? I am already tired of all the pings from Instagram, and Tiktok.”
But this is not the case of a simple notification, but it is space and time of shared awareness between you and the AI. It is a case of the AI which notices something, understands the context, and smartly decides when to say something, because what it has noticed is going to be material to you.
The challenge is not purely technical in nature and so there is room for many other types of companies to solve this problem, rather than thinking that the big boys of tech can only solve it.
It is interesting how we have a lot of talk about agentic workflows, but ironically most of the agents today lack agency.
The challenge is to understand what matters to the human user “in the moment”. This requires deeply understanding how to build trust, and how to model relevance over time.
At least for many of the applications, what we want the agents to do is to behave like super-start executive assistants. The best EAs don’t just respond to what an exec asks for. They are very good at anticipating, filtering, and knowing when to interject.
This is not purely a technology problem but it is a human centered design, behavioral, social, and psychological problem.
Tools won't just need better interfaces, they'll need better instincts.
The good thing is that nobody knows their growers better than people who work with them closely and have trusted relationships. It is the co-op agronomist, it is the equipment dealers, it is the seed salesperson. Every industry, including agriculture has access to this innate knowledge of their customer needs. Even if the agriculture industry will not be developing many of these AI models, they have the know-how, the data, and most importantly the emotional connection and relationships to make these tools much more powerful than just being a chatbot.
We need our LLMs to be like Donna from Suits, not someone who only responds to queries like Amelia Bedelia.
What is the color of your revenue?
When I was at Mineral (an Alphabet company, which no longer exists), during our regular board meetings, we would present the state of the business and then also provide details on the financial performance of the company.
Revenue (or inflows) had to be categorized in multiple ways, but at a high level we would categorize them into two different categories.
The first was dollars coming in due to a customer finding value in the solution we had built for them and they plan to use it as long as they could get value from it. This was real product revenue, and it was shown in dark green.
The second was other kinds of inflows for R&D work, doing paid pilots, etc. At Alphabet, we also had to deal with a unique problem where people wanted to work with us because we were part of Alphabet. These inflows were to be shown in light green.
The board of directors for Mineral were quite clear. They wanted to see more of the dark green inflows, and as little of light green inflows as possible, unless it made sense in the right context.
Focusing on the right type of inflows mattered, as it aligned with the objective to build a large and sustainable business in the future. Light green inflows are okay if you are a consulting or purely a services company, because you are in a different business than a product company.
Sample inflows for an imaginary company
The discussion at the team level was often about building the right products and solutions which would help us grow the dark green inflows and taper down the light green workflows over a period of time.
To be clear, there was a lot more detail and categorization behind both the light green and dark green revenues, but the point remains that the dark green mattered more than the light green.
The bottomline is all inflows and all revenues are not created equal.
The quality of revenue matters.
This was an important point (among many others) which was made by the Building Bridges meeting organized by Ag Ventures Alliance and Tall Grass Ventures. It featured perspectives from Tanmay Bhargava (Corporate VC) at Telus Ventures, and Wilson Action (Managing Partner) at Tall Grass Ventures.
These series of "Building Bridges: Ask Me Anything" webinars will bring together portfolio companies from select venture backed agtech firms for open dialogue, knowledge sharing, and collaborative problem-solving. Participants will have the chance to ask questions, share their experiences, and learn from industry experts and fellow innovators.
I had the opportunity to attend this event. I will not go into the details of the stage of investment and check sizes for Telus Ventures and Tall Grass Ventures, but instead will focus on highlighting some points. I hope these points will be relevant for not only first time investors but also for folks involved in the industry to get a small peek behind how decisions are made around VC investments.
Milestones are where the money is
You will often hear startup founders say they have 18 months of runway or 24 months of runway. All it means is that at their current or expected burn rate, they have enough money to keep the company running for 18 to 24 months.
Wilson Acton made it very clear that the right way for entrepreneurs to think about this is through milestones. What are some of the next steps or milestones, which will show continued de-risking of the business, and prove out the main hypothesis for the business. An entrepreneur should start with a set of milestones, and then work backwards to say what kind of resources and time do they need to hit those milestones.
The right framing is not “What can I do in 18 months because VCs are typically providing enough funding to last 18 months?”
The right framing is to start with the milestones and then negotiate and go back and forth on the timelines and the resources needed. You might change or adjust your milestones to fit certain timelines, but the starting point should be those key milestones which show forward business progress. The starting point should not be timelines.
Granularity of customer profile
Another key point made by both Wilson and Tanmay was around how clearly and granularly does the startup understand the customer who is using their product and ideally paying for it. Product market fit does not just mean you have a set of customers who find your product valuable and are willing to pay for it.
As an entrepreneur, the more granular you can get with the customer profile with whom your product is resonating, the better you are in giving confidence to your investors that you really understand the problem space, and have a keen sense of how your product solves the problem in a unique and differentiated manner.
The granularity of the customer profile has many benefits. It sharpens product market fit, helps focus limited resources, improves messaging & positioning, guides feature prioritization and road mapping, enables precise pricing and monetization, boosts retention and LTVs, simplifies team alignment, accelerates learning and most importantly sets the stage for scalable segmentation in the future.
Repeatability and quality of revenue
Another important point made by Wilson and Tanmay was around repeatability and quality of revenue. Are customers who signed up coming back? Are they coming for the same or are they coming back for more? Does the company have a good sense for why they are coming back?
Both the VCs were quite explicit that they are also very interested in understanding and talking with customers who churn and are no longer using the company’s products. Startups need to not only know how and why they win, but also how and why they lose customers.
Revenue from repeat customers is more valuable than one-time customers. Revenue from your ideal customer profile (ICP) is more valuable in the early stages of the startup, compared to revenue from non-ideal customer profiles.
Both the VCs advised startups on the call to focus on repeatability and really understand what drives it and what kills it.
Go smooth and slow on repeatability. Go fast on growth.
The session last week was focused on software startups. There is another session being organized by AgVentures Alliance which will be focused on “Investing in Hardware”. If your schedule permits, you should attend it.
Other noteworthy articles
Co-bank released a white paper around how AI is empowering agriculture retailers.
Companies in agriculture are using artificial intelligence in their back offices, front offices and within agronomy and supply chain operation divisions to achieve predictive modeling and operational efficiencies.
This paper is worth a skim if you are an agriculture retailer. If you want a deeper dive, you should refer to the white papers from Metal Dog Labs.
Distributed approach to combat herbicide resistant weeds
Researchers from MSU, Arkansas, Mississippi State and Missouri will develop standardized greenhouse diagnostic protocols for resistance detection. Position MSU as a northern diagnostic hub for species such as waterhemp, ragweeds and marestail, Mississippi State as a southern hub for grasses, and Arkansas for Palmer amaranth, possibly the most damaging weed for U.S. soybeans. These hubs will coordinate regularly, sharing reference seed and novel cases of resistance for more intense study.
Kubota increases total dealerships while large players show a drop. Is the future of farming equipment smaller? (SFTW article)
Reactive is fast but blunt. Proactive is slower but smart (Mikalya Mooney) provides some clear advice on how working on proactive solutions might be better for startups than reactive approaches.
RTFM is dead. Long live RTFM from LinkedIn provides a tongue in cheek look at how the “users” of user manuals in the future will be LLMs and not human beings.
Matthew Pryor The distribution mirage by Matthew Pryor “Distribution feels like differentiation, but it's actually the opposite. When you're building to get acquired, you want to offer something the buyer can't easily replicate. Distribution isn't that thing—it's their core franchise.” Instead, he asks startups to focus on technical risk removal, clear product-market fit, and concentrated evidence.
Micropep and Corteva announced their partnership. This bolsters Matthew’s point above.
Biotech startup Micropep technologies just announced a multi-year collaboration with Corteva via the latter’s Corteva Catalyst investment arm to co-develop peptide-based biocontrol solutions. The two will conduct joint R&D and field tests using Micropep’s small linear peptides, for which Corteva Catalyst will hold exclusive rights to apply across biocontrol and biofungicide applications.
New Syngenta herbicide offers sustainable solution to global weed resistance crisis
By leveraging Syngenta’s extensive expertise in ACCase-inhibitors and state-of-the-art computer modeling, scientists precisely designed a new subclass of herbicide capable of controlling grass weeds that had evolved resistance to herbicides such as glyphosate and clethodim, while optimizing the molecule’s sustainability profile.