Artificial Intelligence

The Digital Farmhand: How AI is Solving the Agricultural Labor Crisis

Wed, 13 May 2026 12:00:36 GMT

Agriculture is facing a historic labor shortage at the same time artificial intelligence (AI) is reshaping how the world operates. Some fear AI adoption will result in job loss and businesses being left behind due to rapidly evolving technology. Others say AI is the digital farmhand agriculture needs right now to handle repetitive data tasks while humans focus on high-value animal husbandry or field work.

Either way, one thing is true – AI is not going anywhere.

“Whether AI replaces jobs or not depends on how the industry chooses to use it,” says Angel Andaya, manager of digital solutions for Silver Support, a managed development center supporting operations, finance, digital solutions, information technology and automation services.

If AI is seen purely as a replacement, she says that is likely the direction it will take. But it could also become a powerful tool to help farm operations thrive despite labor challenges.

The “Why Now” of AI: Accessibility and Adoption

While AI has existed for years (think Netflix recommendations and GPS), the launch of ChatGPT marked a paradigm shift that made the technology conversational and accessible to everyone, says Tracy Soper, senior director of data excellence at Keystone Cooperative.

“At 100 million [users] in two months, ChatGPT’s growth is unheard of – nothing has grown that fast,” Soper said at the National Pedigreed Livestock Council’s annual meeting. “Why? Because it was conversational and easy to access. It was something all of us could touch and could relate to, like, ‘Oh, this is a thing. It makes my life easier.’”

In the past, technology adoption took years. Now, it happens in months, creating a sense of “AI hysteria” and a need for clear strategy, he adds.

Problem First, People Always

Rather than viewing AI as a replacement for human expertise, it should be viewed as an amplification tool, he says. The strategy is to avoid expensive shelfware by starting with specific business problems.

“It can do a lot of things, but how are we going to use it?” Soper asks. “For us at Keystone, AI is not about replacing people; it’s making people better.”

Years ago, Soper says his job was to look over all things related to information technology (IT). Today that looks like AI and automation solutions as the cooperative’s scale has grown.

“For us, it’s starting very specifically with what problem we’re trying to solve today and then asking, ‘Why can’t we solve it with what we’ve got?’” he says.

Keystone takes a four-step approach:

1. Start with the problem, not the technology.

“AI only creates value when it’s solving a real business challenge. Companies that buy a tool, hand it to IT and expect magic end up with expensive shelfware,” Soper says.

2. Data readiness before algorithms.

“Any insight is only as good as the data feeding it,” he says. “We invested significant time building a modern data foundation before ever pursuing AI.”

3. Amplify expertise – don’t replace it.

“AI is not replacing agronomists, breed managers or the people closest to the animals. It’s amplifying their experience and sharpening their decision timing,” Soper explains.

4. Your data is the competitive edge.

“The competitive gap will be built on data readiness as much as algorithms,” he says.

In the future, Keystone is working actively in predictive machine learning and generative AI, using them to improve decision timing, streamline operations and better serve the producers who depend on the cooperative.

Shorten Time-Consuming Tasks

AI helps experts ask better questions sooner, Soper says. With data flowing more freely across the value chain, he believes there is great opportunity where AI and animal data converge.

For example, computer vision for body condition scoring, lameness detection and welfare monitoring is moving from research into practice in many barns. He’s also excited about how AI-assisted genomic prediction and health monitoring are advancing across species and can help make progress more quickly.

Andaya encourages farmers to think about the daily realities on the farm. What tasks are essential, but time consuming and repetitive?

“Even small improvements in how they are managed can free up valuable time and improve decision-making on the ground,” Andaya says.

If these processes are supported through AI, she believes it will enable farmers and their employees to focus more on animal welfare, planning and improving overall farm productivity.

“In this sense, AI is less about replacement and more about giving farmers and livestock teams the space to focus on what truly matters,” she says.

4 Tips for Successful AI Implementation

Agriculture and livestock operations are full of valuable data from daily logs to finances, Andaya explains.

“What’s changing is how effectively this information can be used,” she says.

Soper says Keystone has learned four important lessons in their journey to use AI more efficiently.

1. Data quality is everything.

Start with the data you own. Then budget time for discovery and cleanup.

2. Build for the people doing the work.

The tool needs to make someone’s job easier or it won’t get used. AI should amplify good discipline.

3. Scope tight, prove value first.

Prove it works before you scale. The business has to own the problem – IT enables, but stakeholders drive adoption and define success.

4. Governance can’t wait.

Policies around approved tools, data and data protection need to exist before people experiment. Once people start using AI on their own, it’s harder to rein in.

Farmers Don’t Use AI for Answers — They Use It to Think Better

Fri, 17 Apr 2026 15:10:26 GMT

What you should know:

To use artificial intelligence in your business for a competitive advantage — not just a gimmick:

Ask better questions than most people
Combine AI with real-world experience
Execute on the answers

For Rachael Sharp, dry weather hasn’t made planting go any easier in the Lowcountry of South Carolina. And when a planter went down, the first thing she did was pull up Chat GPT.

“I pulled up the part number, and I saw that I’d actually entered in there last year. So it told me the date I changed it, and that was helpful, because I was trying to figure out why is this wearing out so quickly?” she says. “We’re in desperate need of rain, and we’re pulling in some pretty hard non-irrigated land right now. I logged that we changed the bearing again, and so next time, knock on wood, it hopefully doesn’t go out again, but if it does I can look and see I changed it twice in the last year.”

That’s just one of many examples of how Sharp is using ChatGPT to manage equipment, her time, and the farm business. She and her father, Don, are featured in an OpenAI commercial, which premiered during the Super Bowl.

And she’s in good company with other farmers in how to use the artificial intelligence platforms.

Marc Arnusch, the 2025 Top Producer of the Year, says ChatGPT is the most used app on his phone.

Jeremy Jack, leader of Silent Shade Planting Company the 2023 Top Producer of the Year, uses AI as his daily management teammate from agronomy and business decisions.

Here are the four ways these farmers use AI every day on the farm.

(Photo: Yvonne Min)

1. Make better decisions faster

Colorado farmer Arnusch uses ChatGPT and Grok to narrow down his consideration set when making decisions.

“It helps on the strategic side of things, and when making a decision, I’ll let it give the top four or five things to choose from, which helps when there’s a million choices,” he says. “It really is like my funnel. I’ll set up my phone on my dashboard and just dictate to it. Then when I’m back at the farm office, my wife Jill is relieved because I’ve already processed out loud with the AI tool.”

While most farms collect data, Jack uses AI to make decisions, particularly agronomic.

“I uploaded multiple years of soil data across our farms,” he says. “And we’ve found ways to manage fertilizer better, for example with sulfur.”

The data interpretation has shifted his thinking by connecting the yield zones with as-applied fertility and return on investment.

Jack is also using the technology to double check every spray application — from rates, to tank mix, to nozzle selection, to pressure optimization.

Sharp has also found AI helpful in managing chemical applications. She can remember chemical boxes marked up with her father’s calculations by hand.

“I tell the prompt what I’m spraying, where I’m spraying, how many acres, tank size, and then I let it tell me what to order,” she says. “Over time, it’s learned which products are liquid and which are dry flowables. And it’s helped me keep track of the inventory we have so we don’t end up with pallets of odds and ends.”

(Photo: OpenAI)

2. Be more efficient

When it comes to where to start with AI, Sharp has one piece of advice.

“Think of the task that you don’t like to do at the end of the day. For me, I didn’t want to do paperwork at the end of the day,” she says. “So I threw it over to ChatGPT, and I said, hey, this is what I planted today, this is the date, and I left it at that. I started really, really simple.”

Now, she’ll record things directly in the field or in the truck. She says it has helped with FSA 578 forms. And in day-to-day operations, she’s found benefits for time management and accuracy in all record keeping.

“We have seed samples that require a handwritten seed form that I turn in along with the sample, but I spoke into my phone and said, hey, Chat GPT, I need you to log that I sent this variety, this lot number, on this date, to the lab. And so, that’s probably one of 15 entries that I’ve made over the course of a month. And at the end when we finally turn in our last sample to the lab, I’ll ask it for a spreadsheet with all that listed,” she says.

(Photo: Sarah Green Photography)

3. Think more clearly about complex problems

Jeremy Jack often asks ChatGPT “What does this mean for my farm?” with current events.

“With the war in Iran, global fertilizer supply chain concerns, and even things like USDA reports, it’s given helpful perspective in how to think about what’s happening off the farm but impacts the farm.”

And he’s found success in using the platform to specifically think about the business strategy for his farm with vendors, including lenders, landowners and more.

4. Manage more professionally

Jack has been active with an advisory board for their farm, but AI has become like a boardroom in his pocket.

“I bounce ideas—pressure test if you will—before it costs me real money,” he says. “This includes input purchases, land agreements, and equipment purchases.”

He’s also come to use it in his external communications about the farm including his regular social media posts on LinkedIn.

When it comes to team management, Arnusch has input culture index results for vendors and employees, then the AI compares their individual characteristics with the job they are being asked to do.

“This has been a breakthrough,” he says. “It’s shown me that at no fault of their own, why some people fail at what they are being asked to do. It wasn’t because they weren’t working hard or doing the job. It was stretching them beyond what they can do.”

He gives the example of a farm foreman position on the farm, and how he used this process to match the candidate with the role.

(Sarah Green Photography)

The New Ag Economy: Why This Downturn is a Structural Shift, Not Just a Cycle

Mon, 13 Apr 2026 21:22:18 GMT

What You Need to Know:

Structural Evolution: This downturn is a permanent market shift, not just a temporary cycle.
Friend-Shoring: Trade is moving toward geopolitical allies to ensure supply chain resilience.
Aggressive Cost-Cutting: Farmers are doubling generic input use and delaying machinery purchases to protect margins.
Financial Resilience: Better management and working capital make today far more stable than the 1980s.
Premium Protein Demand: GLP-1 medications are driving consumers toward smaller, higher-quality meat portions

As the industry enters the third year of this downturn, farmers and agribusinesses are questioning if a recovery is on the two-year horizon. While cyclical behavior is normal, two economists suggest the structural evolution within crop protection, machinery, technology, livestock and other individual sectors is creating a different kind of staying power for those who survive the recovery.

The Evolution of the Cycle

When characterizing the current economic cycle in agriculture, historical patterns provide a necessary baseline, yet the present landscape is defined by unique pressures. Typical agricultural cycles consist of roughly six years of expansion followed by four years of decline. Currently, the market is navigating a “corrective period,” returning to long-run averages.

The drivers of growth are typically demand shocks — export surges, fuel demand or policy shifts such as the Renewable Fuel Standard. However, Wes Davis, ag economist at Meridian Ag Advisors, notes the current environment is an intersection of traditional contraction and sector-specific evolution.

“What I think we’re experiencing right now is that typical cycle behavior where we see growth in some business firms, and then some contraction and pullback to adjust to the cycle going back to more of the long-run average,” Davis explains. “I think we’re also seeing evolution of individual sectors within the market where there’s adjustments happening because of the industry itself.”

In other words, this isn’t just a cycle — it’s also a structural shift.

Change Fatigue and Modern Volatility

Farmers aren’t strangers to volatility, but global trade disruptions, policy shifts and rising competition, especially from Brazil, are layering uncertainty onto already volatile markets.
Farmers are grappling with “change fatigue,” a byproduct of the high velocity of information and extreme price swings that dwarf the relative stability of the early 2000s.

“When I go talk to any industry group right now, the phrase that I hear is ‘change fatigue’, and I feel that. Every couple minutes, something shifts,” says Trey Malone, Purdue University ag econ professor. “But to be clear, it’s not that the farm economy isn’t used to volatility, it’s just the uncertainty and the volatility now is, like, ‘hold my beer relative’ to the old volatility.”

Malone attributes this to layers of uncertainty created by global trade and policy. The rise of Brazilian production, coinciding with the disruption of U.S.-China trade relations, has created a permanent state of flux. This sentiment is reflected in the Purdue Ag Economy Barometer, which shares a higher correlation with the Small Business Index (.5) than with actual commodity prices. This suggests farmers view themselves primarily as small business owners facing broad economic pressures rather than just price-takers.

“We don’t see very strong correlations even with lagged soybean prices and corn prices,” Malone notes. “The world is more complicated than just looking at what happened in the market yesterday and gauging how farmers feel.”

Global Competitiveness and the Trade Reallocation

A primary concern for U.S. producers is their position as low-cost providers. While the U.S. maintains an infrastructure advantage that lowers the cost of getting products to export ports, Brazil continues to close the gap.

“It’s a fair question farmers ask a lot: Are we actually the ones who are the low-cost producers, and do we still have a place in the global market if Brazil continues to lower the cost of production and transport their grain to export terminals?” Davis asks.

However, Davis points out that global trade hasn’t shut off; it has reallocated. Only three global regions — North America, Latin America and parts of Southeastern Europe/Central Asia — are net exporters. The rest of the world remains net importers.

“While our trade has kind of shifted around ... that shift has really reallocated stuff in different places. Those calories and products end up going somewhere. It’s just a question of where,” he says.

The Shift to “Friend-Shoring” and Resilient Supply Chains

The industry is moving from “just-in-time” (hyper-lean) procurement to “just-in-case” (inventory-heavy) strategies, a lesson reinforced by the pandemic. This shift is accompanied by “friend-shoring,” where the U.S. prioritizes trade with geopolitical allies.

“We’ve gone from offshoring to onshoring to nearshoring to friendshoring,” Malone explains. “We’ve got a paper that’ll be coming out ... where we document friend-shoring in ag and food supply chains. Over the last 10 years, there’s been a shift where we mostly in the U.S. trade with other people who vote like us in the WTO. That’s kind of one way to measure friends.”

This resilience is also visible in crop protection. In 2019, 80% of active ingredients were sourced from China. Today, that is closer to 60%, with manufacturing shifting to India and domestic sites. Davis calls these “geopolitically resilient” supply chains.

The Rise of Generics and Decision Paralysis

The economic downturn is fundamentally changing the business model for input providers. Farmers are aggressively cutting costs, leading to a massive surge in generic usage.

“The latest survey I saw shows about 60% of farmers use generics today. That was about 30% to 40% just 5 years ago,” Davis says. This forces companies to pivot from differentiation to operational volume.

In the machinery sector, high costs and economic uncertainty have led to “decision paralysis.” Farmers are extending the life of their equipment, treating machinery replacement as the most controllable variable in managing annual ROI. Davis notes the U.S. ag equipment cycle is currently 15 to 20 percentage points lower than typical low points, driven by this hesitation. Furthermore, there is significant skepticism toward subscription-based technology models.

“Farmers don’t terribly love this idea, and I think the other interesting thought here is I’m not sure that retailers like selling them either,” Malone adds.

AI: The “Undergraduate Intern”

While artificial intelligence (AI) is a major talking point, its current role in agriculture is more supportive than transformative. Malone views AI as a “highly capable undergraduate intern” — useful for processing information but incapable of replacing the trust and risk management provided by human advisors.

“I don’t think you need to be replacing your agronomist. I think your mediocre agronomist just got OK,” Malone says, noting while LLMs can pass CCA exams, they cannot manage the risk of a wrong decision. “The risk management value proposition of an in-person Claude, or whoever, is probably going to win out because there’s still a risk.”

Currently, the adoption gap is wide: While 75% of agribusiness managers see potential in AI, only 4% have implemented it, according to a Purdue University survey in 2025.

Livestock and the GLP-1 Impact

The livestock sector is facing a unique demand shift driven by weight-loss medications (GLP-1s). This is leading to “premiumization.” As consumers eat smaller portions, they are opting for higher-quality cuts.

“The explosion in demand for protein is just shocking,” Malone says. “What GLP-1s do to that calorie count is they are all shifting toward premium cuts. You don’t care how much it costs because you’re only going to have seven bites of it. But you’re going to have a steak. That premiumization is going to really, really take off in the next 10 years.”

Conversely, the hype surrounding “fake meat” has largely faded, proving to be more of an investor-led phenomenon than a market-driven one.

Financial Stability: Not the 1980s

Despite the downturn, the financial health of the American farmer remains more stable than during the crisis of the 1980s. Currently, 10% to 12% of farmers are in a “tight” financial position , compared to 20% to 30% in the 80s.

“We do have a completely different, more professional ag workforce than we did back then,” Malone says. “The farm policy we have right now does not necessarily match what we need for the future, but all of these things make me think we’re in a much more stable position.”

Farmers have built-in “shock absorbers,” Davis adds, including off-farm income and working capital built up during the expansion years. However, in his research Davis has seen how alternative financing is becoming a major tool for the 50% of farmers who use it — either to manage stress or, for larger operations, to leverage relationships with retailers.

Strategic Reassessment: Winning at the Bottom

The experts agree the “bottom of the cycle” is the time for professionalization and upskilling. Surviving — and thriving — will require sharper management. It is an opportunity to reassess farm transitions and management disciplines, such as financial management, accounting and planning, which become critical in tight margins.

“Farmers are going to have to get smarter and get more creative with how they manage,” Malone says. “This is a good opportunity to take a step back and think about what the strategy needs to be moving forward.”

Davis emphasizes relationships are solidified during these periods: “Farmers are going to remember the folks who were around when they were in the bottom of the cycle, and who were there to support them. The best farmers will continue to get better ... I get excited about what we can look like as we come out of this cycle.”

So Is This Ag Cycle Different?

These experts say yes as every cycle presents its own unique reshaping of future opportunities.

To download the full report on why this ag cycle is different and what it means for your operation, click here .

Inside The Tax Return of Your Farm's Future

Thu, 09 Apr 2026 13:41:28 GMT

The traditional process of preparing agricultural tax returns has long been defined by manual data entry and the complex reconciliation of income. However, the integration of artificial intelligence into financial systems is ushering in a more sophisticated era of tax management. For the modern farm, the future of filing lies in a seamless pipeline where software handles the heavy lifting of data organization, leaving the high-level strategy to human experts.

Comprehensive Data Integration

The foundation of a modern tax return is the accounting system. Platforms like QuickBooks, Xero or specialized farm management software are becoming increasingly autonomous. In the near future, these AI agents will do more than simply record expenses; they will analyze them in real-time.

With direct links to bank feeds and digital invoices, AI can categorize expenditures with precision. It can distinguish between capital investments, such as machinery or land improvements, and standard operating costs like seed and fuel. This continuous synchronization means by the end of the fiscal year, the financial records are already in a format that mirrors the requirements of a tax return.

Automated Document Reconciliation

A significant portion of tax preparation involves matching — ensuring the farm’s internal records align with the documents issued by third parties. A preparer of a farm tax return may spend more time making sure all of the income is in the right box then planning to optimize the income tax level.

AI is uniquely suited to handle this high-volume verification. The system can automatically ingest Form 1099-PATR (cooperative distributions), 1099-G (government subsidies) and other Form 1099s and W-2s and verify them against recorded deposits.

If a document is missing or a figure does not match the ledger, AI identifies the specific discrepancy immediately, allowing for a targeted correction rather than a manual search through months of records.

The Role of Human Oversight

While AI provides the technical framework for the return, the final stage remains firmly in human hands. Once the software has mapped the data to the appropriate tax schedules, it produces a comprehensive draft for professional review.

This allows the farmer or a tax consultant to transition from a data entry role to a strategic advisory role. Instead of spending hours verifying line items, the human reviewer can focus on critical tax planning decisions including accelerated depreciation choices or income averaging that require professional judgment and an understanding of the farm’s long-term goals.

The result is a more accurate, defensible and efficient tax filing process. By automating the clerical aspects of the return, AI allows agricultural producers to maintain focus on their operations while ensuring full compliance with the evolving tax laws.

The Beef Industry Cannot Afford to Treat AI as a Side Project

Fri, 06 Mar 2026 13:42:45 GMT

The U.S. cattle industry is operating under intense pressure. Supplies are tight, feedlots face rising costs and declining placements, and the national herd remains near multi-decade lows after years of drought, liquidation, and high input prices . Recent reports point to shrinking cattle-on-feed inventories and increasing strain on large feeding operations as they compete for a smaller pool of animals . Yet domestic and export demand for beef remains strong, meaning each animal now carries more economic value than at any point in decades.

Artificial intelligence is beginning to reshape how producers manage that reality. Sensors, cameras and predictive software are moving from pilot projects into everyday use across cow-calf operations, stocker systems, feedlots and packers. Wearables can flag illness through changes in activity or temperature. Computer vision systems estimate weight gain and body condition without handling cattle. Feedlot analytics track intake patterns to identify problems before they appear in closeout data.

Similar pattern-recognition systems are already helping operations detect changes in cattle health, management, and economics earlier across the production cycle. These tools matter because they protect value. When herd size is constrained, losses from disease, poor feed efficiency, or reproductive failure become more expensive. AI allows earlier intervention, reducing both biological and financial risk.

Labor pressures reinforce the shift. Skilled workers are difficult to recruit and retain, particularly in rural areas. Remote monitoring allows managers to oversee cattle across large distances while focusing physical effort where it is most needed. This does not replace stockmanship, but it changes how time is used. However, technology alone does not improve performance. Operations gaining real advantage are not simply installing systems. They are rethinking how decisions are made, how information flows, and how risk is managed across the entire business.

From Data Collection to Decision Power

The beef supply chain is long and biologically complex. Decisions made at breeding or weaning influence outcomes months or years later. Feed costs, weather, forage conditions, genetics, health protocols and market signals interact continuously. AI is valuable in this environment because it can integrate diverse data streams and identify patterns humans might miss.

In cow-calf systems, predictive tools can support heifer selection, breeding management and pasture allocation. Stocker operations can match cattle to forage conditions and growth targets. Feedlots can optimize ration strategies, placement weights, and marketing timing. Packers increasingly use similar tools to forecast demand and coordinate procurement.

When information flows across these segments, the system becomes more responsive. Early signals about feed grain prices, export demand or weather conditions can influence decisions long before cattle reach the packing plant. Health issues detected in one stage can trigger preventive action in another.

Yet many operations accumulate data without changing behavior . Dashboards multiply, reports become more detailed, but daily practices remain largely unchanged. Without a clear plan, AI becomes an expensive reporting system rather than a performance driver.

Our recent white paper on AI in agri-food argues that meaningful impact occurs only when technology is embedded in strategy rather than applied piecemeal. One practical approach is the DRIVE framework, which focuses on five priorities for turning digital tools into operational advantage:

Data first. Reliable, integrated records across genetics, health, feed, performance and marketing are essential. Poor data quality limits predictive accuracy.
Run purposeful pilots. Focus on high-value problems such as reducing death loss, improving feed conversion, or optimizing marketing windows, with clear metrics and a path to scale.
Internal capability matters. Managers and staff must understand how systems work and when human judgment should override model outputs.
VIPs are not exempt. Owner and executive engagement signal that AI is central to strategy, not a technical experiment delegated to vendors.
Execute now. Advantage comes from implementation and learning over time, not waiting for perfect solutions.

Operations that follow this disciplined approach move from experimentation to measurable improvement much faster than those pursuing scattered projects.

Leadership Will Determine Who Wins

Technology tends to amplify existing management quality. Producers with clear goals and disciplined processes extract far more value from AI than those hoping technology will compensate for weak planning. The central challenge is leadership, not software. The transition resembles the arrival of major infrastructure such as high-speed rail. The biggest gains accrue to those who reorganize activities around the new capability. Others see only marginal benefits because they continue operating as before. AI increases the speed of analysis and coordination, but speed without direction can create confusion rather than progress.

I have described AI as agriculture’s “bullet train”: fast, transformative, but requiring careful navigation. AI may be overhyped today, but it is likely to become as essential to food production as electricity and the internet. The question is not whether it will arrive, but how prepared operations will be when it does.

For the beef industry, timing is critical. Herd rebuilding will be slow, capital costs are high and volatility remains constant. At the same time, global demand for high-quality protein continues to grow.

Producers who can improve efficiency, consistency and responsiveness will be better positioned to capture that demand. AI will not replace experience or judgment. Successful cattle production remains rooted in understanding animals, land and markets. What AI changes is how that expertise is applied. Routine monitoring may decline, while interpretation, planning and risk management become more central.

In the coming decade, the divide in the beef sector will not be between those who use AI and those who do not. It will be between those who treat it as a tool and those who treat it as part of a long-term operating plan. The technology is already spreading across the industry. Competitive advantage will depend less on access and more on intent. Producers who start building capability now will shape the future of the beef business rather than reacting to it.

Aidan Connolly, president, AgriTech Capital , is described by Forbes as ‘a food/feed/farm futurologist. He is the author of the book ‘The Future of Agriculture’, now in four languages, and a recent white paper on AI in Agri-Food systems.

A Farmer Can Dream, Right? Tesla Robots As the Farm Labor Force of the Future?

Thu, 24 Oct 2024 18:24:44 GMT

With a visual form ripped straight from a skin-crawl inducing robot thriller, Tesla’s new AI-bot, Optimus, is eliciting strong reactions from tech advocates and flip-phone touting technophobes.

Let’s indulge our imaginations for just a second and imagine how a farmer could put one of Musk’s $20,000 helper robots to work around the family farm in, say, the year 2040. I use 2040 because, even though the prototypes in the video below look awesome, it turns out the AI behind it needs more work before any farmer would feel safe setting a squad of them loose on the farm.

Our own Clinton Griffiths was also inspired by Optimus’ unveiling. In his upcoming column in the November issue of Farm Journal, Clinton gets right to the heart of the issue, and that’s whether the bots will pan out on the farm?

The real test, he writes, “will be whether it can keep its glossy finish motoring along regardless of whether or not the field is mud-free.”

I couldn’t agree more, Clinton. Serving up fancy drinks during an unveiling party on a glitzy Hollywood film studio lot is one thing. Standing up to all the dust and heat and tough conditions of your average farm or ranch is a different beast altogether.

In that vein, we offer up the following farm chore list Optimus can take over from here on out.

No, seriously Opti, you don’t need our permission. Just go ahead and take care of these few little things every single day for the rest of time, and we’ll be off, I don’t know, fishing at the lake with the kids, rocking on the front porch, or something.

Farm equipment maintenance tech
Director of crop protection jug disposal
Backpack spraying around-the-clock weed warrior
Chief grain bin inspector
Head ladder climber
Irrigation pivot inspector general
Head high in July crop scout
Pig loader and unloader extraordinaire
Master bottle mixer and calf feeder

Now that you’ve read my list, I’m curious how you would use a robot that walks, talks and moves like a real human (and never gets tired, bored or spends 20 minutes staring at its phone) on your farm? or click

Share your robot wish list by clicking the green “Respond Here” button or click here .

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John Phipps: AI Aftershocks

Mon, 07 Aug 2023 02:33:07 GMT

It seems only yesterday when critics of American education were denigrating social science degrees like history and urging young students to learn to code. The disquieting consequences of the recent advances in artificial intelligence (AI) undermines that advice. Consider Microsoft Copilot — an AI tool that takes prompts in plain English and writes code needed for an app or inclusion in a larger program. The coding efficiency boost is estimated around 50%. The demand for programmers of all levels could rapidly reflect that productivity boost. Machine learning is eating its parents.

Opinions on AI possibilities range from intriguing entertainment to dystopian disruption. The demand for media content drives such speculation far ahead of convincing evidence. Staggering investment bets persuade some to anticipate professional risks and alter their life plans.

Or coworker or competition?

Workers in various fields are watching the uncanny ability of AI to interact, carry out complex tasks and, bluntly put, depopulate categories of employment. Sales, customer relations, medicine, accounting, human resources and education, along with programming will be early tells of AI impact. More importantly the most powerful AI successes might thrive unnoticed to avoid public unease.

Farmers feel little threat from AI. If anything, it promises to make already automated work even easier. Imagine pulling into a field and saying, “Plant this” and sitting back to watch. We’re not far from that now. We cannot, however, picture giving such command from an office or “no-hands” marketing and financial management.

Farmers are likely far down the list of threatened occupations, but they are a tiny fraction. Any such job safety has a price, however. If AI quietly reduces employment demand, where will displaced workers go? What careers will new entrants aim for? Clearly, the “safer” ones.

Family farm job security, usually undervalued, will lure formerly indifferent returnees. Ag’s landed offspring will saturate entry channels in even larger numbers, despite plummeting birth rates.

Multiple aspiring successors are a historical dilemma for parents. It drove immigration from Europe, after all. With the disappearance of 30-years-and-
a-gold-watch jobs, the AI labor displacement eventually will be reflected in land prices. Incumbent farmers will strive to defend and expand those “employment refuge acres.” Recent data suggest farmers are in a strong equity position to drive that land market.

AI could cause earthquakes in some professions. If and when it does, farm families will feel the aftershocks through the earth.