Stuck in the Past: Why Ag Must Accelerate Its AI Adoption

How Artificial Intelligence Will Improve Your Risk Management & Trading

Many groups advertising AI use very rudimentary forms or very little at all. Glorified Excel sheets generate an AI “signal.” Customers who fear Skynet are unwittingly settling for Builder.ai.

Builder.ai was the Microsoft-backed unicorn ($1.5 billion valuation) that turned out to be 700 Indians crammed into a building—essentially the same phenomenon as adding the words “crypto or blockchain” to any business in 2018 and “Dot Com” in 1999.

Geopolitics and the Race for AI Supremacy

Today’s AI is as smart as the training data and the prompts we formulate. The larger the datasets and computing power, the more powerful the model. OpenAI’s partnership with Microsoft provided access to one of the world’s most robust datasets and the warchest to build the world’s fifth most powerful computer.

The billions of investment will start to pay off.

This is why the global race to access the most comprehensive data sets—often supported or enabled by governments—is increasingly intersecting with massive resource demands. Technologies like AI training, data centers, and quantum computing require extraordinary amounts of energy, water, and hardware infrastructure. What remains largely unknown to the public is just how much of the world’s natural and energy resources are consumed daily by Silicon Valley and other tech hubs to fuel digital expansion.

Sidenote. In How Deregulation and Expensive Money Killed Argentina's Zombie Economy I cover Patagonia’s energy industry collapse in provinces Santa Cruz and Chubut. Here is a prediction. Twenty years from now, southern Argentina will be a data center hub.

Yet, many of us think of AI and the more advanced versions (AGI: Artificial General Intelligence) as Skynet or VIKI (I, Robot). An interesting phenomenon is how Western culture focuses on the extermination risks or how AI can become violent towards humans. Eastern culture focuses much more on how AI is a tool to complement our daily lives.

"The way to beat China in the AI race is to outrace them in innovation, not saddle AI developers with European-style regulations."

-Senator Ted Cruz

I really think OpenAI is a manifestation of Altman. He is a product of Silicon Valley himself and he is very much a product of this philosophy of growth at all costs. Try and be ambitious about changing the world and assume that the world is a zero sum game and that winners take all in this race.

-Karen Hao (Empire of AI)

Some US leadership places the AI race as the most important geopolitical battleground. The consequence is that caution gets thrown to the wind, and proactive regulation becomes an afterthought. The messaging from the White House and AI leaders is about winning, not responsible development.

The race for artificial intelligence supremacy began between 2016 and 2019; the rest is now an afterthought.

Nathan: So, do you know what the Turing Test is?

Caleb: Yeah. I know what the Turing Test is. It's when a human interacts with a computer and if the human doesn't know they're interacting with a computer, the test is passed.

Nathan: One day the AIs are going to look back on us the same way we look at fossil skeletons on the plains of Africa. An upright ape living in dust with crude language and tools, all set for extinction.

Ava: Isn't it strange, to create something that hates you?

The key takeaway is that while risks exist, the current race for AI dominance has produced rules that are often rushed and loosely defined. Axios asks a critical question: What if the doomers are right? Citing workers who have quit AI companies because of grave concerns.

Artificial intelligence stands as one of the most transformative developments of our time. It is the equivalent of the computer AND the nuclear bomb. It holds the potential to be either a powerful tool or a dangerous weapon. For now, let’s focus on how it can be a force for good — enhancing everyday life, productivity, and human potential.

It is important to remember that everything you put into AI has the potential to live somewhere.

NLP, LLMs, & Machine Learning: What is AI?

The most common forms of AI we work with at AiQ are LLMs, big data/deep learning, and machine learning. Let’s take a second to understand the different types of AI to discuss apples and apples.

Here are a few of the most common types of artificial intelligence.

• Machine Learning (ML)

• Deep Learning

• Natural Language Processing (NLP)

• Large Language Models (LLM)

• Computer Vision

• Robotics

• Generative AI (AGI)

• Cognitive Computing

This is a short list. Type any of these into ChatGPT or Grok and start down the educational rabbit hole of each type of AI and its capabilities.

NLPs and LLMs have leapt from kindergarten to PhDs in less than half a decade. AI identification software can no longer accurately identify AI, especially when humans use it efficiently.

Unsupervised Machine Learning (ML) identifies patterns in virtually anything (data mining), which is bad for some purposes (making money) and good for other purposes (research idea generation). The more complex the AI, the more error-prone it is. We are in the early stages of exponential progress.

This is a phenomenal article about how AI is upending education conventions. All teachers and parents should read.

Cheating with AI viewed from the prism of confrontation is as much a failure of the professor as the student, maybe even more so. Fighting AI will only end one way: the students and professors will be left worse off.

Cheaters. Kids these days, everyone says, are all a bunch of blatant cheaters via AI.

Then again, look at the game we are forcing them to play, and how we grade it.

If you earn your degree largely via AI, that changes two distinct things.

1. You might learn different things.

2. You might signal different things.

Both learning and signaling are under threat if there is too much blatant cheating.

There is too much cheating going on, too blatantly.

Why is that happening? Because the students are choosing to do it.

Ultimately, this is a preview of what will happen everywhere else as well. It is not a coincidence that AI starts its replacement of work in the places where the work is the most repetitive, useless and fake, but its ubiquitousness will not stay confined there. These are problems and also opportunities we will face everywhere.

“Get a Degree, Take on Debt, It Will Pay Off”

Is advice that is outdated and may even prove detrimental for young people. AI is poised to have the most profound impact on labor markets and productivity in modern history, and many of these shifts were already underway before 2020.

If you're still offering students or recent graduates the same career advice you gave a decade or two ago, it's time to reassess. Employment trends themselves are evidence that this guidance no longer fits the new reality. In fact, the earnings premium for college graduates has dropped nearly 40% between 2015 and 2025, signaling a major risk when young people take on debt for education.

Employers have also trimmed jobs in graduate-friendly industries. Across the EU the number of 15-to-24-year-olds employed in finance and insurance fell by 16% from 2009 to 2024.

Since 2016, however, the number of twentysomethings in law and finance has fallen by 10%.

It is tempting to blame AI for these waning opportunities. The technology looks capable of automating entry-level “knowledge” work, such as filing or paralegal tasks. Yet the trends described in this piece started before ChatGPT came along. Lots of contingent factors are responsible.

-The Economist (Why Today’s Graduates Are Screwed)

Agriculture Risk Management Will Evolve

AI is showing up almost everywhere in agriculture—from warehouse robots and drone sprayers to self-driving combines and cutting-edge seed genetics. But when it comes to using AI for information flow and risk management, adoption is still lagging far behind. Why the disconnect? Here are a few of my thoughts.

1. Many “experts” offering AI services deliver glorified Excel sheets, not representative of AI’s capabilities. Farmers or traders still pay to hear people’s “opinions,” only marketed more creatively — I am being generous here. It is not worth much.

2. Age and skills. The average US worker in agriculture is nearly 50, 6-8 years older than in other professions. Adoption is slower in this environment.

3. Uncertainty breeds distrust of new methods. Weather is, by definition, uncertain. Prices are volatile. Farming and trading are similar phenomena where a farmer or trader can do everything right and still lose money. Crop insurance limits this for some in the USA.

In the same vein, failing to understand or misrepresenting AI does the entire industry a disservice. “Watson,” a proof of concept, accomplished its goals of beating humans in a voice-required memory game. This was an early AI proof-of-concept in its infant stage. Imagine a genius toddler suddenly able to beat the world’s best adult at a specific task. The hype and subsequent missed the point. Watson was not ready to teach at a PhD level; it was tasked with voice recognition and memory.

I had a discussion a while back that irked me. An agriculture expert writes with authority about AI, yet makes no effort to understand AI. In the never-ending race to catch eyeballs, articles like these will prevent readers from improving.

Let’s be clear, modern AI was in its infancy in 2018. If your education and opinion have not changed, AI is not the problem; you are.

The following are excerpts from the article referenced above.

• The argument could be made that late Thursday and early Friday market activity in the grain and oilseed complex has been heavily influenced by trading algorithms reaction to incorrect weekly export sales data from USDA.

• While I've long given algorithms credit for evolving beyond the simplistic, and not overly intelligent practice of trading USDA reports, I am being forced to reexamine my position.

• Was Watson (my name for algorithm trade) really unintelligent enough to trade outdated USDA numbers? A look at the results at Friday midday and the answers seems to be yes, Watson is unintelligent enough to trade USDA reports. This confirms fears from earlier in the week when Watson responded to pictures of ears of corn on social media with a resounding round of buying, setting the stage for what should be an interesting CFTC Commitments of Traders report

Let’s clarify a few things. The only data an AI uses is the data it’s trained on by humans. So, when the author blames “AI,” they’re actually criticizing the people behind it—implying that speculators themselves are “unintelligent.”

That’s a very unintelligent premise if you’ve met any portfolio managers in 2025.

Next, an algorithm is just an equation. While an algorithm can be used in AI—machine learning being one example—AI is not just an algorithm. Algorithms have existed long before Alan Turing famously cracked Enigma during WWII.

An algorithm is a finite, executable sequence of steps designed to solve a problem or perform a computation.

That’s not AI—it’s basic math. Reducing all of AI and algorithmic trading to the term “Watson,” which was popularized during the early stages of the most transformative technology in modern history, shows a fundamental misunderstanding of what AI was then.

What AI is today.

And what AI will be capable of in the future.

If you don’t want to learn, no problem, don’t write misleading information.

While Darin’s arrogance is mildly offensive, I will take the high road and let Chat respond.

Chat, how should I respond to someone who arrogantly says your capabilities are no more intelligent than Watson, the original jeopardy memory computer?

ChatGPT:

"That’s an outdated comparison. Watson was essentially a question-answering system built on preprogrammed logic and keyword search—it didn’t understand context, nuance, or intent. Modern AI models like ChatGPT are trained on massive datasets using deep learning, which enables far more flexible reasoning, contextual understanding, and generative capability. Saying today’s AI is 'no more intelligent than Watson' is like comparing a bicycle to a self-driving car just because both have wheels."

"Watson played Jeopardy. I can write Jeopardy. There’s a difference."

There you have it. From the AI himself… itself… Let’s move on.

It’s Worth Exploring How AI Can Help Your Business and Lower Costs

AI is here to streamline your operation and put more money back in your pocket. While some will be hesitant or ignore it, AI isn’t here to replace trusted relationships — it’s here to support them. It can take repetitive tasks off your plate, reduce errors, and give you more time to focus on what matters most: running your farm, advising your clients, or becoming a more disciplined trader.

Whether it’s optimizing input costs, monitoring field conditions in real time, or identifying trends in your grain marketing strategy, AI will be working behind the scenes to simplify decision-making. Learning a few basics now will give you a head start on tools that are quickly becoming essential. Below is an example of how much simpler the process is to generate a meaningful signal with AI.

Corn & Crude Oil: Using AI to Explore Prices