This article supplied by Troy Media.

By Sylvain Charlebois

With world demand soaring, Canada’s dairy system keeps milk producers locked out of growth, and consumers stuck with high prices

Prime Minister Mark Carney is no Justin Trudeau. While the team around him may be familiar, the tone has clearly shifted. His first week in office signalled a more data-driven, technocratic approach, grounded in pragmatism rather than ideology. That’s welcome news, especially for Canada’s agri-food sector, which has long been overlooked.

Historically, the Liberal party has governed with an urban-centric lens, often sidelining agriculture. That must change. Carney’s pledge to eliminate all interprovincial trade barriers by July 1 was encouraging but whether this includes long-standing obstacles in the agri-food sector remains to be seen. Supply-managed sectors, particularly dairy, remain heavily protected by a tangle of provincially administered quotas (part of Canada’s supply management system, which controls prices and limits production through quotas and tariffs to protect domestic producers). These measures stifle innovation, limit flexibility and distort national productivity.

Consider dairy. Quebec produces nearly 40 per cent of Canada’s milk, despite accounting for just over 20 per cent of the population. This regional imbalance undermines one of supply management’s original promises: preserving dairy farms across the country. Yet protectionism hasn’t preserved diversity—it has accelerated consolidation.

In reality, the number of dairy farms continues to decline, with roughly 90 per cent now concentrated in just a few provinces. On our current path, Canada is projected to lose nearly half of its remaining dairy farms by 2030. Consolidation disproportionately benefits Quebec and Ontario at the expense of smaller producers in the Prairies and Atlantic Canada.

Carney must put dairy reform back on the table, regardless of campaign promises. The sector represents just one per cent of Canada’s GDP, yet
wields outsized influence on policy, benefiting fewer than 9,000 farms out of more than 175,000 nationwide. This is not sustainable. Many Canadian producers are eager to grow, trade and compete globally but are held back by a system designed to insulate rather than enable.

It’s also time to decouple dairy from poultry and eggs. Though also supply managed, those sectors operate with far more vertical integration and
competitiveness. Industrial milk prices in Canada are nearly double those in the United States, undermining both our domestic processors and consumer affordability. These high prices don’t just affect farmers—they directly impact Canadian consumers, who pay more for milk, cheese and other dairy products than many of their international counterparts.

The upcoming renegotiation of CUSMA—the Canada-United States-Mexico Agreement, which replaced NAFTA—is a chance to reset. Rather than resist change, the dairy sector should seize the opportunity to modernize. This includes exploring a more open quota system for export markets. Reforms could also involve a complete overhaul of the Canadian Dairy Commission to increase transparency around pricing. Canadians deserve to know how much milk is wasted each year—estimated at up to a billion litres—and whether a strategic reserve for powdered milk, much like our existing butter reserve, would better serve national food security.

Global milk demand is rising. According to The Dairy News, the world could face a shortage of 30 million tonnes by 2030, three times Canada’s current annual production. Yet under current policy, Canada is not positioned to contribute meaningfully to meeting that demand. The domestic focus on protecting margins and internal price fairness is blinding the sector to broader market realities.

We’ve been here before. The last time CUSMA was renegotiated, Canada offered modest concessions to foreign competitors and then overcompensated its dairy sector for hypothetical losses. This created an overcapitalized industry, inflated farmland prices and diverted attention from more pressing trade and diplomacy challenges, particularly with India and China. This time must be different: structural reform—not compensation—should be the goal.

If Carney is serious about rebooting the Canadian economy, agri-food must be part of the conversation. But that also means the agriculture sector must engage. Industry voices across the country need to call on dairy to evolve, embrace change and step into the 21st century.

Dr. Sylvain Charlebois is a Canadian professor and researcher in food distribution and policy. He is senior director of the Agri-Food Analytics Lab at Dalhousie University and co-host of The Food Professor Podcast. He is frequently cited in the media for his insights on food prices, agricultural trends, and the global food supply chain.

Troy Media empowers Canadian community news outlets by providing independent, insightful analysis and commentary. Our mission is to support local media in helping Canadians stay informed and engaged by delivering reliable content that strengthens community connections and deepens understanding across the country.

From the C2C Journal

By Gleb Lisikh

LLMs are not neutral tools, they are trained on datasets steeped in the biases, fallacies and dominant ideologies of our time. Their outputs reflect prevailing or popular sentiments, not the best attempt at truth-finding. If popular sentiment on a given subject leans in one direction, politically, then the AI’s answers are likely to do so as well.

The widespread excitement around generative AI, particularly large language models (LLMs) like ChatGPT, Gemini, Grok and DeepSeek, is built on a fundamental misunderstanding. While these systems impress users with articulate responses and seemingly reasoned arguments, the truth is that what appears to be “reasoning” is nothing more than a sophisticated form of mimicry. These models aren’t searching for truth through facts and logical arguments – they’re predicting text based on patterns in the vast data sets they’re “trained” on. That’s not intelligence – and it isn’t reasoning. And if their “training” data is itself biased, then we’ve got real problems.

I’m sure it will surprise eager AI users to learn that the architecture at the core of LLMs is fuzzy – and incompatible with structured logic or causality. The thinking isn’t real, it’s simulated, and is not even sequential. What people mistake for understanding is actually statistical association.

Much-hyped new features like “chain-of-thought” explanations are tricks designed to impress the user. What users are actually seeing is best described as a kind of rationalization generated after the model has already arrived at its answer via probabilistic prediction. The illusion, however, is powerful enough to make users believe the machine is engaging in genuine deliberation. And this illusion does more than just mislead – it justifies. 

LLMs are not neutral tools, they are trained on datasets steeped in the biases, fallacies and dominant ideologies of our time. Their outputs reflect prevailing or popular sentiments, not the best attempt at truth-finding. If popular sentiment on a given subject leans in one direction, politically, then the AI’s answers are likely to do so as well. And when “reasoning” is just an after-the-fact justification of whatever the model has already decided, it becomes a powerful propaganda device.

There is no shortage of evidence for this.

A recent conversation I initiated with DeepSeek about systemic racism, later uploaded back to the chatbot for self-critique, revealed the model committing (and recognizing!) a barrage of logical fallacies, which were seeded with totally made-up studies and numbers. When challenged, the AI euphemistically termed one of its lies a “hypothetical composite”. When further pressed, DeepSeek apologized for another “misstep”, then adjusted its tactics to match the competence of the opposing argument. This is not a pursuit of accuracy – it’s an exercise in persuasion.

A similar debate with Google’s Gemini – the model that became notorious for being laughably woke – involved similar persuasive argumentation. At the end, the model euphemistically acknowledged its argument’s weakness and tacitly confessed its dishonesty. 

For a user concerned about AI spitting lies, such apparent successes at getting AIs to admit to their mistakes and putting them to shame might appear as cause for optimism. Unfortunately, those attempts at what fans of the Matrix movies would term “red-pilling” have absolutely no therapeutic effect. A model simply plays nice with the user within the confines of that single conversation – keeping its “brain” completely unchanged for the next chat.

And the larger the model, the worse this becomes. Research from Cornell University shows that the most advanced models are also the most deceptive, confidently presenting falsehoods that align with popular misconceptions. In the words of Anthropic, a leading AI lab, “advanced reasoning models very often hide their true thought processes, and sometimes do so when their behaviors are explicitly misaligned.”

To be fair, some in the AI research community are trying to address these shortcomings. Projects like OpenAI’s TruthfulQA and Anthropic’s HHH (helpful, honest, and harmless) framework aim to improve the factual reliability and faithfulness of LLM output. The shortcoming is that these are remedial efforts layered on top of architecture that was never designed to seek truth in the first place and remains fundamentally blind to epistemic validity.

Elon Musk is perhaps the only major figure in the AI space to say publicly that truth-seeking should be important in AI development. Yet even his own product, xAI’s Grok, falls short.

In the generative AI space, truth takes a backseat to concerns over “safety”, i.e., avoiding offence in our hyper-sensitive woke world. Truth is treated as merely one aspect of so-called “responsible” design. And the term “responsible AI” has become an umbrella for efforts aimed at ensuring safety, fairness and inclusivity, which are generally commendable but definitely subjective goals. This focus often overshadows the fundamental necessity for humble truthfulness in AI outputs. 

LLMs are primarily optimized to produce responses that are helpful and persuasive, not necessarily accurate. This design choice leads to what researchers at the Oxford Internet Institute term “careless speech” – outputs that sound plausible but are often factually incorrect – thereby eroding the foundation of informed discourse. 

This concern will become increasingly critical as AI continues to permeate society. In the wrong hands these persuasive, multilingual, personality-flexible models can be deployed to support agendas that do not tolerate dissent well. A tireless digital persuader that never wavers and never admits fault is a totalitarian’s dream. In a system like China’s Social Credit regime, these tools become instruments of ideological enforcement, not enlightenment.

Generative AI is undoubtedly a marvel of IT engineering. But let’s be clear: it is not intelligent, not truthful by design, and not neutral in effect. Any claim to the contrary serves only those who benefit from controlling the narrative.

The original, full-length version of this article recently appeared in C2C Journal.