November 21, 2025
Global Outlook
Artificial intelligence (AI) is no longer an emerging technology; it has become the engine reshaping the global economy. The AI Index 2025, the most comprehensive report on AI evidence and trends, reveals a structural shift: in 2024, global AI investment reached US$252 billion, 26% higher than the previous year and 13 times greater than a decade ago. The United States leads with more than US$109 billion, followed by China, while generative AI alone attracted US$34 billion in investment. Investment is concentrated in three pillars that sustain the entire ecosystem: model development (foundation, multimodal, and generative models), sector-specific AI applications, and deep infrastructure—chips, data centers, cloud computing, fiber optics, and all the technological backend required for the current revolution.
Whoever controls computing power controls the AI of the future.
Infrastructure has become as strategic as energy or logistics. The competition is no longer only about who trains the best models, but about who controls AI’s critical infrastructure. The United States is promoting data centers exceeding 500 MW and has allocated US$52 billion through the CHIPS & Science Act program. China, meanwhile, launched Big Fund II, a state-backed fund worth US$40 billion aimed at achieving hardware self-sufficiency and expanding its Green AIDC (energy-efficient data centers). Europe is moving forward with its own Chips Act and the EuroHPC supercomputing network.
Efficiency, Chips, and the Falling Cost of Inference
The pace of technical progress is astonishing. According to the AI Index, chip performance is growing by 43% per year, while energy efficiency improves by 40% annually. The cost of inference—the moment when a model “thinks”—fell 280-fold between 2022 and 2024, thanks to more powerful chips, more efficient models, and software optimization.
This enables a crucial shift: small models (3–8 billion parameters) are already achieving performance levels that were previously reserved for giant models. Why does this matter for Argentina? Because it enables offline AI in the field, in agricultural machinery, sensors, drones, and mobile phones. Lower costs mean greater adoption.
AI is already transforming Argentine agriculture across four key fronts:
🌦️ AI + Climate Change: Forecasting and Risk Management
AI is improving short-term weather forecasting, early warning systems for frost, droughts, and wildfires, as well as multivariate risk analysis. This allows climate uncertainty to be managed with much greater precision—something strategically important for a country highly exposed to extreme weather events.
🧑🌾 Generative AI as a “Technical Copilot”
Generative models are already assisting technicians and extension agents in drafting diagnostics, interpreting crop images, summarizing academic papers, and developing agronomic recommendations. Most importantly, smaller models can operate offline, making them viable for use in rural areas with limited connectivity.
🤖 Robotics and the Industrialization of Agriculture
AI is “moving into the physical world”: robots for weed control, autonomous weeding systems, robotic milking, livestock monitoring, and assisted harvesting. This is a global trend: in 2023 alone, China installed 276,000 industrial robots. Agriculture is fully immersed in this wave of transformation.
📊 Traceability and New Environmental Standards
AI enables intelligent traceability through remote monitoring, automated certifications, and climate or carbon verification systems. This will be essential for gaining access to demanding markets such as the European Union and China, where environmental data requirements are increasingly becoming mandatory.
What Can INTA Do in This Scenario?
INTA can move forward with an agenda for the next three years that positions it to lead this process on multiple fronts.
For Argentina to fully capitalize on this new technological wave, INTA can play a decisive role. I propose four strategic priorities that could guide a national agenda for AI in agriculture:
1. Build a National Agricultural Data Strategy
Unify, standardize, and govern Argentina’s agricultural data so that producers, technicians, and institutions can apply AI in a reliable, secure, and scalable manner. Without a well-organized data layer, transformation is not possible.
2. Promote AI Copilots and Large-Scale Training for Extension Agents
Train field teams in the use of intelligent assistants that can help diagnose problems, interpret images, formulate recommendations, and communicate more effectively and efficiently.
3. Create Applied AI Laboratories and Federal Pilot Projects
Develop experimental environments where real AI solutions can be tested in the field and adapted to each agroecological region of the country. This will accelerate adoption, validate technologies, and generate local evidence.
4. Define Interoperability Standards for Machinery and Sensors
Ensure that tractors, drones, weather stations, and sensors can “speak the same language,” integrating data and enabling true automation. Interoperability is essential for achieving efficiency and scaling AI solutions.
INTA can become the key actor connecting knowledge, technology, and territory, enabling Argentina’s entry into the new technological frontier.