The AI model layer has taken the main course of venture capital over the past year. Brazilian agriculture could serve up dessert.
Hungry Capital
AI has already swallowed the venture capital agenda. According to Crunchbase, AI funding reached $211 billion in 2025, up 85% from $114 billion in 2024, representing roughly half of all global venture funding. OECD data puts the figure even higher, estimating that AI firms captured 61% of global VC investment in 2025, equal to $258.7 billion out of $427.1 billion.
This huge wave of investment was needed to build models, infrastructure and computing power before AI can be applied widely across the economy. But as this layer becomes more consolidated and harder for new entrants to win, venture capital will start looking for other places where AI can create real business value.
The next wave of AI investing will flow to where models can be applied to large markets, complex workflows and expensive problems. Legal, healthcare, finance and logistics are already attracting attention. Agriculture will also be part of that search, and this is where Brazil comes in.
Brazilian agriculture has scale, complexity, data, pain points and global relevance. It is also full of workflows that are manual, fragmented, relationship-driven and only partially digitized. The same complexity that made agriculture hard for traditional software may be what makes it so attractive for AI.
Big Field
Agriculture is often described as one sector, but that misses the point. It is really a dense network of decisions that stretch from the soil to the port, from the farm gate to the balance sheet, and from weather risk to working capital.
A farmer is not only deciding what to plant. He is managing weather risk, input costs and market timing. A cooperative is not only selling inputs. It is advising producers, extending credit and coordinating logistics. A grain trader is not only buying soybeans. It is managing freight, price risk and counterparty exposure.
Vertical AI becomes interesting in agriculture because so many of these decisions already shape margins, risk and cash flow. Selling grain, approving credit and spotting disease pressure are all high-value areas where better intelligence can make a measurable difference.
Brazil gives this thesis unusual scale. Agribusiness exports reached a record $169.2 billion in 2025, accounting for 48.5% of Brazil’s total exports. Brazilian agriculture is not a small market waiting to be discovered by technology investors. It is already one of the country’s core economic engines and one of the most important food and agricultural systems in the world.
Messy Moats
Every investor likes the word moat. In agriculture, the moat may come from the mess.
Brazil’s ag economy still runs through a blend of WhatsApp messages, field visits, invoices, satellite images, phone calls, spreadsheets, informal knowledge and long-term relationships. For traditional software, that can be difficult to organize. For AI, it is raw material.
To work in a vertical like agriculture, AI needs more than a strong model. It needs industry context, proprietary data and access to processes that outsiders cannot easily understand or copy. Agriculture has all three, although they rarely come neatly packaged. Information is often scattered across people, machines, fields, buyers, lenders and service providers, which makes the problem harder but the opportunity more defensible.
Moving grain in Brazil, for example, is a seasonal, regional and highly volatile decision system shaped by harvest timing, rail capacity and port lineups. A platform that understands those variables is doing more than digitizing freight. It is building intelligence around one of the biggest cost centers in the value chain.
The same idea applies across animal protein, biological inputs and crop monitoring. The companies that can capture and interpret signals from these activities will not only have better software. They will have a stronger position in the value chain.
Agrifoodtech funding has been through a painful reset globally, but investor interest is moving toward technologies that can create real defensibility, including AI, robotics, biotech and advanced sensing. That should favor companies solving hard problems, not those adding a thin AI layer to old software.
Credit Gateway
The easiest bridge between AI capital and agriculture may be the fintech route.
Latin America’s venture story has already been shaped by fintech. Many of the region’s most successful startups used better data, better interfaces and better underwriting to reach customers that traditional financial institutions ignored, misunderstood or priced poorly.
Agriculture has a similar opportunity, but with more variables and muddier boots. In consumer fintech, credit risk depends on income, spending patterns and repayment history. In agriculture, repayment also depends on production, weather and commodity prices. A farmer may be a good borrower and still have a bad year because yields fall, prices move or freight costs eat into margins.
That makes ag credit harder to model, and more valuable when done well. Traditional credit models are often too blunt for agriculture, while AI can process richer signals from satellite imagery, production history, invoices, market prices and logistics data.
Agfintech is already one of the most venture-compatible categories in agriculture because it has clear pain, large markets, recurring demand and business models investors understand. In The Yield Lab’s Latam portfolio, around 40% is already made up of agfintech companies. Much of the other 60% could eventually offer some form of embedded finance, because many already have customer relationships, operational visibility and rich data.
That may be the real unlock. Credit does not need to remain a standalone product. A marketplace can offer credit, a logistics platform can finance freight, and a sustainability platform can support transition finance. The first wave of agfintech digitized access to capital. The next wave could use AI to predict risk, price capital and personalize finance around the real economics of the farm.
Seed Scale
If global AI investors start looking seriously at Brazilian agriculture, they will not arrive searching for ideas. They will be looking for companies with traction, proprietary data, scalable distribution and enough maturity to absorb large Series A or Series B rounds.
That creates both an opportunity and a risk. The opportunity is that Brazilian agriculture fits what vertical AI capital will likely want next: large problems, complex workflows, valuable data and real economic pain. The risk is that many AI-native agriculture startups may still be too early when that capital arrives.
This is why early-stage conviction matters. Brazil needs more founders building AI-native solutions for large, scalable problems in agriculture. It needs more data-driven pilots with farmers, corporates, cooperatives and financial institutions. It also needs more events, demos and practical use cases that move the market from general AI excitement to measurable business value.
That is exactly the kind of momentum we hope to build at our next AI in Ag event on May 28 in São Paulo, with participation available both in person and online for Portuguese speakers. The goal is to showcase real use cases, connect the right people and help move AI in agriculture from potential to practice.
When capital moves from models to markets, Brazilian agriculture should be ready to serve.
Thanks for reading.
KFG
Kieran Finbar Gartlan is an Irish native with over 30 years experience living and working in Brazil. He is Managing Partner at The Yield Lab Latam, a leading venture capital firm investing in Agrifood and Climate Tech startups in Latin America.