The AI Cost Curve Lets LATAM Ventures Skip the Series A

The cost to run a model of a given capability is falling by roughly an order of magnitude per year, and that single fact rewrites the math of building an AI startup. Stanford's AI Index found the inference cost for a GPT-3.5-level system dropped more than 280-fold between November 2022 and October 2024. When the build gets that cheap, the money that used to fund a 20-person engineering team moves to product and distribution instead.

For Brazil and the broader LATAM market, the timing matters. Founders here never had the capital depth of their US peers. A falling AI inference cost curve neutralizes that disadvantage just as Brazil's services economy stays under-digitized. This is the case for why a LATAM venture can now launch without a Series A, and why Avante Ventures treats that as a structural opening rather than a slogan.

The cost curve, with dated numbers

Start with the number that anchors everything else. For a model of equivalent performance, inference cost is decreasing by about 10x every year, per Andreessen Horowitz. The concrete version is sharper. At roughly GPT-3 capability, the price ran 60 dollars per million tokens in November 2021 and about 0.06 dollars per million tokens by November 2024, a 1,000x reduction over three years, as documented in [a16z's LLMflation analysis](https://a16z.com/llmflation-llm-inference-cost/).

Stanford backs the same story from a neutral seat. The [AI Index 2025](https://hai.stanford.edu/ai-index/2025-ai-index-report) reports the inference cost for a GPT-3.5-level system fell more than 280-fold between November 2022 and October 2024. For a 2026 reader that is the figure to lead with, because it is recent and it comes from academia, not a fund.

Epoch AI measured the same collapse and held it to a stricter method. Across benchmarks, the price to reach a fixed capability level fell at a median of about 50x per year, with the range running from 9x to 900x depending on the task. The decline has accelerated. From January 2024 onward, the median rate rose to roughly 200x per year, according to [Epoch AI's inference price research](https://epoch.ai/data-insights/llm-inference-price-trends).

The three sources agree on direction and order of magnitude. They differ on the exact slope, which is the honest way to report a moving target. Take the Stanford 280-fold and the a16z 10x per year as the working headline and the 9x to 900x range as the reason to trust it.

What cheap inference moves the money toward

When inference, vector search, and managed infrastructure all commoditize, the fixed cost of standing up an AI product collapses and the marginal cost of testing an idea approaches zero. The capital a 2021 startup burned on a platform team to build retrieval, eval harnesses, and serving infrastructure is now a managed API line item. The scarce input shifts from engineering capacity to product judgment and access to a market.

This is the structural reason a class of AI companies now reaches scale with tiny teams and little outside capital. Reporting through 2025 describes seed-strapped AI startups that refuse large rounds to stay lean and reach profitability early, and a wave of AI-native companies hitting serious revenue with headcounts under 50. The verified hard number underneath all of it is the cost curve.

• The build is no longer the differentiator. Writing the plumbing is a commodity that gets cheaper every quarter.
• The differentiated value moves to domain access, proprietary data, and speed to revenue.
• That is exactly the set of inputs a venture studio supplies on day one rather than leaving a founder to assemble over 18 months.

Routing $300K-500K to product, not infra

Here is where the cost curve meets the studio balance sheet. Solving company plumbing once routes roughly $300K-$500K of effective capital per venture into product and traction rather than overhead. With Avante deploying $500K-1.5M per venture across pre-seed, the falling cost curve is what makes that routing real instead of aspirational. When the infrastructure line item shrinks toward an API bill, more of the first ticket reaches the customer.

Put it in founder terms. A 2021 seed-stage AI team might have spent a third of its first year of cash standing up infrastructure that a 2026 team rents by the call. That recovered third is the difference between one shot at product-market fit and three.

The studio model compounds this. Shared infrastructure across a portfolio, plus a cost curve that keeps falling, means the same dollar buys more product attempts every year.

Why the timing favors Brazil

Start with the structural fact. Services account for roughly 70% of Brazilian GDP, with low software penetration. That base is still under-digitized, and it is still growing. The services sector expanded 3.1% in 2024, its fourth straight year of growth, according to [IBGE](https://agenciabrasil.ebc.com.br/economia/noticia/2025-02/setor-de-servicos-cresce-31-em-2024-mostra-ibge). A large, growing, software-thin economy is exactly what an AI-native team can now address without a Series A.

Now the capital backdrop. LATAM venture funding reset hard after 2021 and is recovering off a low base. In 2024 the region drew about 4.5 billion dollars across 751 deals, an 8% increase year over year, with Brazil taking 44% and Mexico 26%, per [LAVCA industry data](https://www.lavca.org/research/2024-lavca-industry-data-analysis/). For scale, that full-year regional total is a rounding error next to a single large US AI round. LATAM founders have never competed on capital depth.

The timing argument follows directly. A cheaper cost curve neutralizes the exact disadvantage that thin capital used to impose. When the build no longer requires a 20-person team and a Series A to fund it, the infrastructure playing field flattens, and the edge that remains is domain operator depth. Brazil has that in abundance. AI infrastructure is now cheap enough to deploy without a Series A.

Cheap inference is not a moat

Here is the part a pitch deck would skip. A falling cost curve is available to everyone. It lowers the barrier for your competitors at the same rate it lowers it for you. Cheap inference is a tailwind, not a moat. Anyone with a credit card and an API key gets the same prices you do.

There is a second trap. Per-token prices fall while total inference spend can climb, because newer reasoning models burn far more tokens per task. Cheap per unit is not cheap in aggregate once usage scales. Epoch AI flagged this directly in its 2025 work. The lesson is to treat the cost curve as a starting condition, not a strategy.

If cost is not a moat, the durable advantage has to come from somewhere the cost curve does not touch. The studio answer is the copilot to data to fund flywheel. Build an AI copilot to generate proprietary data, then use that data to raise and deploy capital. The copilot is cheap to build precisely because of the cost curve. The data it accumulates is the moat the cost curve cannot erode.

How Avante uses the curve

Avante Ventures is a venture studio building AI-native companies in Brazil and Latin America. It treats the cost curve as a tailwind, not a thesis. The thesis is operator depth paired with proprietary data, assembled on day one.

The mechanics are specific. Avante launches 3-4 ventures per year through a six-stage system. Research, Partner, Build, Traction, Revenue, Compound. It deploys $500K-1.5M per venture across pre-seed and retains co-founder economics. Because the cost curve routes roughly $300K-$500K of effective capital per venture into product rather than overhead, a studio venture launches 6-9 months ahead of a comparably funded standalone team.

The benchmark behind the model is blunt. Venture studios materially outperform traditional venture capital on IRR, at a studio IRR of ~50% versus an industry-standard ~19% for traditional VC, per the Global Startup Studio Network (GSSN). That is roughly 2.5x the IRR of traditional VC over realistic time horizons, and it is the studio-model benchmark, not Avante's own realized return.

The cost curve makes the build cheap. Domain operators with 10+ years of Brazilian-market scar tissue, and the proprietary data they generate, are what make it defensible. The first one is a gift the whole market receives. The second is the only part a competitor cannot buy with a credit card. Read the full thesis at [/why-avante](/why-avante), or browse related market analysis in the [/library](/library).