Shiva Pitch Deck: All 10 Slides + Teardown

The Shiva Pitch Deck: A High-Volume Bet on AI Efficiency

Shiva represents a new breed of investment vehicle that looks less like a traditional venture capital firm and more like a high-throughput talent incubator. The deck for their $10M pre-seed round, raised in 2024, outlines a thesis built entirely on the transformative power of AI to reduce the human capital required to build software. By focusing on solo founders and small teams, Shiva aims to build a portfolio of 100 micro-startups. This teardown examines how the deck balances the high risk of early-stage talent with a disciplined, milestone-based capital allocation strategy.

Slide 1: Title Slide

The deck opens with a minimalist black slide featuring the Shiva logo in a muted green. There is no tagline or mission statement on this slide, relying entirely on the brand name. In a fundraising context, this requires the subsequent slides to immediately establish the 'what' and 'why' of the business.

Slide 2: The Moment Has Arrived

Slide 2 establishes the market thesis. It uses three key data points to argue that the 'era of the micro-startup is here.' First, it claims 1 engineer can now ship what previously required a full multidisciplinary team (front-end, back-end, design, QA) in weeks rather than quarters. Second, it cites that 19% of all early-stage AI rounds in 2025 were raised by solo founders, which it notes is double the rate of 2023. Third, it highlights an $80M exit for Base44, a solo-founder AI startup sold six months after launch. This slide effectively sets the stage: AI has collapsed the cost of building, making solo-founder companies a viable asset class.

Slide 3: What is Shiva?

This slide defines the company as a 'community to boost obstinate entrepreneurs who are using AI to create global products.' It breaks the operations into two columns: 'What We Do' and 'What We Receive.' The fund plans to select 100 programmer-founders , paying monthly grants for up to 12 months with 'no upfront bet.' In exchange, Shiva receives up to 15% equity and a secondary right to sell up to 1/3 of their stake in each priced round. This is a crucial detail for investors, as it suggests a path to liquidity that doesn't rely solely on billion-dollar IPOs.

Slide 4: The Shiva Program

Slide 4 details the operational mechanics of the fund through three pillars. 01 Talent Scouting emphasizes proactive sourcing rather than open applications, citing the founder's experience hiring at companies like Méliuz and Ambev. 02 Mentorship & Pressure describes a high-intensity environment where founders set 'almost impossible' goals. 03 Fast Death of Ideas is the most distinct part of the model, stating a 12-month maximum runway and weekly reviews. The slide explicitly states that 'Speed of failure is a feature, not a bug,' signaling to investors that capital will not be wasted on stagnant projects.

Slide 5: Business Model

This slide uses a comparison table to contrast 'Traditional VC' with 'Shiva.' It highlights that Shiva targets $20M-$50M exits or profitable dividends as a win, whereas traditional VC requires IPOs or massive exits. The portfolio is described as 100 micro-startups that are 'diversified' and 'antifragile.' Team sizes are limited to 1-3 people , using AI as the 'operational substrate.' This comparison is designed to de-risk the investment by showing that Shiva doesn't need every company to be a unicorn to return the fund.

Slide 6: Built Global From Day One

Slide 6 addresses the geographic strategy. It notes that Brazil is only 2% of global GDP , making the global market 50x larger. It lists 'Speed of Anticipation,' 'Community Network,' and 'Platform & Knowledge' as the fund's moats. A small box at the bottom provides a proof point: FoxApply , a portfolio company, reached 20+ countries and 102 international customers in just 20 days of operation. This slide counters the potential concern that a Brazilian fund might be limited to a local, emerging market.

Slide 7: Who We Back

Shiva segments its target founders into three personas. Underdogs (45 founders) are young programmers from diverse backgrounds who receive a 'lower stipend.' Blacksmiths (45 founders) are experienced programmers from top startups who receive a 'mid stipend.' Camels (10 founders) are seasoned former CTOs or first engineers who receive a 'larger ticket.' This segmentation shows a sophisticated approach to talent acquisition, acknowledging that different levels of experience require different capital incentives.

Slide 8: Use of Proceeds -- US $10M Pre-Seed

This slide provides a transparent breakdown of how the $10M will be spent over 36 months. The largest allocation is $5.5M for Blacksmiths , followed by $1.8M for Underdogs and $1.6M for Camels . The core team and mentors (8 people) account for $0.7M , and G&A/Legal takes $0.4M . The bottom of the slide reiterates the 'milestone-based (stage-gate) model,' ensuring investors that the full $10M isn't committed upfront to unproven ideas.

Slide 9: The Founder

The team slide focuses exclusively on Lucas Marques . His credentials include being the Ex-COO at Méliuz , where he took the company from zero to IPO, and the founder of Programadores do Amanhã , an NGO that places 150 low-income students per year in tech jobs. He is also described as an Endeavor Entrepreneur for 5+ years. The slide uses his past success in scaling a company and his experience in talent development to validate his ability to lead this high-volume incubator model.

Slide 10: Join Us in Building What's Next

The final slide is the call to action. It restates the raise of US$10M in pre-seed capital and offers 'asymmetric exposure to the generative-AI boom.' It ends with a quote from Sam Altman : 'The next billion-dollar startup will have just three employees.' This aligns Shiva's specific micro-startup model with the broader industry vision held by leaders in the AI space.

What Works in the Shiva Deck

The deck excels at thesis-driven storytelling . It doesn't just say 'we invest in AI'; it argues for a fundamental shift in how software is built and how capital should be deployed as a result. By defining the 'era of the micro-startup,' Shiva creates a category where they are the primary player. The use of specific founder personas (Underdogs, Blacksmiths, Camels) is also a strong tactical choice. It demonstrates that the fund has a clear plan for sourcing and pricing talent, which is the most critical variable in an incubator model. Furthermore, the Use of Proceeds slide is exceptionally detailed for a pre-seed deck, providing a clear roadmap for how the $10M will be distributed across the cohort.

What is Missing from the Shiva Deck

The most notable omission is a broader team slide . While Lucas Marques has an impressive background, Slide 8 mentions a 'Core Team & Mentors' of 8 people. Investors typically want to see who these individuals are, especially in a model that relies heavily on 'expert mentorship' and 'proactive sourcing.' Additionally, while the deck mentions 'AI tools and cloud credits' as part of the offering, it does not specify any strategic partnerships with major cloud providers or AI labs, which are often a staple of AI-focused funds. Finally, there is no mention of the investment committee or the specific criteria used for the 'weekly reviews' and 'fast cuts,' leaving the actual decision-making process somewhat opaque.

What Founders Should Copy

Founders building investment vehicles or platforms should emulate Shiva's transparency regarding capital allocation . The bar chart on Slide 8 is a masterclass in showing exactly where every dollar goes, which builds significant trust with LPs. Another element to copy is the Business Model comparison table on Slide 5. Instead of just listing features, Shiva frames their model as a direct alternative to the 'flaws' of traditional VC (e.g., needing massive exits vs. $20M-$50M wins). This makes the investment feel like a strategic diversification rather than just another high-risk bet. Lastly, the inclusion of a real-world proof point (FoxApply on Slide 6) within a thesis-heavy deck provides the necessary evidence that the model can actually work in practice.