Venture investing in operationally and/or capital-intensive businesses requires a different foundation than traditional software or consumer models. At Logos, we use a simple, two-pillar framework for scalable disruption called Future Fundamentals. These pillars are: Quantifiable Value and Unit Economics. Together, they inform our evaluation of opportunities and our work with portfolio companies.

Quantifiable Value

Disruption starts with the delivery of superior value. We view Value = Quality / Cost, measured against incumbents and alternatives.

• Cost – Is the solution outright cheaper, or does it lower the total cost of ownership (upfront investment, ongoing spend, working capital, etc.)?

• Quality – Domain and product specific attributes such as throughput, efficiency, speed, yield, durability, uptime, precision, repeatability, resolution, ease of use, and/or safety. Identifying the most important product quality element(s) is an essential part of the analysis.

We group quality-over-cost advantages into three bands:

SpaceX’s drop in launch cost, Illumina’s reduction in genome sequencing cost, and NVIDIA’s GPU order-of-magnitude better performance per dollar than CPUs all reset industry baselines and ignited markets. Step-change gains, like Tesla’s three-fold reduction in EV sticker price or Astranis’ five-fold cheaper satellites, drive market capture. Incremental gains such as Wolfspeed’s modest EV range bump, Redwood Materials’ estimated 30 percent materials cost reduction, and Desktop Metal’s savings on niche parts have struggled to achieve material market penetration.

To achieve venture-scale growth, a business must deliver value that clearly outweighs the frictions of adoption. While value compared to alternatives is critical, the solution must also target a critical point in the customer’s business, either a primary economic lever or an operational bottleneck – a principle we call process centrality. When strong value is applied at critical choke points, it creates the urgency required for rapid adoption and scale.

Unit Economics

The excitement around capital-intensive businesses has obscured the fact that only those with exceptional capital efficiency can generate venture returns. When each dollar deployed drives disproportionate incremental growth, promising technologies become enduring businesses.

To assess capital efficiency, we look at the fully loaded unit economics of a solution which includes:

• Defining the unit of production (e.g. device, job, customer, site, geography, etc.)

• Determining the revenue per unit

• Attributing all costs to delivering a unit

  • Direct variable costs

  • Amortized fixed asset costs

  • Allocated fixed operational overhead

Strong unit economics drive attractive returns to equity, both in unlevered and levered contexts. They need not exist at inception, but must be reasonably attainable as the business grows. Ideally, fully loaded unit economics should be predictable and demonstrate a path towards 50%+ margins. This enables rapid (<2 years) capital recycling and supports non-linear scaling. Predictable economics also unlocks the potential for financing that further accelerates the cycle times of equity capital. Margins above 30% can still be viable, but often require more equity and lead to greater early-investor dilution.

Understanding both the path and the destination of an economic model is essential. The journey from early unit costs to at-scale economics often follows Wright’s Law, with costs falling and quality improving as cumulative production grows.[1]

Three questions frame scalability: Where is the company on its development curve? How far and how quickly can it move along the curve? Which levers, such as innovation or process advantage, will drive that progress?

Other Considerations

Quantifiable Value and Unit Economics do not live in isolation. Greater value creation accelerates adoption, shortens the sales cycle, lowers CAC, and increases the share of value captured.

Even when a company appears disruptive, there are additional dynamics that separate mediocre businesses from great ones.

Understanding the True Benchmark

A common mistake is benchmarking solutions against the replacement cost of existing systems. The correct comparison is often the marginal cost of the installed base, not the replacement cost. Ignoring this can create the illusion of cost advantage when the solution is uncompetitive. Asset replacement cycles, the useful life of existing systems, and the switching costs or residual value will define the adoption window and inform how the product should be priced relative to incumbent options.

Commodities and the Supply Curve

When a company produces a commodity, long-term defensibility and margin potential rest on being the lowest-cost producer. Competing purely on price typically requires durable cost advantages driven by scale, supply chain efficiency, process innovation, or access to advantaged resources. A strong cost position relative to peers allows a company to stay profitable even in down cycles. On the wrong side of the supply curve, the market sets the price, not the company.

If durable cost leadership is unattainable, the only viable alternative is defensible differentiation. This can come from product features that customers care about, such as superior reliability, lower maintenance needs, faster delivery times, or better integration with existing systems. At Logos, this is a critical part of our assessment of energy investments.

Vertical Integration and the Jobs to be Done

A common failure is to mistake a novel technology for a business model. A breakthrough creates outsized value only when applied to a meaningful profit pool, the point in the value chain where the majority of margin is captured. Branded products, critical components, manufacturers with limited supply, or specialized capabilities often extract outsized margins. We assess:

- Where profit pools live in an industry and how they might shift with the introduction of disruptive technology

- What jobs need to be done to deliver and capture value, and decide where the business must integrate or outsource

The goal is to maximize profitability. Sometimes that means taking on more operational responsibility to ensure performance, utilization, or customer ownership. Other times it means resisting the urge to over-build. This is an ROI question where the incremental investment and effort must be compared to the incremental economics captured by integrating another job/part of the value chain.

Future Fundamentals

Future Fundamentals is not a rigid formula but an operating lens. At Logos, we rely on it twice: first, to assess opportunities during diligence; and again, to engage the founders we back. The framework surfaces the economic and technical levers that determine whether a company can scale in capital‑intensive, real‑world sectors. Hardwiring these fundamentals early helps founders avoid costly course corrections as growth accelerates.

Logos

[1] Nagy, B., Farmer, J. D., Bui, Q. M., & Trancik, J. E. (2013). Statistical Basis for Predicting Technological Progress. PLoS ONE, 8(2), e52669. https://doi.org/10.1371/journal.pone.0052669

h/t Roxanne Tully Baine and Claire Goldsmith

Highlights

1. Instant Context & Memory: Years of nested folders, version histories, and document metadata give an AI assistant deep context on user and corporate workflows. Rather than building a file store from scratch, OpenAI could plug into Dropbox’s opt‑in data pipelines and get immediate access to personal and enterprise documents—fuel for personalized models that remember past conversations, drafts, and analyses. People have loved the memory capabilities recently launched by OpenAI. Imagine those but with the last 15 years of your saved data and work.

2. Built‑In Distribution Engine: 700 million registered users (18 million paid and 575K businesses)

3. Exabyte‑Scale Backbone: Dropbox’s Magic Pocket system operates multi‑exabyte storage across proprietary data centers (handling the bulk of user data) and a global edge network. Acquiring this would spare OpenAI years of capital spending and ops build‑out, letting it focus on product.

4. Ecosystem Neutrality: As a platform‑agnostic hub—integrated with Office, Google Workspace, Slack, Zoom, and hundreds of third‑party apps—Dropbox lets OpenAI’s tools plug seamlessly into diverse corporate stacks, avoiding lock‑in and maximizing reach.

5. Defensive: Preempt other players from securing this strategic asset.

Considerations

• User Trust & Churn Risk

  • Risk: Fears over “OpenAI owning my documents.”

  • Mitigant: Historically, Dropbox users have stayed through acquisitions when new capabilities outweighed concerns. If OpenAI enforces strict opt‑in AI usage, honors existing privacy commitments, and demonstrates clear productivity gains, churn should be minimal—and those who do leave are a small fraction of the newly AI‑empowered base.

• Enterprise Compliance & SLAs

  • Risk: Large corporations worry about data governance.

  • Mitigant: OpenAI would inherit Dropbox’s ISO/SOC certifications, HIPAA/GDPR compliance, and existing SLAs. A clear pledge to maintain these standards—and to operate Dropbox as a standalone trust‑neutral subsidiary—smooths the path.

• Financial & Execution Load

  • Risk: A $8–10 billion price tag and running a global storage business.

  • Mitigant: Dropbox is net‑debt‑zero and generates $700–800 million of free cash flow annually. Its infrastructure is already lean—OpenAI’s acquisition would be immediately accretive, funding further R&D and share repurchases.

Key Data Points (Updated - May 2025)

• Scale & Distribution: 700 million registered users, 18 paid seats, 575K businesses

• Data & Infrastructure

  • Multi‑exabyte storage (Magic Pocket)

  • Hybrid cloud: 90% on proprietary data centers, edge network for global performance

• Financial & Ownership

  • Enterprise Value: ~$1.4 B; EV/Revenue: ~4.1×; EV/FCF: ~12.1× (CF from Ops less Capex)

  • Voting Control: Drew Houston holds ~77% voting power via dual‑class shares

  • Ownership Split: 75.7% institutional, 6.2% insiders, 18.1% retail

Trusted data scale, enterprise‑grade storage infrastructure, and a global distribution engine—all primed for immediate AI integration. Acquiring Dropbox could be the most direct path from powerful models to transformative, memory‑enabled AI assistants.

The incentive for humans creating and sharing original content will decline as it becomes more challenging to breakthrough the noise and guaranteed to be scraped by AI models for further training. A doom-loop emerges: AI-generated content proliferates, authentic human content recedes, and the open internet gradually becomes less authentic and useful—a phenomenon sometimes referred to as the “dead internet.”

In response, users may migrate towards trusted, smaller networks characterized by stronger identity verification and selective or gated access. We’ve seen early evidence of this trend across social media (to private messaging)1, forums (to invite-only communities), and ecommerce (to group-buying, live-streaming, and DTC storefronts). While these shifts point to a broader movement, large segments of the internet remain unchanged and vulnerable to degradation from unchecked AI content.

Degradation is the most benign interpretation of what is currently taking place. The internet as a mechanism for predatory behavior is growing rapidly as crime organizations professionalize. AI supercharges this challenge. Sue-Lin Wong (The Economist) has done excellent reporting on internet fraud. Please read or listen to her Scam Inc series and share it with anyone for whom you care.

Informal solutions, such as industry Slack groups or private job boards, currently provide interim relief. However, there is a significant opportunity to build businesses around trusted networks, leveraging strong identity verification and targeted, closed-network dynamics. Companies able to offer compelling services within these networks could quickly establish leadership positions, subsequently expanding their offerings into workflow automation, AI-assisted coordination, and vertical-specific software solutions. We believe this is a second-order way to create vertical AI-powered software businesses.

There is enormous potential in companies solving this trust and truth gap and are starting to invest behind this hypothesis. As AI-generated noise increases, trusted networks will become essential infrastructure for preserving discovery-driven value creation.

This fails to capture the full story. While operational labor replacements will be a temporary value creation mechanism, AI point solutions will eventually compete against similar software offerings and have limited standalone moats. Competition (and subsequent margin compression) is accelerated by AI, as code generation enables rapid software development and replication.

Companies will need to deliver both lower costs and increased product quality to capture and maintain material take rates. AI that replaces labor can and will drive cost savings, but only those businesses that offer transcendent and differentiated product quality will build enduring moats. To achieve these outcomes, rapid feature/product expansion (beyond initial labor savings features) and the creation + usage of previously unusable/unavailable data will be required. With this data, the correct positioning in the value chain, and the features enabled by foundation models, companies will be able to drive incremental volume, revenue, and/or product margin, making their products an inextricable part of a customer’s business.

Therefore, we believe the largest opportunities exist in companies that focus on specific verticals where unique data can be combined with industry insights to deliver meaningfully better customer experiences. Even the best AI models generally do not contain the industry expertise or context necessary for the best outcomes. Vertical businesses can connect to legacy systems to extract and leverage existing data, drive downstream operations, and, over time, expand feature and product offerings that span the full value chain.

An example (that conveniently talks the Logos book) is Mia, which provides AI-powered automotive retail software. At first glance, Mia is a straightforward replacement of labor with its AI-powered customer service for automotive dealers. Alone, this first product solves a material labor and cost challenge for dealerships. Mia’s AI-powered customer service provides 24/7 coverage and connectivity to additional dealer systems that enable automated and seamless downstream operations including scheduling, test-drives, inventory management, service optimization, and financing administration. However, Mia sees these customer interactions as a wedge to capture customer data and build a machine deliver outbound customer communications that deliver high ROI customer retention and marketing interactions. Dealerships spend $500K+ annually1 on marketing across channels to refill the sales funnel due to low consumer retention rates (i.e. repeat purchase) of ~30%2. Successful delivery of these services can make Mia an invaluable part of the full dealer value chain and can transform both the processes and economics of consumer acquisition and retention.

This perspective is not in keeping with the historical outcomes of software. Total software market capitalization in the United States is $6T+3, with the top fifteen vertical software businesses only comprising ~$300B (<5%)4. IT budgets in most industries are <10% of revenue5, with vertical SaaS spend (where applicable) often <1% of customer revenue. As software replaces headcount, we expect to see material expansion of IT budgets driven by vertical software offerings.

The shift toward vertical AI businesses—those that leverage unique industry data to create differentiated, deeply embedded products—will redefine traditional IT budget constraints and significantly expand the total addressable market for software. Identifying these businesses early, as they transform operational economics, is central to our investing approach.