The State of MSP Capital in the Age of AI (Investor Edition)

Forward

Managed services is a cornerstone of the IT industry and channel. Over the last 20 years, the managed services model, adopted and delivered by managed service providers (MSPs), has evolved from its humble origins as a rudimentary replacement for break-fix services to today’s virtual CIOs. Its value lies not only in technology delivery, but in recurring revenue durability, embedded customer relationships, and the operational control points that shape how AI and automation enter real businesses.

Today, and in the near future, the industry stands at a defining moment. Private equity, venture capital, and corporate capital are reshaping market structure, while AI is transforming MSP economics — from labor-based service delivery models to software-enabled, data-driven managed intelligence. This shift is not incremental; it introduces a fundamentally different basis for scale, margin expansion, and defensibility.

This report reflects a year of analysis and dialogue with investors, operators, and technology leaders. It offers a clear view of the market as it exists in early 2026 and what the next decade may yield as automation, data leverage, and governance redefine competitive advantage.

For Top Down Ventures, the mission is straightforward: align capital with capability, accelerate the modernization of the MSP ecosystem, and illuminate the long-term value creation potential of an industry that has only begun to convert its strategic position into investor-grade returns.

We trust this paper will help investors sharpen their theses, identify where compounding value is most likely to accrue, and recognize the MSP economy as a maturing yet still under-capitalized opportunity with meaningful upside in the years ahead. This is more than a state-of-the-industry report; it is a guide for navigating the future of capital allocation in managed services over the near and long term.

Joel Abramson, Managing Partner

Mark Scott, General Partner

Top Down Ventures

MSPs Matter to Capital Markets

Managed service providers occupy a distinct position in the global technology economy, blending recurring revenue durability with embedded customer relationships. The sector’s current value is approximately $595 billion and is projected to exceed $950 billion by 2030, reflecting a 9.8% CAGR. Growth is supported by rising IT complexity, heightened cybersecurity requirements, and expanding data governance needs across the small and medium-sized business (SMB) market.

Scale alone explains part of the sector’s importance, but the underlying economics further elevates MSPs from tactical vendors to investment-grade assets. Gross retention rates consistently above 90 percent and average contract terms of 3 to 5 years provide predictable cash flow and resilience against market volatility. At the same time, the SMB technology market is undergoing a structural shift. For the first time, SMB IT spending growth is outpacing enterprise IT investment, creating a demand environment in which MSPs hold privileged distribution access. Their reach into millions of businesses is difficult and costly for vendors to replicate directly.

Artificial intelligence reinforces this position. Although AI automates labor-intensive processes, it does not replace the governance, integration, compliance, and operational oversight that MSPs deliver. AI instead broadens the scope of managed services — from traditional endpoint support to managed intelligence characterized by predictive remediation, workflow automation, and outcome-based delivery. Early adopters are demonstrating measurable margin expansion as automation increases capacity without commensurate headcount growth.

For investors, the thesis is clear: MSPs convert global AI investment into recurring economic value. Hyperscalers and software vendors can develop models and platforms, but MSPs operate them for end-customers. Every dollar invested in AI infrastructure ultimately requires managed security, deployment, and lifecycle management.

This convergence of market scale, durable economics, AI-driven efficiency, and strategic positioning makes MSPs a compelling asset class with the potential for compounding returns in the decade ahead.

Market Structure and Capital Epochs

Over the past 25 years, the industry has evolved through 3 foundational phases that continue to shape the market today. A fourth phase - what we refer to as the Next-Gen MSP - is in its early stages, but in our view, will define the industry’s trajectory for the next 25 years.

The shift from technical maintenance to strategic enablement has elevated managed services from a cost center to a capital asset. For investors, this evolution presents a paradox: the industry is mature in size, yet early in value realization. The next wave of returns will not be driven from rolling up fragmented providers, but from monetizing the intelligence within them. The question is no longer how many endpoints an MSP manages, but how much actionable insight its data can generate.

The MSP capital landscape has evolved through three distinct epochs, each reflecting broader shifts in technology adoption and investor priorities.

• EPOCH 1 — 2000 to 2009: MSPs were largely bootstrapped operations, transitioning from break-fix models to recurring service contracts. Capital, where present, came from bank loans, angel investors, and vendor rebates. Growth was local, linear, and talent-driven. Scale was constrained, but margins were stable and predictable — a foundation that helped establish the credibility of the managed services model.
• EPOCH 2 — 2010 to 2020: Financial sponsors drove consolidation, centralizing back-office operations, standardizing service offerings, and creating platform MSPs with regional and national reach. Valuations reflected expectations of scale: Revenue growth and geographic footprint translated into higher multiples, even when operational efficiency lagged. Yet, as more roll-ups reached maturity, diminishing returns emerged.
• EPOCH 3 — 2021 to Present (and Beyond): Investors are now rewarding MSPs for their ability to convert AI-based automation into margin and capacity expansion, rather than simply aggregating revenue. This shift is visible in valuation divergence: AI-enabled service and software firms command materially higher multiples than legacy MSPs reliant on labor-centric models.

Correspondingly, capital priorities are shifting from CapEx-driven scaling to OpEx-driven restructuring. Instead of funding headcount growth or regional expansion, investors are underwriting modernization: Workflow automation, integration of AI agents, and transformation of service portfolios. Efficiency now outweighs raw growth as the primary driver of enterprise value.

MSPs are regaining relevance as the distribution layer for AI adoption. SMBs lack the internal expertise and capacity to operationalize AI effectively and at scale. MSPs provide the integration, compliance, and lifecycle management required to translate AI capabilities into real outcomes. This positioning — embedded with customers, trusted as advisors, and increasingly augmented by automation — cements MSPs as a critical component in the broader AI economy and reshapes how capital approaches the sector going forward.

Macro Trends Reshaping Investment Strategy

Artificial intelligence is now the single largest force reshaping capital allocation and valuation dynamics across the technology ecosystem. The divide between AI beneficiaries and legacy software models is widening, creating a new premium for companies that can demonstrate verifiable productivity gains.

According to CIBC Capital Markets, AI-native firms trade at 10x to 12x forward revenue, while traditional SaaS companies tied to seat-based licensing trade at 5x to 8x. This valuation gap is increasingly influencing investor screening, underwriting, and post-acquisition strategy; efficiency and leverage now matter more than top-line momentum.

MSPs sit at the center of this structural shift. Their role as custodians of operational context, workflow data, and compliance telemetry makes them a critical part of how AI operates in real businesses.

AI engines require context — configuration baselines, device signals, historical remediation data, and user patterns — to operate. MSPs own this context, which transforms their service platforms into data moats that vendors and software companies cannot replicate easily. As intelligence becomes the defining competitive advantage, this operational layer becomes the primary conduit for value creation.

These dynamics are driving capital toward automation, governance, and defensible data assets. Investors are shifting away from scale-only consolidation plays and toward operators or software platforms that can demonstrate automation yield:

• Cost reduction per ticket
• Hours saved per endpoint
• Margin improvements
• Service velocity

AI’s impact on public markets reinforces this trend. Generative AI contributed $22 trillion to U.S. equity market capitalization and pushed global AI spending to $1.5 trillion annually, a signal that capability leverage — not headcount — is now the determinant of value.

Institutional capital is responding accordingly. Infrastructure funds, sovereign wealth funds, and large pension allocators are entering the segment, recognizing that MSPs represent a scalable AI distribution layer for more than 100 million SMBs globally. MSPs are emerging as a critical asset class that converts AI CapEx into recurring productivity gains.

MSPs are no longer service businesses; they’re becoming the operating layer of the AI economy.

The Diligence Playbook for Managed Services Investments

Evaluating MSPs and MSP-focused software companies in the AI era requires a shift from traditional diligence frameworks to models that emphasize capability leverage, automation, and defensibility. The sector’s unit economics are being reshaped by AI-driven productivity, telemetry-rich workflows, and the widening gap between operators that can convert intelligence into margin expansion and those that cannot.

Investors who incorporate these criteria into underwriting will be better positioned to capture the upside of the industry’s next phase. The following guidance highlights the attributes that signal strong investment potential and the red flags that indicate heightened risk or limited scalability.

What to Look For

• Automation maturity and efficiency: Look for measurable reductions in cost-to-serve and faster service outcomes, such as improved ticket resolution or workflow automation rates. Evidence shows that when automation reaches roughly 20–25% of recurring workloads, incremental gains can deliver notable margin improvement and accelerate deployment cycles.
• Data leverage and context advantages: MSPs control remediation data, device telemetry, configuration baselines, compliance artifacts, and vertical-specific operating patterns. These assets form defensible moats because AI systems require context to function. MSPs with stronger data pipelines — or software platforms that enable data normalization across customers — are best positioned to create compounding value.
• Governance readiness: As AI systems become embedded in critical operations, diligence must assess auditability, risk-management controls, regulatory adherence, and cyber insurance posture. Platforms that can demonstrate traceability, explainability, and compliant data-handling processes will command premium valuations.
• Learning velocity: Investors should measure how quickly an MSP or software platform improves from one cycle of automation to the next. Higher learning velocity — as evidenced by shorter deployment times, lower error rates, or greater predictive accuracy — is a reliable indicator of compounding capability.
• Customer concentration and expansion economics: Retention remains high across the sector, but expansion revenue, net dollar retention, and vertical depth now differentiate high-quality operators. Investors should prioritize providers with diversified customer portfolios and verifiable expansion paths.

Investor Red Flags

• Tool sprawl without orchestration: Multiple overlapping tools without integration signal operational inefficiency and limited automation potential.
• Roll-up scale without operational transformation: The previous epoch proved that consolidation alone cannot produce differentiated returns. Compressed buyout performance despite revenue growth demonstrated the limits of scale-only models.
• Dependency on seat-based licensing: As agentic automation reduces end-user seat counts and repetitive labor, companies reliant on traditional per-seat economics face structural risk.
• Lack of automation metrics or model governance: A failure to quantify automation impact — or to govern AI models — indicates shallow capability maturity and limited defensibility.

These due diligence factors reflect the new realities of the MSP capital landscape, where efficiency, intelligence, and data compounding — not scale — will determine long-term value creation.

Metrics for the AI-Enabled MSP

Between 2015 and 2021, the market rewarded top-line expansion. The mantra was “grow at all costs.” As of 2025, the model has inverted. The metric that matters now is automation-adjusted Rule of 40:

Revenue Growth + Automation-Driven Margin Expansion

Providers achieving a combined score above 60 are commanding premium valuations.

Capital Value Chain 2.0 The MSP capital stack has evolved to this:

Each stage funds the next proof point. Capital no longer flows sequentially but cyclically: venture creates capabilities, private equity operationalizes them, and public markets re-rate the winners. This feedback loop compresses innovation cycles and raises the diligence bar for founders and investors alike. While the MSP and Managed AI ecosystem is still too young to show a full venture-to-public lifecycle, parallels exist in the broader enterprise-software market. Companies such as ServiceNow and Salesforce have repeatedly acquired early AI innovators, operationalized their technologies, and then been re-rated by public markets as those capabilities matured. Similar dynamics are now emerging in the MSP software space, where AI-native tools are progressing from seed funding to platform integration under private-equity ownership.

The old rules and metrics for measuring MSP value and potential are done. AI is changing not just the way AI-empowered MSPs operate, but how they generate value to customers, vendors and investors. To understand the true value of an MSP, investors need to look beyond the common key performance indicators to next-generation metrics that reflect true value and future potential.

• Automation-adjusted Rule of 40: The post-growth model pairs revenue expansion with automation-driven margin improvement, replacing the historical emphasis on top-line progression alone. Providers that demonstrate autonomous workflow penetration and documented cost-to-serve reductions receive premium valuation treatment.
• Learning rate coefficient: This multiplier assesses how quickly an MSP compounds capability. Evidence suggests that once automation covers 20–25% of recurring workflows, incremental gains deliver margin leverage and accelerate deployment cycles. In this model, learning velocity becomes a predictor of future enterprise value.
• Governance integrity coefficient: As AI introduces new risk vectors — model drift, compliance, explainability — the presence of auditable governance frameworks and risk controls becomes valuation-accretive. Governance is no longer overhead; it is a prerequisite for defensibility.

These multipliers directly tie to the three premium drivers defining the AI-enabled MSP:

• Margin expansion driven by converting labor-based workflows to autonomous processes.
• Positioning on the autonomy adoption curve, which determines future efficiency and scale.
• Data moats and defensible workflows — the operational context MSPs control that AI models require to deliver real-world outcomes.

In short, valuation leadership will accrue not to the largest MSPs, but to those demonstrating the greatest compounding capability — efficiency, intelligence, and governed data leverage.

Capital Stack & Liquidity Landscape

The AI-enabled MSP ecosystem is attracting capital from every tier of the investment stack, with each layer validating different aspects of capability, scale, and durability. At the early stage, angels and seed/venture funds focus on proof of capability—whether a platform can deliver measurable automation, data leverage, or managedintelligence outcomes. This is where AI agents, orchestration tools, and MSP-focused software are initially tested. Between 2023 and 2025, AI-focused venture rounds absorbed roughly $440 billion globally, yet only about 30% of funded companies achieved operational profitability, underscoring the premium on real-world utilization over model sophistication alone.

Growth equity enters once scale becomes demonstrable. These funds look for repeatable go-to-market motion, channel leverage, and the ability to extend AI-enabled services across broad customer bases. For MSPs, that typically includes regional expansion, vertical specialization, or service catalog enlargement supported by established partner ecosystems.

Private equity increasingly plays the role of efficiency arbiter. Roughly $13 billion in dry powder targeted MSP consolidation by 2020, but compressed returns despite revenue growth have shifted the thesis. Modernization - automation, governance, and margin uplift - now replaces scale as the primary value driver, with automation-adjusted metrics becoming central to underwriting.

Secondary markets and return pathways are also broadening. Continuation vehicles and structured secondaries allow sponsors to extend hold periods for assets exhibiting automation flywheels and recurring revenue resilience.

Exit routes are diversifying, quickly. Traditional roll-ups persist, but success hinges on operational transformation; strategic buyers seek MSP platforms for AI distribution; and infrastructure-style and sovereign funds are beginning to classify scaled, automation-rich MSPs as quasi-infrastructure assets - predictable, cash-generative, and foundational to the emerging AI economy.

The Managed AI Transition — Structural Implications for Investors

Thinking of MSPs as the providers of remote monitoring and management of infrastructure and application is easy and apparent, but doesn’t tell the whole story. Today, MSPs are evolving because of AI and the other advanced, emerging technologies. Their mission rapidly becomes one of helping their customers transform and leverage these new technologies.

Pax8, an MSP marketplace and support provider, calls this concept Managed Intelligence Provider (MIP). From the Pax8 perspective, one with which Top Down agrees, MSPs are moving from reactive technical support to orchestrating AI agents, curating vertical solutions, and managing compliance as a core service.

In this model, MSPs no longer simply maintain infrastructure; they deliver intelligence as a managed outcome. Managed AI Services are on track to become as mainstream as managed security services over the next 3 to 5 years.

This transition is reshaping the economics of labor. Digital labor mirrors human labor in cost structure but scales very differently. Once trained, each AI agent operates at nearzero marginal cost. Our modeling shows that a mature Managed AI Service Provider running a fleet of approximately 500 active agents can perform work comparable to roughly 50 human technicians at a fraction of the variable expense.

Even pricing automation at a 50% discount to human rates can still double gross margins, depending on workload mix and governance overhead. At the same time, model licensing and GPU costs introduce new fixed expenses, making model selection and inference efficiency financial decisions, not purely technical ones.

Differentiation in this environment will come from verticalization and specialization. MSPs and platforms that align AI agents, workflows, and compliance frameworks to specific industries — such as healthcare, financial services, or manufacturing — build defensible domain expertise and data adjacencies. Data breadth and interoperability, not brand alone, increasingly determine deployment speed, model accuracy, and customer trust.

The transition also reinforces platformization and the role of MSP software control planes. As Top Down and Tidemark describe, the next generation of platforms will serve as the “system of action,” unifying system-of-record data with intelligent execution across tickets, security events, and business workflows. For investors, control plane ownership becomes a key indicator of strategic positioning and ecosystem leverage.

With this transition, monetization is shifting toward AI-native pricing models. Top Down sees four emerging constructs:

• Per agent
• Per action
• Per workflow
• Per outcome

These constructs, with hybrid contracts that mix fixed retainers and outcome-based bonuses, will capture up to 20% more value when tied to verified ROI. As MSPs evolve into Managed Intelligence Providers, pricing will increasingly reflect the value of digital labor and outcomes delivered, rather than time and materials — directly impacting revenue quality, margin structure, and valuation potential.

Regional Investment Landscape

The global managed services market is large, but it isn’t the same everywhere. Regional differences in maturity, technical capability, and profitability mean that valuation varies significantly by location. Investors should understand these differences when evaluating opportunities.

North America is the most mature and capital-rich MSP market. The focus has shifted from simple roll-ups to automation and platformization. MSPs and vendors that can show automation-driven margin gains receive higher valuations. Examples such as New Charter, NinjaOne, Slide, and Pax8 show how unified platforms and marketplaces can create scale and efficiency advantages. As a result, North America continues to attract the most investment and sets the benchmark for AI-enabled MSP models.

Europe and the broader EMEA region are driven by compliance demand. Regulations such as NIS2 and DORA are forcing mid-market companies to adopt managed services, often using MSPs as outsourced compliance functions. These services typically generate margins five to eight points higher than basic infrastructure management. Funds like EQT and Perwyn are building regional platforms to capitalize on this regulatory pull.

Asia-Pacific is more diverse, but the common theme is marketplace adoption and sovereign-cloud requirements. Hyperscalers, telcos, and distributors are delivering AI services as bundled offerings, which often introduce SMBs to managed AI for the first time. Australia and Japan lead with sovereign-AI initiatives, while emerging markets use AI to close skills gaps. Valuations trail North America by one to two turns of EBITDA, but investment is increasing.

Latin America is less mature, but it offers near-shore cybersecurity talent and cost advantages. MSPs can serve North American clients at 30–40% lower prices while meeting service expectations. Currency risk complicates valuation but creates favorable entry points.

Globally, margins and valuations are beginning to converge. As automation lowers costs, profitability is expected to trend toward the high teens, with scaled AI-enabled MSPs clustering near 9x EBITDA. This creates timing advantages—acquiring earlier in Europe, APAC, and LATAM could yield North American-level valuations over time.

Risk, Governance, and Regulation as Capital Determinants

As managed services evolve into managed intelligence, regulation and governance are no longer peripheral considerations; they are central to capital allocation, valuation, and exit potential. The next decade of MSP investment will be defined not only by automation and data leverage, but by the ability to operate within emerging regulatory frameworks and translate compliance into value.

The European Union’s AI Act is the most comprehensive articulation of risk-tiered oversight, requiring documentation of model logic, auditability, and human override mechanisms. In parallel, NIS2 and DORA mandate continuous monitoring, incident reporting, and third-party risk management across critical sectors by 2026.

These standards elevate MSPs from tactical service provider to quasi-governance and regulatory compliance (GRC) operators. Compliance-centric offerings by MSPs typically earn gross margins 5 to 8 points higher than traditional infrastructure services - a tangible valuation uplift directly tied to regulatory competence.

Regulation also amplifies liability and model risk. As AI agents assume more operational tasks, error attribution, model drift, and explainability become insurable events. Top Down sees a growing “middleware” layer around warranty, outcome tracking, and compliance telemetry - a precursor to insurance markets that will monetize risk transfer. Investors should expect premium pricing and stronger retention for MSPs capable of delivering verifiable audit trails, decision logging, and board-level reporting.

Governance maturity now reduces risk discounts and expands valuation multiples. Under Quality of Annual Recurring Revenue (QARR) models, governance integrity can lift enterprise value by 15% to 25% without increasing revenue. Conversely, weak model governance invites discounts, regulatory exposure, and litigation risk - particularly when MSPs manage sensitive sectors such as healthcare or finance.

For MSP investors, governance is no longer compliance hygiene; it’s a valuation engine. Providers that treat risk controls, auditability, and sustainability as core product attributes will command premium multiples, lower cost of capital, and stronger exit optionality in markets where trust is a prerequisite to scale.

Strategic Implications for PE, VC, and Angel Investors

The managed services market is shifting from traditional IT support to becoming a key distribution and orchestration layer for applied AI. This changes where value is created and how investors should evaluate and build MSP portfolios.

For private equity, the focus must move beyond roll-ups. Scale alone no longer delivers returns. The next wave of PE-backed MSPs will need shared automation, unified platforms, and common data architectures that drive margin gains and lower operating costs. The priority is not size, but smarter and more efficient operations.

For venture capital, MSPs offer a ready-made channel for AI adoption. They provide access to SMB and mid-market customers, along with the context and data required to deploy AI tools effectively. MSPs should be viewed as distribution partners, not end markets. Investments in middleware, orchestration, automation, and governance tooling stand to benefit most.

For crossover investors, the opportunity lies in building and owning platforms that unify telemetry, billing, security, and automation—control planes that guide both human and AI-driven work. These positions offer defensible moats through data, ecosystem influence, and pricing power.

For corporate venture capital, there is a chance to shape emerging standards in compliance, interoperability, and responsible AI. Backing technologies that support auditability and policy enforcement can set the baseline that others follow.

Ultimately, the winning investors will be those who see MSPs not as service shops, but as the operational and governance layer for AI. The strategic edge will come from intelligence, automation, and platform leverage - not raw scale.

2030 Outlook and Scenario Planning

The next decade will see managed services shift into managed intelligence — a model defined by digital labor, governed automation, and steady productivity gains. Top Down’s scenario planning outlines three paths to 2030, each with different expectations for growth, margins, and valuation.

The Base Case, assigned a roughly 55% probability, assumes steady AI adoption and maturing governance standards. In this outcome, the global managed services market reaches about $1 trillion, EBITDA margins converge around 16–18%, and valuations settle between 8–10x ARR. M&A activity remains strong, and the IPO window gradually reopens. This scenario emphasizes efficiency over explosive growth, with automation raising margins while maintaining workforce stability.

The Acceleration Case, with about a 30% probability, anticipates faster adoption — AI agents performing more than half of recurring workloads — along with supportive regulation and declining compute costs. Under these conditions, the sector could grow to approximately $1.3 trillion, achieve EBITDA margins above 20%, and see leading companies command valuations greater than 12x ARR. In this world, managed intelligence takes on characteristics of infrastructure: predictable, cash-generating, and attractive to sovereign and infrastructure investors.

The Compression Case, assigned around a 15% probability, reflects regulatory fragmentation, energy constraints, or public resistance. Growth slows to roughly 8% CAGR, margins flatten, and capital partially shifts toward energy and cyber infrastructure. Even then, the sector remains cash-flow positive, showing its resilience.

Across all three scenarios, a key driver is the productivity flywheel. Incremental automation spending can yield outsized productivity gains — often 8–12% within 12–18 months. Once MSPs automate about 20% of recurring workloads, the cost savings and margin gains help fund further automation. By 2030, an estimated 40% of mature-market MSPs could reach this point.

The overarching conclusion: AI will transform MSP economics, and the main variable is speed. Regardless of scenario, managed intelligence becomes a core means of accessing SMB productivity with defensible governance and compounding efficiency.

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Acknowledgments

Top Down Ventures extends its thanks to the research and investment community that informed this report. Special appreciation to CIBC Capital Markets (Daniel Lee), Tidemark Capital (David Yuan), Pax8 (Scott Chaison, Nick Heddy), Canalys (Jay McBain), GTIA (Dan Wensley), and William Blair for their published analyses and collaboration through industry forums.

A very special thank you to Larry Walsh and the Channelnomics team for their editorial support and contributions.

The authors also recognize the operators and founders who shared data and experience from the front lines of the Managed AI transition. Their insights made this paper a reflection of practice as well as theory.

All interpretations and conclusions are the independent judgment of Top Down Ventures.

Sources and Methodology

1. Channelnomics MSP & AI Research (2023-2024). State of Artificial Intelligence in the Global IT Channel.
2. CIBC Capital Markets (2025). Gradient Ascent 3.0 – Navigating the AI-Driven Divergence in SaaS Capital Markets; Daniel Lee essays on AI and Software M&A.
3. Tidemark Capital (2025). The Race to Become the System of Action Parts I & II; AI Playbook.
4. Pax8 (2025). The Agentic Inflection Point.
5. Canalys (2024 – 2025). MSP Trends and Predictions 2025; Now and Next for Cybersecurity Managed Services 2024.
6. CompTIA (2024). State of the Channel 2024.
7. GTIA (2025). State of the Channel 2025 and SMB Technology & Buying Trends 2025.
8. William Blair (2020). MSP Market: Entering the Golden Age.
9. Precedence Research (2025). Managed Services Forecast 2025–2034.
10. Analysys Mason (2023 – 2024). Regional managed services benchmarks.
11. PitchBook (2024). Global PE Deal Multiples Q4 2024.
12. Stanford HAI (2025). 2025 AI Index Report.
13. SVB / PitchBook (2025) – State of Corporate Venture Capital 2025.
14. McKinsey Global Institute (2023). The Economic Potential of Generative AI.
15. PwC (2023). Global Artificial Intelligence Study: Sizing the Prize.
16. Accenture (2024). Reinventing Enterprise Operations
17. IMF (2025). World Economic Outlook.
18. Gartner (2024). Gartner’s SOAR Market Guide.
19. Frost & Sullivan (2024). Latin America Managed Security Services Market, Forecast to 2027.
20. Top Down Ventures (2025). Fund Briefings, Super-Max Cyber Framework, MSP Outliers Blog Series, and Proprietary Capital Models.

Methodological Notes

Quantitative Data drawn from public market indices, private fund disclosures, and Top Down portfolio benchmarking (2023–2025). Forecasts use a weighted CAGR model based on Canalys and Precedence Research baseline growth rates. Qualitative Insights compiled through interviews with 37 operators, 15 LPs, and 10 industry analysts across North America, EMEA, and APAC. All financial figures are presented in USD. Percentages rounded to the nearest whole number. All forecasts are subject to standard Top Down assumptions on capital cost (9% discount rate) and FX neutrality.