Building Value in the Next Phase of the Economy

Written by Michael Burnett

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This document is provided for informational and educational purposes only. It does not constitute a financial promotion, investment advice, or an invitation or inducement to engage in any investment activity within the meaning of the Financial Services and Markets Act 2000 (FSMA). The content herein should not be relied upon as a basis for making any investment decision.

The views and opinions expressed in this document are solely those of the authors and do not necessarily reflect those of any affiliated parties, companies, or investors. While care has been taken to ensure the accuracy of the information at the time of writing, no representation or warranty is made as to its completeness, accuracy, or ongoing relevance.

Readers should not construe any part of this document as regulated advice. We strongly recommend seeking independent financial, legal, and tax advice from qualified professionals before taking any action based on the information provided.

Any references to companies, technologies, or market trends are provided as part of a general market overview and should not be interpreted as endorsements or investment recommendations.

Foreword: Navigating the Inflection Point

By Michael Burnett, Managing Partner, Fuel Ventures

Dear Reader,

As we all start to find our rhythm in 2026, I’ve found it valuable to first pause and reflect on markets, business and life more broadly. This year, I thought it would be helpful to share some reflections in this letter to my investors, network and wider audience. Reflection, for me, has become a deliberate practice. In a world that increasingly rewards speed, immediacy, and constant output, stepping back feels almost counterintuitive.

Compartmentalising is something I have found increasingly valuable. That is, to separate the professional from the personal and the signal from distraction.

There are 365 days in a year, or 8,760 hours. That is an extraordinary amount of time to get things done but just as much time to move quickly without ever truly reflecting. Regardless of how successful or productive a year has been, it is always my tendency - and most people’s tendency - to want more. Ambition can sometimes be my own worst enemy, always striving for more, always doing more, which can also come with some significant downsides. That tendency is deeply human. We are wired to strive, to aspire, to optimise. Those instincts are responsible for extraordinary outcomes - innovation, prosperity, growth. But, without conscious awareness, ambition can quietly become a moving goalpost, where satisfaction is always deferred to the next milestone.

Appreciation, in my view, is one of the most important skills in life. The older I get, the more I have come to realise that genuine appreciation doesn't dampen ambition; it refines it. Always needing more can become a vicious cycle, an endless chase that rarely delivers lasting fulfillment. As Morgan Housel articulates it, "Happiness is the result of expectations minus reality." With time, I have learned that appreciation is not about settling or standing still. It grounds you enough to aim high without becoming overly attached to where you land. When you are not chasing outcomes for validation, progress becomes more intentional and less hurried. Ultimately, real satisfaction comes from being present in the journey itself, not fixated on a distant, arbitrary end goal.1

2025 was a year to remember for so many reasons. From a market perspective, we are ending the year in a profoundly contradictory place. On the surface, global equity markets appear strong, with major indices sitting at or near all-time highs. Capital has flowed aggressively into a relatively narrow group of large technology companies, many of them directly or indirectly exposed to AI. In the US, market concentration has reached levels typically associated with late-cycle environments, where a small number of companies account for a disproportionate share of total returns.1

Yet, beneath this surface, the macro-economic picture is complex. Inflation remains sticky, particularly in services, and productivity growth in the UK continues to lag historical averages. Geopolitically, the world feels more fragmented than it has in decades, with protectionism around strategic resources intensifying. We are witnessing an increasingly explicit challenge to the dollar’s role as the world’s reserve currency - a trend reinforced by experimentation with alternative global payments infrastructure and crypto/stablecoins.1

Despite these challenges, optimism is not just warranted; it is essential. It has unquestionably been the year AI shifted from narrative to economic enabler. What began as experimentation has translated into tangible impact, influencing cost structures, margins, and capital allocation across the board. We may well be in a bubble, but if so, it increasingly resembles an "inflection bubble" rather than a mean reversion one - a bubble that funds the infrastructure for a new era of productivity and growth.1

As we look ahead to 2026, I remain optimistic. Not because the path ahead is guaranteed to be smooth, but because the underlying drivers of progress are still very much in motion. When thoughtful people work on meaningful problems, supported by powerful technology and capital markets, the outcomes tend to compound.

I hope you find the following report valuable and I wish you all a fantastic year ahead.

Very best wishes,

Michael

Section 1: The Macro Lens - The Inflection Bubble and Circular Capital

1.1 The Nature of the Current Cycle: Distinguishing Signal from Noise

The financial landscape of 2025 was defined by a palpable tension between macro-economic strain and technological exuberance. To understand where the market is heading in 2026, one must first dissect the nature of the current "bubble." In financial history, bubbles generally fall into two distinct categories: mean-reversion bubbles and inflection bubbles. A mean-reversion bubble is a financial fad - asset prices rise based on leverage, credit expansion, and speculation without any fundamental shift in economic utility. When the credit cycle turns, these assets collapse back to historical averages, leaving little behind but bad debt. The subprime mortgage crisis of 2008 serves as the archetype for this dynamic.

In stark contrast, the current environment surrounding Artificial Intelligence (AI) bears the hallmarks of an inflection bubble. As outlined by Howard Marks in his recent analysis, inflection bubbles are based on genuine technological progress. While they often involve speculative mania and the destruction of individual wealth during the "installation phase" (as described by economist Carlota Perez), they act as necessary catalysts for building the infrastructure required for a subsequent "deployment period".1 The dot-com boom of the late 1990s was an inflection bubble; it crashed, destroying trillions in paper wealth, but the fibre-optic networks and server capacity it funded laid the groundwork for the modern internet economy, eventually birthing giants like Google and Amazon.

We see the same pattern today. The massive capital expenditures by hyperscalers are funding the data centres, energy grids, and GPU clusters that will underpin the next decade of global productivity. If AI delivers even half of what credible experts expect, it is not unreasonable to imagine a world where sustained 4%+ GDP growth in developed economies becomes achievable again - driven not by leverage or demographics, but by productivity.1 AI is inherently deflationary at the margin: it lowers the cost of intelligence, compresses labour inputs, reduces error rates, and accelerates innovation. This deflationary pressure on the cost of doing business is the counter-force to the inflationary pressures seen in the broader services economy.1

1.2 The Circular AI Capital Flow: A Reinforcing Loop

One of the defining characteristics of 2025 was the emergence of "circular AI capital flows." This phenomenon represents a self-reinforcing loop that has aligned public equities, venture capital, infrastructure investors, and private credit around a single technological theme. It is a financial perpetual motion machine, at least for the duration of the installation phase.

The mechanism operates as follows:

Hyperscalers (Producers): Entities like Microsoft, Google, Meta, and Oracle raise vast amounts of capital - often via debt or retained earnings - and deploy it into compute infrastructure (GPUs, data centres, custom silicon).
Enabling Scale: This massive injection of compute capacity enables AI foundation model companies (e.g., OpenAI, Anthropic) to scale their models and capabilities.
Driving Demand: The scaling of these models drives even greater demand for compute. This is fueled by scaling laws, where incremental improvements in intelligence require exponential increases in processing power, and the rise of inference-time compute, where models "think" longer to solve complex problems. This creates a feedback loop where the more capable a model becomes, the more hardware it consumes to both operate and improve.
Capital Stack Alignment: This loop pulls in venture capital to fund the application layer, infrastructure investors to build the physical data centres, and private credit to finance the hardware and energy needs.1

It is rare to see so many layers of the capital stack aligned so tightly around a single thematic axis. However, the market is beginning to show sophistication in how it prices this risk. Credit markets, often the most conservative signal, are distinguishing between companies that structurally benefit from AI and those that merely adopt it. For instance, Oracle’s debt now trades at a materially wider spread than Microsoft’s, reflecting a nuanced view of creditworthiness in the AI arms race.1 Furthermore, sophisticated investors have begun hedging AI exposure through the use of Credit Default Swaps (CDS) on single names such as CoreWeave, Oracle, and Meta, signalling that while the trend is upward, the volatility risk is acknowledged.1

1.3 2026 Outlook: The Year of Delays vs. Adoption

Looking toward 2026, the market faces a divergence defined by a collision between soaring demand and physical constraints. While end-user adoption of AI accelerates, the infrastructure build-out faces a "Year of Delays". This lag suggests that value will accrue not to those building the infrastructure, but to those deploying the software or those who already possess the "hard" assets required to operate.

The Constraints of Physics and Supply Chains:

The TSMC Brake & The Competitive Moat: The semiconductor supply chain, reliant on monopolies like TSMC and ASML, cannot ramp capacity instantaneously. With Capex increasing at a fraction of revenue growth, GPU availability will remain tight. Crucially, this scarcity creates a formidable competitive moat for incumbents; those with existing hardware allocations possess a structural advantage that new entrants simply cannot buy their way into.1
Industrial Fragmentation & The Energy Alpha: Data centre construction relies on fragmented inputs - generators, cooling units, and grid interconnects. Delays in any single component push back entire timelines. In this environment, entities that control the "bottleneck" assets - specifically those with direct links to the grid, stable electrical supply, or the ability to deploy modular nuclear power - are in a prime position to benefit from the infrastructure squeeze.2
Labour Shortages: The immense scale of construction requires skilled labour (electrical engineers, HVAC specialists) that is currently in short supply, further extending the two-year average build time for data centres.2

1.4 Geopolitics and the Multipolar Financial System

Parallel to the technological shift is a geopolitical one. The global order is fragmenting into a "war for AI" played out across policy, capital, and talent flows. Protectionism around strategic resources like semiconductors and rare earths has intensified.1

New Chains and Commodity Arbitrage

Perhaps most notably, we are seeing an explicit challenge to the dollar’s role as the world’s reserve currency. This shift is fueled by a move toward a multipolar financial system and experimentation with alternative payment infrastructures.1

Strategic Note: The changing political order is opening up entirely new supply chains and physical commodity arbitrages. As economic blocs like BRICS grow, we anticipate a recovery in diverse commodity markets; however, current shipper and payment infrastructure may not be ready for this decentralised reality. This friction is a primary driver for our "Fintech 2.0" thesis, where non-sovereign rails become critical infrastructure for global trade.1

Section 2: The Compute Economy and the Shift to Application

2.1 Producers vs. Consumers of Compute

The initial phase of the AI boom (2023-2025) overwhelmingly rewarded the Producers of Compute - the chip designers (Nvidia), the fabricators (TSMC), and the hyperscalers providing the cloud infrastructure. These entities captured the lion's share of value as the market scrambled for scarcity. However, as we move into 2026, the centre of gravity is shifting toward the Consumers of Compute - the application layer that converts raw intelligence into economic value.1

The economic logic for this shift is grounded in historical precedent. During the internet revolution, the initial value accrued to Cisco and the fibre layers (infrastructure), but the enduring value was created by Google, Amazon, and Facebook (applications) which utilised that infrastructure to reshape behaviour. Similarly, in the "Compute Economy," the winners of the next cycle will be the companies that can effectively arbitrage the cost of intelligence against the value of the business problem they solve.

Table 1: The Shift in Value Accrual (2024-2026)

Phase	Dominant Players	Key Metric	Investment Focus
Phase 1 (Installation)	Nvidia, TSMC, Hyperscalers	GPU Availability, FLOPs	Infrastructure, Foundation Models
Phase 2 (Deployment)	Vertical SaaS, Agents, Fintech	ARR per Employee, ROI	Application Layer, Orchestration

This shift is not just theoretical; it is visible in the margins. The cost of "raw tokens" - the fundamental units of AI-generated output - is plummeting (deflationary). Just as the cost of a long-distance phone call or a megabyte of data eventually dropped to near zero, the cost of generating AI intelligence is becoming a commodity.

However, while the "ingredients" are getting cheaper, the value of a solved business problem (like automating legal discovery or real-time supply chain optimisation) remains high.

The massive opportunity for investors lies in "capturing the spread":

The Input: Dirt-cheap raw AI processing power.
The Output: High-value, mission-critical business solutions.

Companies that sit between the raw model and the end-user - those that add specific business context, ensure 100% reliability, and integrate AI directly into existing workflows - are positioned to pocket the difference between these two price points.

2.2 The Rise of AI Orchestration Layers

As enterprises move from experimentation to integration, they are discovering that raw models are insufficient for complex business workflows. A foundation model can write a poem, but it cannot reliably reconcile a ledger or execute a trade without guardrails. This has given rise to the AI Orchestration Layer - the middleware that connects foundation models to enterprise data, manages context, ensures compliance, and executes multi-step tasks.

Our team sees this layer as critical. It is the "connective tissue" that transforms a probabilistic LLM into a deterministic business process. A prime example of our thesis in action is Revenue Labs. Self-described as the "Databricks for GTM" (Go-to-Market), Revenue Labs sits at the intersection of data and execution, orchestrating AI agents to automate complex revenue operations. By treating GTM processes as a data engineering problem, their platform allows enterprises to deploy AI that doesn't just "chat" but actually "does" - updating CRMs, scoring leads, and executing outreach with high fidelity. This capability effectively replaces the "swivel chair" work of junior sales operations staff with scalable code.1

This thesis aligns with the broader market movement toward "agentic" workflows. In 2026, we expect the conversation to move beyond "copilots" (which assist humans) to "agents" (which act on behalf of humans). This shift requires robust orchestration to manage the safety, accuracy, and sequence of agent actions. Without orchestration, agents are unreliable; with it, they are transformative.

2.3 State of Software 2025: Efficiency as the New Moat

The software landscape in 2025 has matured significantly. The "growth at all costs" mentality of 2021 has been replaced by a disciplined focus on efficiency. According to ICONIQ’s "State of Software 2025" report, the Rule of 40 (growth rate + profit margin) remains the primary valuation signal, but the composition has shifted. Companies are achieving efficiency not just through cost-cutting, but through structural changes enabled by AI.3

Key Metrics Defining 2025 Software:

Burn Multiples: Top-quartile companies have driven burn multiples down to <1.5x, with elite AI-native companies often operating at ~0.4x. This is due to rapid revenue scaling relative to headcount - AI companies can add millions in ARR without adding dozens of sales reps.4
ARR per FTE: Revenue per employee is rising faster than OpEx per employee, signaling a decoupling of revenue growth from headcount growth. This is the holy grail of software economics - infinite leverage.4
Scaling Velocity: AI-native applications like Cursor and Lovable have redefined speed, scaling from zero to over $100m ARR in approximately 12 months. This is unprecedented velocity compared to the SaaS era, where $100m ARR was a 7-10 year journey.1

Section 3: Fintech 2.0 - The Convergence of Intelligence and Value

3.1 The Undervalued Opportunity

While the market’s attention has been captivated by Generative AI, Fintech has quietly entered a transformative phase we call Fintech 2.0. We believe this segment is currently undervalued. The "Fintech 1.0" era was defined by user experience - better apps, neobanks, and smoother onboarding. It was about putting a digital face on analog banking rails. Fintech 2.0 is defined by infrastructure and intelligence: the convergence of AI, blockchain, and deep payment orchestration.1

The narrative that "Fintech is dead" is misplaced. Instead, we are seeing the installation of a new financial operating system. AI is not just a feature here; it is rewriting the unit economics of financial services. From underwriting to fraud detection, AI agents can process information and make decisions at a speed and cost that human-heavy incumbents cannot match. Simultaneously, blockchain infrastructure, particularly stablecoins, is maturing into a viable rail for cross-border B2B payments, bypassing legacy correspondent banking networks (SWIFT).

3.2 Portfolio Deep Dive: The Fintech 2.0 Cohort

Our team has already been busy actively deploying capital into this area, backing companies that sit at the intersection of these technologies:

Velocity: A prime example of the convergence thesis. Velocity has emerged from stealth with significant backing to power the "velocity of money." It integrates stablecoin payment accounts directly into enterprise workflows, allowing businesses to move capital across banks and blockchains seamlessly. Traditional treasury systems are siloed; Velocity unifies fiat and crypto liquidity, solving real-world treasury headaches regarding liquidity and settlement speed. This moves crypto from speculation to utility, enabling 24/7/365 settlement for global enterprises.6

Damisa: Focused on simplifying cross-border payments, Damisa targets complex sectors like logistics, physical commodities, real estate, and education - industries often plagued by slow settlements and opaque fees. Damisa uses stablecoins and smart wallets to replace archaic escrow services. By programmaticising the release of funds based on verified events (e.g., shipping confirmation), Damisa reduces friction in high-value global transactions. This is a classic "unsexy but critical" infrastructure play.7

Allasso: Targeting the sophisticated end of the market, Allasso secured $3m to bring AI-ready analytics to options trading. Legacy trading infrastructure is often outdated, siloed, and heavily reliant on manual spreadsheets or mainframes. Allasso delivers a modern, API-first interface that empowers traders with real-time risk management, backtesting, and scenario analysis. This fits our "vertical AI" view - applying intelligence to highly specific, high-value workflows where accuracy and speed are paramount.9

Prosper: In the wealth management space, Prosper is building a platform to democratise access to high-net-worth investment strategies. Historically, top-tier financial advice and private market access were gated by high minimums. Using AI, Prosper provides tailored financial advice and access to private market investments that were previously out of reach for most consumers. This represents the application of AI to drive financial inclusion and better outcomes, scaling the role of a private banker to the mass affluent.11

PayControl: As payment stacks become more complex, orchestration becomes essential. PayControl acts as the control layer for enterprise payment management. It is provider-agnostic infrastructure designed for scale, enabling businesses to route transactions intelligently across multiple providers, reducing fees and increasing acceptance rates. In a fragmented global payment landscape, PayControl is the "universal adapter," ensuring that payments succeed regardless of geography or method. This is "plumbing" in the best sense - critical, sticky, and highly scalable.13

3.3 The Future of Money

The direction of travel is toward a multipolar financial system. As geopolitical fragmentation continues, we see an increasing challenge to the dollar's hegemony and a rise in alternative payment rails.1 Fintech 2.0 companies are building the bridges for this new reality. They are not trying to be banks; they are building the software layer that moves value as easily as the internet moves data. For us, this remains a high-conviction area for 2026. We expect to see further consolidation of stablecoin rails into B2B software, making "programmable money" a standard feature of enterprise ERPs.

Section 4: The AI-Enabled Services Roll-Up

4.1 Intelligence Arbitrage vs. Labour Arbitrage

A core component of our house view is the potential for AI-Enabled Services Roll-Ups. The traditional private equity roll-up model relies on labour arbitrage - buying service firms (accounting, legal, HR) and reducing costs by centralising back-office functions or offshoring labour to lower-cost jurisdictions. The AI-enabled model relies on intelligence arbitrage.

This strategy involves acquiring cash-generative professional services firms and systematically injecting AI to automate workflows. The goal is not necessarily to reduce headcount (though that is a lever), but to decouple revenue growth from headcount growth. By implementing "Universal GPTs" and agentic workflows, these firms can process 10x the volume of work with the same staff. An accountant equipped with AI agents can handle 50 clients instead of 5. This radically expands margins and valuation multiples, turning a 1x revenue services business into a 5-10x revenue technology platform.1

4.2 Case Study: Abingdon Software Group

Abingdon Software Group is our flagship execution of this strategy. Backed by us in their first round, Abingdon is on a journey to become one of Europe's leading software acquirers. It operates with a "Fortress P&L" mentality, prioritising best-in-class software metrics over vanity growth metrics.

2025 Milestones:

Financial Health: Abingdon has seen cash flow, equity value, and portfolio diversification continue to compound. It targets mission-critical Vertical Market Software (VMS) businesses with low churn and high pricing power.
Capital Structure: In 2025, the group achieved a significant milestone by closing a debt facility with Ares, a tier-one credit provider. This facility provides the dry powder needed for aggressive acquisitions without excessive equity dilution.
Growth: The group also commenced its Series C equity round, positioning it for further scale in 2026. This round will fuel the acquisition of larger targets, accelerating the flywheel.1

Abingdon represents a lower-risk, high-reward way to play the AI theme. Rather than betting on a single AI startup finding product-market fit, Abingdon acquires established VMS businesses and improves them. It is a compounder, designed to generate consistent returns by applying operational excellence and AI efficiency across a diversified portfolio. We view this as "private equity returns with venture capital upside."

Section 5: Vertical AI - The Application Layer Thesis

5.1 Why Horizontal AI Won't Win Everything

There is a prevalent fear that horizontal models (GPT-5, Gemini) will swallow all vertical applications. We take a contrarian view: we believe that while horizontal models provide the reasoning engine, Vertical AI provides the solution. Generalist models lack the context, regulatory compliance, and workflow integration required for specific industries like law, healthcare or construction.

5.2 The Vertical Moats

Vertical AI companies build defensibility through four key mechanisms:

Proprietary Data: They sit on unique datasets (e.g., patient records, legal precedents) that are not available to the public web. This data fine-tunes the model to be hyper-accurate for that specific domain.
Workflow Integration: They embed deeply into the daily tools of the user (EMRs, ERPs). AI becomes a feature of the workflow, not a destination.
Regulation & Trust: In regulated industries, "good enough" is not acceptable. Vertical AI builds the compliance guardrails (HIPAA, GDPR) that generalist models ignore.
Ecosystem Effects: As more users in a vertical join, the data advantage compounds, making the model smarter for that specific industry.

We are actively investing in founders who understand the specific pain points of their vertical and are using AI to solve them, rather than just wrapping a GPT API.

Section 6: Venture Mechanics - The Power Law

6.1 The Hidden Power Law

Venture capital is not a game of averages, it is a game of outliers. The Power Law dictates that a small number of companies drive the vast majority of returns. A single "fund returner" can outweigh all losses in a portfolio. Understanding this asymmetry is crucial for investors. It explains why we must remain aggressive in backing "A+ founders" with category-defining ambition, even in uncertain markets.1

Our approach focuses on the "tail curve." We construct portfolios broad enough to provide optionality, but concentrated enough to back the winners meaningfully.

6.2 Anatomy of a Strong Vintage

History suggests that the best venture vintages are often born during periods of correction and technological transition.

2002-2004: Following the dot-com crash, this vintage produced LinkedIn, Facebook, and YouTube as internet infrastructure matured.
2009-2011: Following the Global Financial Crisis, the mobile/cloud shift produced Uber, Airbnb, Stripe, and Slack.

We believe vintages during this period are shaping up to be of a similar magnitude. We have the combination of a massive platform shift (AI), a correction in valuations (from 2021 peaks), and a concentration of elite talent entering the founder pool. Our team strongly believes that the current cycle has attracted the "highest calibre of technical talent" we’ve seen in a decade. Engineers are leaving Big Tech to build, driven by the once-in-a-career opportunity AI presents.1

This vintage is defined by the shift from Labour to Software. AI allows startups to attack the largest cost base in the global economy - human labour ($40 Trillion+ globally). Companies founded today can scale revenue without linearly scaling headcount, creating business models with unprecedented operating leverage. We are not just funding software; we are funding the replacement of services with code.

Section 7: Conclusion - The Road to 2026

7.1 The Shifting Tides

As we look toward 2026, the signal is clear. We are in the midst of a structural transformation of the economy. The noise of the "bubble" serves a purpose - it attracts the capital and talent necessary to build the rails for the future. The UK market specifically is positioned to lead in this new era, with its strong base of technical talent, supportive policy environment for AI infrastructure and a robust fintech ecosystem.1

7.2 Our House View

For our team, 2026 will be defined by four strategic pillars:

Vertical AI & Orchestration: Doubling down on the application layer where AI meets specific industry workflows and the middleware that makes it work.
Fintech 2.0: Backing the infrastructure of the new multipolar financial system - stablecoins, cross-border rails, and programmable money.
Efficiency & Arbitrage: Supporting our portfolio in leveraging AI to break the link between revenue and headcount, and acquiring legacy businesses to transform them via intelligence arbitrage.
Discipline: Remaining "reasonable for a long time," avoiding the hype while staying fully engaged in the innovation.

The defining test for the year ahead will be adoption. Can the technology move from the "wow" phase to the "ROI" phase inside large enterprises? We believe the answer is yes, and we are positioned to back the companies making it happen. We are grateful for the trust you place in us to navigate these tides. The opportunity set is vast, the talent is exceptional, and the engine is running.

Appendix: Key Data & Metrics

Table 2: Market Adoption Benchmarks

Technology/App	Time to Scale	User/Revenue Milestone
ChatGPT	< 3 Years	~800 Million Users
Cursor / Lovable	~12 Months	$100 Million ARR
Mobile Internet	~10 Years	Global Saturation

Sources cited:

Michael Burnett’s Substack: https://michaelburnett3.substack.com/ - I’ve also aggregated all the articles in one place here.
AI in 2026: A Tale of Two AIs | Sequoia Capital, accessed January 15, 2026, https://sequoiacap.com/article/ai-in-2026-the-tale-of-two-ais/
State of Software 2025: Rethinking the Playbook - ICONIQ, accessed January 15, 2026, https://www.iconiqcapital.com/growth/reports/2025-state-of-software
Iconiq's State of Software in 2025: Forward Deployed Engineers Up 12x; 55% of GTM is Now in Post-Sales; 2025 Is Crushing 2024 For Funding | SaaStr, accessed January 15, 2026, https://www.saastr.com/iconiqs-state-of-software-in-2025-much-smaller-teams-55-of-gtm-is-now-in-post-sales-2025-is-crushing-2024-for-funding/
State of Software 2025: Rethinking the Playbook, accessed January 15, 2026, https://cdn.prod.website-files.com/65d0d38fc4ec8ce8a8921654/68b8696eb46a99918d197637_ICONIQ_Analytics_State_of_Software_2025.pdf
Velocity emerges from stealth with $10M to power the velocity of money - Fuel Ventures, accessed January 15, 2026, https://www.fuel.ventures/velocity-emerges-from-stealth-with-10m-to-power-the-velocity-of-money
Damisa secures £2.25 Million pre-seed to build the fastest and most secure stable-coin payment experience yet | Fuel Ventures | Early-Stage VC Funding for High-Growth Tech Startups, accessed January 15, 2026, https://www.fuel.ventures/damisa-secures-2-25-million-pre-seed
Damisa raises £2.25M to simplify stablecoin-powered cross-border payments - Tech.eu, accessed January 15, 2026, https://tech.eu/2025/04/10/damisa-raises-ps225m-to-simplify-stablecoin-powered-cross-border-payments/
Allasso wins $3m investment to bring AI-ready, data-enabled and complete analytics to options trading and beyond | Fuel Ventures, accessed January 15, 2026, https://www.fuel.ventures/allasso-wins-3m-investment-to-bring-ai-ready-data-enabled-and-complete-analytics-to-options-trading
Allasso Raises $3 Million in Funding | The SaaS News, accessed January 15, 2026, https://www.thesaasnews.com/news/allasso-raises-3-million-in-funding
Fuel Ventures Leads £4M Investment in Prosper to Transform Wealth Management, accessed January 15, 2026, https://www.fuel.ventures/prosper-4million
London's fintech startup Prosper secures £4M led by Fuel Ventures | Vestbee, accessed January 15, 2026, https://www.vestbee.com/insights/articles/prosper-secures-4-m
Top 15 Franchise Startup Investors in United Kingdom in September 2025 - Shizune.co, accessed January 15, 2026, https://shizune.co/investors/franchise-investors-united-kingdom
PayControl | Payment Infrastructure for Enterprises, accessed January 15, 2026, https://paycontrol.xyz/
PayControl Raises $1.7M Seed Funding - LeadsOnTrees, accessed January 15, 2026, https://www.leadsontrees.com/news/paycontrol-raises-17m-seed-funding

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