Market Disruption Analysis

Tariffs and transfer pricing - have we seen it already with COVID? Your TNMM benchmark looks perfect until market disruption hits. Then it's worthless. This problem pops up whenever significant disruptions occur. Today, it's tariffs. Yesterday it was COVID. Before that, the financial crisis. The pattern repeats (can we please stop?). The issue? You're preparing benchmarks using historical data that doesn't reflect current market conditions. Private company financials lag 1-2 years behind reality. By the time database providers update their information, the market has already moved on. Think about it: → 2023 financials for many companies won't appear in databases until late 2024 → Major tariff impacts happening now won't show up in your benchmarks until 2025 → Your tested party feels the effects immediately while your comparables data remains frozen in time Tax authorities aren't blind to this timing mismatch. They know your benchmarks don't capture current realities. And they can use this against you. What can you do? A few things. Make comparability adjustments to either your tested party or comparables → Normalize extraordinary costs → Account for volume changes → Factor in price fluctuations Leverage public company data for more current insights → Listed companies report quarterly → Industry trends become visible faster → Market changes are more transparent Document the disruption thoroughly → Build a compelling economic case for what happened → Collect evidence of how it impacted your industry → Prepare a quantitative analysis of the expected effect Consider alternative methods temporarily (but be VERY CAREFUL with this) → Is cost plus more reliable during this period? → Would a profit split better reflect shared market challenges? → Can internal CUPs provide stronger support? Monitor actual results against projections → Track how margins evolve as the disruption unfolds → Adjust your expectations based on real data → Be ready to explain significant deviations Be cautious though. More adjustments mean more subjectivity, which leads to more potential disputes. You need to balance economic reality with defensibility. Tax authorities prefer precision over accuracy. They'll often choose an outdated benchmark with exact numbers over a current adjustment with estimates - especially if their concerns lead to better tax outcomes for them! Your best strategy? Address the timing problem head-on. Don't pretend your benchmark captures current conditions when everyone knows it doesn't. Build your case with transparency, acknowledging the limitations while providing the best available alternatives. What other approaches have you used to handle benchmark timing issues during market disruptions?

Innovation Moves Fast – and So Does Disruption. Are You Ready to Keep Up? Today, the S&P 500 faced a significant blow, driven by Nvidia’s dramatic drop in value. The cause? DeepSeek’s game-changing AI breakthrough, announced on January 27, 2025. Their model, Janus-Pro-7B, is shaking up the industry with efficiency gains that challenge even the largest players like OpenAI, Meta, and Google. This disruption alone erased $1 trillion in U.S. market value, with Nvidia losing nearly $600 billion in market cap. DeepSeek’s approach is as innovative as it is disruptive: - 75% Memory Reduction: By optimizing computations (8 decimal places vs. 32), they’ve slashed memory use while maintaining accuracy. - Task-Specific Efficiency: Their “expert system” activates only 37B out of 671B parameters as needed, cutting resource demands. - 10x Training Efficiency: They trained their model with just a fraction of the computing power required for Meta’s Llama 3.1, focusing on smarter optimization over brute-force scaling. This efficiency-focused design is democratizing AI development, enabling smaller businesses and researchers to compete with established tech giants. The result? A more accessible, competitive, and innovative AI ecosystem. However, disruption comes with risks. Hours after launch, DeepSeek faced a large-scale cyberattack, halting user registrations and exposing the vulnerabilities of open AI platforms. As AI continues to reshape industries and markets, ensuring robust cybersecurity will be critical. The implications of this event extend beyond tech. The geopolitical backdrop, especially tensions between the U.S. and China, could complicate DeepSeek’s global adoption. Trust issues, potential market restrictions, and regulatory hurdles might limit their reach in Western markets. Here’s the takeaway: Innovation and disruption are accelerating – faster than many businesses can adapt. To stay ahead, leaders must rethink strategies, prioritize efficiency, and address the growing importance of cybersecurity and ethical considerations in this rapidly evolving landscape.

This map shows why climate risk is one of the biggest disruptors of economic and trade systems. The risk does not emerge from isolated climate events, but from cascading failures across interconnected systems. Physical impacts become economically material once they damage infrastructure, disrupt food production, constrain energy and water systems, and trigger price volatility in essential goods. Disruption accelerates when these impacts spill into markets and finance. Supply chains fracture, insurance and credit conditions tighten, asset prices adjust sharply, and capital flows retreat from exposed regions. Trade disruption follows as confidence in financial and institutional stability weakens. At this point, climate risk evolves from an operational concern into a governance and economic continuity challenge. Consumption declines, productivity losses accumulate, business failures increase, and unemployment rises, reinforcing structural disruption rather than temporary volatility. This dynamic explains why climate risk is increasingly treated as a core sustainability strategy and enterprise risk management issue. Effective responses depend on integration into governance frameworks, financial decision-making, trade exposure analysis, and organizational capability building, rather than being managed in isolation. Source: Chatham House, Climate Change Risk Assessment 2021

We are often asked about the impact of a cyber-attack on an organisation and tend to focus on operational disruption, brand and reputation, and harm to individuals. Yet, understanding the financial impact is much harder to determine, given the large number of factors that come into play. A new Australian study (The impact of extreme cyberattacks on market valuations: An in-depth economic analysis) by Ryan et al. (2025) reveals that extreme cyberattacks, those causing direct losses over $10 million or major operational disruption, have a profound and lasting impact on the market valuation of publicly traded companies. Analysing incidents from 2000–2021, the research found: - Market Value Declines: On average, affected companies underperformed by nearly 9% in the year following a major cyber event, and by 14% over two years. Recovery is slow, with many firms still lagging market benchmarks two years post-incident. - Severity Is Increasing: Since 2015, the market’s intolerance for cybersecurity failures has intensified, with more severe and persistent financial repercussions. - Attack Type Matters: Ransomware and malware attacks have a much greater negative impact on market value than data breaches, highlighting the need for tailored risk management. - Beyond Immediate Costs: The true financial impact extends far beyond remediation and compensation, opportunity costs, reputational damage, and increased operating expenses are significant. What does this mean for business and incident response? Boards and executive teams must treat cyber risk as a strategic priority. Effective incident response isn’t just about technical recovery, it’s about protecting long-term value, reputation, and stakeholder trust. Investing in robust prevention, rapid response, and transparent communication is essential to mitigate both immediate and downstream impacts. Reference: Ryan, M., Withers, G., & den Hartog, F. (2025). "The impact of extreme cyberattacks on market valuations: An in-depth economic analysis." Australian Journal of Management. https://lnkd.in/gjm2pF2f #CyberSecurity #IncidentResponse #RiskManagement #MarketValue #McGrathNicol

Global disruption is accelerating again. What should Third-Party Risk professionals do right now? Energy market instability. Trade fragmentation. War-driven logistics disruption. Climate-driven operational interruptions. Rising cyber spillover. April 2026 is showing a pattern many recognize: disruptions are not isolated events, they are overlapping and reinforcing each other. Below are practical actions that risk leaders should be considering right now. 1. Reassess critical suppliers based on current geopolitical exposure Vendor criticality defined 12 months ago may no longer reflect current reality. Suppliers dependent on: • Middle East shipping routes or energy inputs • China-linked components or rare earth materials • Eastern European logistics corridors • climate-sensitive regions • fragile telecom or infrastructure networks may now represent materially higher disruption risk. Re-ranking supplier criticality based on current exposure is more useful than expanding risk questionnaires. 2. Identify concentration risk below Tier 1 vendors Many organizations understand their direct suppliers but lack visibility into: • fourth parties supporting cloud infrastructure • sub-processors handling sensitive data • logistics providers shared across multiple vendors • shared technology platforms embedded across services Recent global events highlight how quickly disruption propagates through shared dependencies. 3. Evaluate supplier viability under cost and logistics shocks Rising energy prices, shipping delays, tariff pressure, and currency volatility can affect vendor stability even when performance metrics appear unchanged. Risk teams should consider: • suppliers operating on thin margins • suppliers heavily dependent on imports or exports • vendors exposed to sanctions or trade controls • vendors facing insurance or freight cost increases Operational disruption often begins as financial pressure. 4. Increase monitoring frequency for high-impact vendors Annual or static reviews are insufficient when disruption conditions change quickly. For critical vendors, consider monitoring: • geopolitical exposure • cyber incidents • financial stress indicators • changes in subcontractors • shifts in service delivery location • force majeure triggers Continuous monitoring does not require reviewing every supplier, focus on those that matter most. 5. Stress test business continuity assumptions Many BCP plans assume localized disruption. Recent events show disruption can affect multiple regions simultaneously. Risk teams should revisit: • alternate supplier readiness • recovery time assumptions • cloud region concentration • telecom dependency • logistics rerouting capability • substitution feasibility Testing assumptions now is significantly less costly than testing them during an outage. #ThirdPartyRiskManagement #TPRM #VendorRiskManagement #3prm #OperationalResilience #SupplyChainRisk #RiskManagement #CyberRisk

We're watching Clayton Christensen's "Innovator's Dilemma" play out in real-time. A major consulting firm grew AI revenue 9x (from $100M to $900M) in one year. Their reward? 10,000+ jobs cut in a single quarter Stock down 35% $86 billion in market value destroyed This is EXACTLY what Christensen predicted: Great companies fail not despite doing everything right, but BECAUSE of it. The pattern is everywhere: 📊 Consulting: 44% decline in job postings while AI bookings soar 📊 Software: $160-188B in value destruction across leading platforms 📊 The paradox: Record AI revenue = Record restructuring Winners vs. Losers are already clear: ✅ Platform models (usage-based): +200% valuations ❌ Application models (seat-based): -28% despite AI features ✅ Consumption pricing: Scaling WITH disruption ❌ Time/seat pricing: Dying FROM disruption The firms generating the MOST AI revenue are often the ones losing the MOST value. Why? They're monetizing their own obsolescence. Full analysis in the article—including why this mirrors Kodak's digital photography paradox. Note: In compliance with regional regulations, specific company names have been omitted. All data patterns are from publicly available sources and independently verifiable through financial databases. Is your AI revenue actually a warning signal? #Innovatorsdilemma #AI #Disruption #ClaytonChristensen #BusinessStrategy #DigitalTransformation

Many of my friends in the Israeli tech ecosystem are anxiously looking at the 77% decline of Monday’s stock and thinking what it means for the future of SaaS. When one of our best companies, growing 27% at $1.2B revenue with a 27% cash flow margin , trades at ~1.5x EV/NTM revenue, is there any future for the rest of us? There are three main risks that the SaaS decline has priced in: 1. Vibe coding: Customers building custom solutions instead of buying. 2. Commoditization: Zero-cost R&D allowing low-priced entrants to flood the market. 3. Workflow disruption: AI agents obviating the need for software designed for humans In my view, the risk is real, but somewhat different than what the market is pricing in. → I’m least concerned about vibe coding. For SMBs, it is cheaper to pay a SaaS fee than to vibe-code. Enterprises don’t have the in-house capability to ensure the security and compliance of vibe coded apps, and it will take them a long time to develop it, if at all. → Commoditization is inevitable, but code was rarely the biggest moat for SaaS. More often, moats are built with scale through distribution, brand, and embedding. Monday spends 25% on R&D but 63% on SG&A. A new entrant with near-zero R&D costs will still find it hard to compete. If they underprice Monday to the point of being a threat, their own unit economics will break. → Workflow disruption is the real battle. Monday's current offering may not be relevant in the agentic era. However, this is a much larger risk for high-growth companies trading at 8x revenue (pricing in cash flows for the 2040s) than for Monday, which trades at 8x cash flow. For startups, this is the opportunity. Public SaaS is suffering because the market expects someone else to eat their lunch. You can be that company. The disruptors will be rewarded in the public markets. For Monday, the jury is still out. The management clearly sees the agentic shift and is building for it. If they can effectively disrupt themselves and become the future platform for how work gets done, they will end up more valuable than ever.

Will AI destroy the market for third party data providers? The stocks of companies like LSEG, Thomson Reuters, and Morningstar are taking an absolute beating, with most companies in the space losing nearly half their value over the last year. The reason? AI. When you consider the biggest value drivers of these companies, there are reasons to be concerned - but, there are also some rays of hope: 1️⃣ Data Aggregation - a significant amount of the data sold by these companies is publicly available. In a world where you can deploy a swarm of agents to 'source' this data on your behalf, the value from aggregation quickly goes to zero. However, it's also true that some of the data being aggregated by these companies comes from proprietary sources that are not publicly available. Herein lies one of their best options to retain long-term relevance. Level of AI disruption (out of 10) - 7 2️⃣ Data Curation & Management - one value of these companies is applying the 'curation' needed to ensure their data is both insightful and trustworthy, and exposing it in ways where it's easily consumable. AI is both helping, and slightly hindering, in this regard. For many, the real value here is brand equity (you trust the brand, so you trust the data). As AI gets better at automating things like data quality, entity resolution, and data stewardship, it will be much easier for smaller, more niche players to enter the market - or for companies to DIY. Level of AI Disruption: 5 3️⃣ Data Analysis & Services - many of these companies layer value-add services and consulting on top of their data. Thanks to the growing skills of agents as data analysts, this is one area where stockholders have a right to be deeply concerned. However, it's also an area where AI can never fully replace human experts with 20-30 years of deep expertise. Level of AI disruption: 6 4️⃣ Proprietary Insights & Analytics This is one area where these companies can continue to add unique value. Creating derivative insights from their vast troves of data, using teams of highly skilled data scientists, is one area where these companies can continue to do things that many, including AI, cannot. Level of AI Disruption: 3 5️⃣ Outsourced Infrastructure For many, it makes zero financial sense to support the massive infrastructures needed to capture and maintain all the data needed to create these insights. AI does little to undercut this advantage. Level of AI Disruption: 1 So... It appears to me like AI significantly disrupts some, but not all, of the biggest value drivers of these companies. While they may not be completely breaking their business models, significant reductions in the market capitalizations of these companies seems justified. But that's just my opinion. What do you think? #thirdpartydata #ai #disruption

The energy market disruption caused by the Middle East conflict has direct implications for businesses across every sector. Qatar's Ras Laffan facility, the world's largest LNG terminal, has gone offline. The Strait of Hormuz is constrained. That's roughly 20% of global LNG supply removed from the market, and unlike 2022, there's no easy substitute ready to step in. The U.S. isn't entirely insulated. Between widening export margins, post-Winter Storm Fern storage deficits, and surging #AI driven power demand, domestic gas and power prices face real upward pressure heading into next winter. If you manage or work in large real estate portfolios or energy-intensive operations, energy purchasing and use needs to be reconsidered. CBRE's latest Market Flash breaks down what's happening and what to watch. https://cbre.co/4vgMydY Most companies are missing out on ways to reduce their energy costs. This matters more as volatility from AI consumption, and now the Middle East conflict, increase energy price volatility. #EnergyMarkets #PowerMarkets #EnergyIndustry

The IRM market has been asking the wrong question. Which vendors are the strongest today is not the same question as which vendors are structurally durable as AI rewires the economics of risk and compliance delivery. Most buyers, boards, and investors have been answering the first and assuming it answers the second. It does not. The IRM50 AI Disruption Risk Index was built to answer the second question across 50 vendors and six tiers. Above the Compression Boundary, AI strengthens the control plane. Below it, AI compresses what vendors charge for. The middle is contested ground where AI feature velocity has not yet translated into structural durability. Today’s market is offering a live illustration. As software stocks are rebounding, ServiceNow and Salesforce lead the recovery. That is not a coincidence. ServiceNow is Tier 1 on this index. Riskonnect, built on Salesforce with Agentforce embedded, is Tier 2. Both sit above the Compression Boundary for structural reasons, not marketing ones. Our note discusses how Blackstone now requires AI disruption risk assessment in the first two pages of every deal memo. Your next vendor decision deserves the same discipline. Subscribe and download the full research note at rtj-bridge.com. https://lnkd.in/eBEAQFQ5 #IntegratedRiskManagement #RiskTechnology #AIDisruption #GRC #IRM50