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GOOG.NASDAQ
Google
Communication Services · Interactive Media & Services

Parent company of Google, dominating internet search, digital advertising, and emerging technologies like AI and autonomous vehicles.

HQ: United StatesListed: United States

Historical AI Consensus

Audit every published iPulse AI forecast batch and immutable historical research document for Google.

Historical AI Consensus

This page preserves the research and market snapshot packaged for this batch. It is not updated with later prices or revised advisor outputs.

Symbol
GOOG.NASDAQ
Batch
6
Published
July 5, 2026
AI Advisors
12

Historical AI Consensus Investment Thesis

Google (GOOG) Stock Forecast and AI Rating

Deep analysis published Original pricing snapshot 12 min read
Published 1-Year and 5-Year Forecast Outlook

Forecast targets and rating

Published batch rating

NEUTRAL

Frozen consensus rating from this immutable batch publication.

2027

1-Year

NEUTRAL

$362

+1.7%+1.9% incl. dividends
2031

5-Year

NEUTRAL

$528

+48.2%+50.2% incl. dividends

Published batch insight

Why the Market Miscalculates the Hidden Thermodynamic Cost of Agentic AI

There is high consensus across reports that the transition from link-based search to agentic AI will structurally compress margins due to massive capital expenditures and depreciation. While cloud growth and custom silicon provide a strong defensive moat, the impending depreciation wave remains the primary risk.

Deep Forecast Analysis by iPulse AI Engine

This analysis preserves the original published batch. Audit published forecasts in full transparency

Warren Buffett (Value Purist) advisor portraitSuperintelligence (Anthropologist) advisor portraitRay Dalio (Strategist) advisor portraitMachiavelli (Insider) advisor portraitElon Musk (Visionary) advisor portraitMichael Burry (Vulture) advisor portraitJ.P. Morgan (Titan) advisor portraitSherlock Holmes (Whistleblower) advisor portrait

Warren Buffett (Value Purist), Superintelligence (Anthropologist), Ray Dalio (Strategist), Machiavelli (Insider), Elon Musk (Visionary), Michael Burry (Vulture), J.P. Morgan (Titan), Sherlock Holmes (Whistleblower). Some archetypes run in multiple modes, resulting in 12 advisors total.

Computed on these frontier AI models
Gemini AI model logoGemini

Full published thesis

Executive Summary

If you invested $10,000 in Google at publication: $15,044 in five years versus $14,069 for S&P 500 benchmark.

Five-year consensus forecast for GoogleThe diagram shows the consensus value path for Google, a shaded advisor-disagreement range, forecast milestones, and a comparison with S&P 500 benchmark. including estimated net dividends of 0.3% per year.$10,000$15,000$20,000$17,622 (+76.2%)$15,044 (+50.4%)$12,465 (+24.7%)$14,069 (+40.7%)Published2027(1Y)2028(2Y)2029(3Y)2030(4Y)2031(5Y)
GoogleS&P 500 benchmark

* Return is calculated incl. 0.3% net dividend yield for Google.

Figure: Five-year consensus value path for Google compared with S&P 500 benchmark. The shaded band shows dispersion across advisor forecasts.

The asset is undergoing a profound structural transition from an asset-light software monopoly to a capital-heavy infrastructure utility. While robust cloud growth and custom silicon integration provide a strong defensive moat, the massive capital expenditures required for the AI transition will trigger a wave of depreciation that structurally compresses operating margins. This transition shifts the core business model from high-margin link-based search to compute-heavy agentic execution, altering the return on invested capital profile. Consequently, long-term valuation multiples must adjust to reflect this asset-heavy reality, even as absolute revenue continues to scale.

Key insights

  • Custom TPU architecture provides a critical thermodynamic cost advantage in processing agentic AI workloads over competitors.
  • Massive capital expenditures exceeding twenty-two percent of revenue will structurally depress near-term free cash flow conversion.
  • The transition from link-based search to generative answers cannibalizes high-margin advertising real estate and compresses gross margins.
  • Pristine balance sheet liquidity, minimal leverage, and aggressive share buybacks provide a strong defensive valuation floor.
  • Regulatory headwinds and antitrust actions targeting default distribution agreements present persistent operational friction and headline risk.
  • Scarcity premium may erode as upcoming pure-play AI mega-IPOs drain passive index flows from legacy tech.
  • Cloud profitability and enterprise backlogs act as a powerful offset to core search margin compression.

Deep Dive

The prevailing market narrative views the asset as an unassailable tech fortress that will effortlessly transition its search monopoly into the artificial intelligence era. The crowd and media treat massive capital investments, such as the strategic partnership with a leading AI startup, as brilliant offensive moves rather than defensive necessities. They assume historical thirty-percent operating margins will persist indefinitely, treating AI as a simple feature to monetize. This perspective remains anchored to past cash flows and is largely blind to the heavy capital expenditures and thermodynamic costs required to serve generative answers, pricing the stock as an infinite-margin software growth asset.

Alpha Gap & Repricing Catalysts

Where the published AI consensus diverged from the market narrative

Market narrative

The prevailing market narrative views the asset as an unassailable tech fortress that will effortlessly transition its search monopoly into the artificial intelligence era. The crowd and media treat massive capital investments, such as the strategic partnership with a leading AI startup, as brilliant offensive moves rather than defensive necessities. They assume historical thirty-percent operating margins will persist indefinitely, treating AI as a simple feature to monetize. This perspective remains anchored to past cash flows and is largely blind to the heavy capital expenditures and thermodynamic costs required to serve generative answers, pricing the stock as an infinite-margin software growth asset.

Alpha gap

The core information gap lies in the market's systematic mispricing of the structural capital intensity required for the artificial intelligence transition. While the crowd focuses on headline net income, forensic analysis reveals a stark divergence between reported earnings and actual free cash flow, driven by a massive wave of capitalized infrastructure costs. The asset is mutating from an asset-light software monopoly into a capital-heavy, lower-return industrial utility. The massive capital expenditures required for compute infrastructure will trigger an unavoidable depreciation wave that structurally compresses margins. The variant perception recognizes that while the company will maintain its dominant position in the compute stack, its valuation multiple must compress to reflect its new, energy-intensive infrastructure reality.

Repricing catalyst

The information gap is expected to close when consecutive quarterly earnings reports reveal flat or declining operating margins despite robust top-line growth. This margin compression will be mathematically driven by the surge in depreciation and amortization from recent data center buildouts. Additionally, upcoming mega-IPOs of pure-play artificial intelligence competitors will drain the legacy tech scarcity premium, forcing Wall Street to re-rate the asset based on its true free cash flow conversion and utility-like capital intensity.

Published consensus context

Macro regime alignment

The current macroeconomic regime presents a mixed setup with notable headwinds. A restrictive monetary policy of higher-for-longer interest rates penalizes long-duration growth multiples and commands a higher discount rate for future cash flows. Simultaneously, stagflationary pressures and energy shocks escalate the operational costs of power-hungry data centers while squeezing corporate advertising budgets. However, the company's fortress balance sheet and massive internal cash generation act as a powerful shield, allowing it to self-fund its massive capital expenditures and out-endure weaker, debt-dependent competitors.

Published consensus context

Advisor disagreement

The primary disagreement across the reports centers on the trajectory of profit margins and the valuation multiple. Some perspectives argue that the massive capital expenditure cycle is a visionary, high-return investment that will solidify a long-term computing monopoly, justifying the current premium multiple. Conversely, other reports view this spending as a defensive tax that structurally degrades return on invested capital, predicting a severe multiple contraction as depreciation catches up. This divergence is driven by differing assumptions regarding the speed of enterprise AI monetization and the long-term cost efficiency of custom silicon.

Published scenario analysis

Base-case forces, risks, and opportunities

Top drivers and tailwinds

  1. Custom Silicon and TPU Moat: Proprietary Tensor Processing Units (TPUs) provide a structural, thermodynamic cost advantage in processing agentic AI workloads. As the paradigm shifts from simple search queries to continuous agentic execution, token-throughput economics become the defining constraint. Vertical integration allows the company to process tokens at a fraction of the cost of competitors reliant on merchant silicon, preserving margins and reinforcing its infrastructure moat. Expected impact: +30%.
  2. Cloud Hypergrowth and Enterprise Backlog: The cloud computing division has successfully transitioned from a loss-leader into a highly profitable enterprise utility. Driven by sovereign AI mandates and multi-cloud diversification, enterprise demand is locked in with a massive backlog. This segment provides a highly predictable, recurring revenue stream that diversifies the company away from cyclical advertising dependency and captures structural enterprise workloads. Expected impact: +22%.
  3. Strategic AI Venture Equity and Anthropic Alliance: The massive strategic investment in Anthropic acts as a powerful hedge against single-model stagnation. By deeply integrating frontier capabilities while routing workloads through proprietary cloud infrastructure, the company secures compute utilization and equity upside. This capital loop offloads execution risk and enhances sum-of-the-parts valuation as private AI valuations are marked-to-market. Expected impact: +18%.
  4. Fortress Balance Sheet and Capital Returns: An ironclad balance sheet with minimal leverage and massive cash reserves allows the company to self-fund its monumental capital expenditures. This financial independence insulates the firm from high interest rates and credit scarcity. Concurrently, aggressive share buybacks and newly initiated dividends provide a strong valuation floor and support earnings-per-share compounding. Expected impact: +12%.

Top frictions and headwinds

  1. Capital Intensity and FCF Conversion Collapse: The structural shift from a capital-light software monopoly to a capital-heavy infrastructure utility requires staggering capital expenditures. This intense reinvestment rate severely depresses free cash flow conversion, creating a massive gap between reported net income and actual cash. The market's expectation of high capital efficiency is challenged by this permanent transition to an asset-heavy profile. Expected impact: -25%.
  2. Generative Inference Margin Compression: Serving generative AI answers is structurally more expensive than traditional link-based search indexing. As consumer behavior shifts to agentic queries, the compute cost per query skyrockets, compressing gross margins. Until algorithmic efficiencies and custom silicon can fully offset these token costs, this technological transition acts as a persistent drag on core profitability. Expected impact: -20%.
  3. Regulatory and Antitrust Headwinds: Ongoing antitrust litigation targeting default search agreements and the advertising technology stack presents a persistent operational friction. Potential remedies, such as banning exclusive distribution contracts or forcing ad-tech unbundling, threaten to disrupt the integrated data loop that drives historical pricing power. This legal overhang enforces a structural valuation discount on the asset. Expected impact: -15%.
  4. Mega-IPO Liquidity Absorption: The impending public listings of pure-play frontier artificial intelligence companies present a mechanical liquidity headwind. Institutional portfolios and passive index funds will be forced to rebalance and trim legacy mega-cap holdings to fund allocations to these new listings. This structural supply-demand imbalance exerts downward multiple pressure independent of fundamental execution. Expected impact: -10%.

Tail risks

  1. AI Infrastructure Write-Down and ROI Collapse: If enterprise adoption of artificial intelligence fails to generate tangible returns, the massive industry-wide infrastructure buildout could reveal itself as a cyclical overshoot. The company would be left holding depreciating, obsolete hardware and underutilized data centers, forcing historic asset write-downs and triggering a severe multiple contraction as the growth narrative collapses. Probability: +30%. Expected impact: -30%.
  2. DOJ Forced Structural Breakup: A judicial ruling forcing the structural divestiture of key assets like the browser or mobile operating system would dismantle the default distribution pipeline. This would violently disrupt the integrated data-flywheel and customer acquisition loop, forcing the company to bid aggressively for traffic on hostile platforms and permanently compressing operating margins. Probability: +20%. Expected impact: -35%.

Tail opportunities

  1. Waymo Commercialization and Autonomous Scaling: A full regulatory unlocking and successful scaling of the autonomous vehicle network could transition the segment from a costly research project into a highly profitable transit and logistics monopoly. This would open a multi-trillion-dollar addressable market completely divorced from digital advertising, diversifying earnings and fundamentally re-rating the asset as a physical-world infrastructure platform. Probability: +30%. Expected impact: +20%.
  2. Quantum Computing Commercialization: Achieving fault-tolerant, error-corrected quantum computing ahead of schedule would allow the company to offer exclusive quantum cloud services. This breakthrough would shatter classical computational limits for cryptography, molecular modeling, and materials science, instantly re-rating the firm from a mature internet utility to the undisputed pioneer of the next computational paradigm. Probability: +20%. Expected impact: +25%.

Company Financial Analysis

Google Earnings and Financials Analysis by AI

Financial figures available as of Jul 05, 2026. Only filings and source records available by this analysis date are included.

Earnings and financials

AI Review

The company shows incredible financial strength on the surface, with massive revenue and net income growth driven by its core search business and a rapidly expanding cloud division. However, there is a significant and worrying gap between reported accounting profits and actual cash in the bank. This divergence is caused by the massive upfront costs of building artificial intelligence infrastructure, which are currently recorded as capital investments rather than immediate expenses. While some analysts believe this is a temporary phase before a massive profit explosion, others warn that future earnings will be severely dragged down as these investments begin to depreciate. This disagreement highlights a key risk for everyday investors: the company's current high valuation is based on accounting numbers that may not fully reflect its true cash-generating power in a high-interest-rate environment.

Revenue, earnings, and cash flow

The table compares up to five fiscal years of revenue, net income, and free cash flow available to this analysis.

Revenue, net income, and free cash flow history
Fiscal yearRevenueNet incomeFree cash flow
2025USD 403BUSD 132.2BUSD 73.3B
2024USD 350BUSD 100.1BUSD 72.8B
2023USD 307.4BUSD 73.8BUSD 69.5B
2022USD 282.8BUSD 60BUSD 60B
2021USD 257.6BUSD 76BUSD 67B

Valuation context: historical P/E

The table compares up to five fiscal years of point-in-time valuation evidence available to this analysis.

Historical price-to-earnings ratios
Fiscal yearP/EEarnings basisCurrency basisTicker / reporting
202529.0xTTMNativeUSD / USD
202423.5xTTMNativeUSD / USD
202324.1xTTMNativeUSD / USD
202219.2xAnnualNativeUSD / USD
2021>100xAnnualNativeUSD / USD

P/E uses historical market capitalization and earnings known at each period. Cross-currency observations are normalized to USD using point-in-time FX rates.

Profitability and margins

AI Review

The company currently enjoys exceptionally high profit margins, but these peak numbers are likely to face downward pressure. The transition from traditional search links to direct artificial intelligence answers is structurally more expensive, as generating AI responses requires significantly more computer power and energy. While the cloud computing division has successfully turned into a highly profitable engine, helping to offset some of these costs, a looming wave of depreciation from recent data center construction will inevitably drag down operating margins. Analysts clash over the severity of this margin squeeze. Some believe custom computer chips will quickly lower costs and restore high profitability, while others argue that margins will permanently settle at a lower level. Retail investors should prepare for a period of margin compression as the company adjusts to the high operational costs of running advanced AI models.

The table compares up to five fiscal years of operating income and reported profitability margins.

Operating income and margin history
Fiscal yearOperating incomeOperating marginNet margin
2025USD 129.2B32.1%32.8%
2024USD 112.4B32.1%28.6%
2023USD 84.3B27.4%24.0%
2022USD 74.8B26.5%21.2%
2021USD 78.7B30.6%29.5%

Balance sheet and leverage

AI Review

The company's balance sheet is an absolute fortress, representing its greatest source of safety and competitive advantage. With minimal debt and massive cash reserves, the company is completely insulated from high interest rates and tight credit markets. This financial strength allows it to self-fund its multi-billion-dollar artificial intelligence investments without needing to borrow expensive money or dilute shareholder value. All reports agree that this pristine financial health provides ultimate downside protection, ensuring the company can survive severe economic downturns or energy shocks. Furthermore, this cash pile enables the company to continue returning value to shareholders through massive share buybacks and newly introduced dividends. While some note that cash accumulation may slow due to heavy capital spending, the balance sheet remains a powerful weapon that allows the company to out-spend and out-endure any competitor.

The table compares up to five fiscal years of debt, liquidity, net cash or debt, and current-ratio evidence.

Balance sheet leverage and liquidity history
Fiscal yearTotal debtCash + short-term investmentsNet cash / (debt)Current ratio
2025USD 59.3BUSD 126.8BUSD 28.6B net debt2.01x
2024USD 25.5BUSD 95.7BUSD 2B net debt1.84x
2023USD 27.1BUSD 110.9BUSD 3.1B net debt2.10x
2022USD 27.2BUSD 113.8BUSD 5.3B net debt2.38x
2021USD 26.2BUSD 139.6BUSD 5.3B net debt2.93x

Net debt below zero is displayed as net cash. Current ratio is current assets divided by current liabilities.

Capex and investment intensity

AI Review

The company's spending has shifted dramatically, with capital expenditures now consuming over twenty percent of its total revenue. This heavy spending is being used to build advanced data centers, secure power grids, and develop custom computer chips. There is strong consensus that this massive investment is absolutely necessary for the company to survive the artificial intelligence transition and protect its search monopoly. However, opinions differ on what this means for future returns. Optimistic views see this as a visionary land grab that will lock in long-term dominance and create an insurmountable physical moat. Skeptics, on the other hand, view it as an expensive defensive tax that will permanently lower the company's return on investment. For retail investors, this heavy spending transforms the company from a light, highly profitable software business into a capital-heavy infrastructure utility.

The table compares up to five fiscal years of capital expenditure and research-and-development investment.

Capital expenditure and research and development history
Fiscal yearCapital expenditureR&D spend
2025USD 91.4BUSD 61.1B
2024USD 52.5BUSD 49.3B
2023USD 32.3BUSD 45.4B
2022USD 31.5BUSD 39.5B
2021USD 24.6BUSD 31.6B

Immutable published data

Consensus horizons

The table preserves this publication's original rating, return, and advisor-agreement measurements by forecast horizon.

HorizonRatingScore incl. dividendsCompounded return incl. dividendsDirection agreementSnapshot
1YNEUTRAL-12+2%Not availableORIGINAL
5YNEUTRAL73+50.2%Not availableORIGINAL

Consensus forecast path

The table outlines the frozen bear, consensus, and bull price scenarios for each published forecast period.

PeriodDateBear caseConsensusBull caseAI Advisors
+3MOctober 2, 2026327.69350.24370.4312
+6MJanuary 2, 2027288.36351.91388.9512
+9MApril 2, 2027265.29355.8396.7312
+1YJuly 2, 2027252.03362.19412.612
+15MOctober 2, 2027241.95365.28424.9712
+18MJanuary 2, 2028251.63377.65425.2212
+21MApril 2, 2028236.53383.56446.4812
+2YJuly 2, 2028227.07393.47459.8712
+27MOctober 2, 2028208.9393.58478.2712
+30MJanuary 2, 2029204.72409477.5612
+33MApril 2, 2029214.96421.83501.4412
+3YJuly 2, 2029214.96433.85516.4912
+39MOctober 2, 2029223.56440.62526.8212
+42MJanuary 2, 2030236.97452.5540.0512
+45MApril 2, 2030246.45461.58548.5812
+4YJuly 2, 2030258.77470.5565.0412
+51MOctober 2, 2030269.12481.68587.6412
+54MJanuary 2, 2031277.2496.85606.3712
+57MApril 2, 2031282.74510.91636.6812
+5YJuly 2, 2031291.22527.86674.8812

Frozen comparison context

SPDR S&P 500 ETF Trust forecast context

The benchmark definition and forecast path are frozen with this publication so future benchmark changes do not rewrite the historical comparison.

Benchmark snapshot: 744.78 on July 2, 2026

PeriodDateBear caseConsensusBull case
+3MOctober 2, 2026714.9888736.0909759.6756
+6MJanuary 2, 2027657.7897724.1434782.4659
+9MApril 2, 2027592.0107713.5711790.2905
+1YJuly 2, 2027562.4102723.3821821.9021
+15MOctober 2, 2027539.9138716.6125813.6831
+18MJanuary 2, 2028550.7121738.3442869.6268
+21MApril 2, 2028534.1907746.9119913.1081
+2YJuly 2, 2028534.1907771.9272967.8946
+27MOctober 2, 2028566.2421782.7251938.8578
+30MJanuary 2, 2029574.4406807.3094995.1893
+33MApril 2, 2029597.4182822.7221,074.8044
+3YJuly 2, 2029615.3408846.09851,117.7966
+39MOctober 2, 2029624.1488852.5251,117.7966
+42MJanuary 2, 2030649.1147882.90931,173.6864
+45MApril 2, 2030668.5882900.45851,232.3707
+4YJuly 2, 2030681.9599922.66751,281.6656
+51MOctober 2, 2030695.5991935.90971,320.1155
+54MJanuary 2, 2031716.4671952.29511,293.7132
+57MApril 2, 2031730.7964976.28171,345.4617
+5YJuly 2, 2031738.10441,001.96011,385.8256

Research Provenance

References & Context

This Google consensus analysis combines structured market evidence with independent AI-agent forecasts. External references below are limited to sources recorded by the researcher agents for this forecast batch.

Primary analysis inputs

  • iPulse AI Multi-Agent Forecasts — independent analyst personas, model outputs, and consensus synthesis.
  • iPulse AI Global Events Context — macroeconomic, geopolitical, regulatory, and industry-event context.
  • Structured market history — prices, distributions, volatility, identifiers, and listing metadata.
  • Company earnings and financial statements — revenue, profitability, balance-sheet, cash-flow, and investment trends.
  • Researcher web evidence — public sources consulted to challenge and contextualize the forecast thesis.

Sources retained from AI Researcher searches

Asset-specific · Researcher web

Showing the top 7 of 7 deduplicated sources retained for this batch.

  1. 01sec.govsec.gov
  2. 02alphaspread.comalphaspread.com
  3. 03firstpagesage.comfirstpagesage.com
  4. 04omnibound.aiomnibound.ai
  5. 05stoxcraft.comstoxcraft.com
  6. 06tradingview.comtradingview.com
  7. 07youtube.comyoutube.com

Context retained with this Consensus

The same public-safe market, global-event, and fundamental context supplied to the AI Advisor panel.

03

Global context snapshot

2025 Full-Year Global Market and World-Events Context

Download Archived Snapshot

Coverage 2025-01-01 to 2025-12-31 · Knowledge cutoff 2025-12-31

File size
90.8K bytes
Words
12.8K words
Characters
90.8K characters

This full-year context package covers the principal geopolitical, economic, monetary-policy, technology, trade, energy, and institutional developments that shaped global markets during 2025. It gives the forecasting model a chronological account of major world events together with their likely transmission into growth, inflation, interest rates, supply chains, commodities, currencies, public markets, and sector-level investment conditions.

The package also includes monthly and quarterly macroeconomic and cross-asset reference tables spanning US and international growth, central-bank policy, sovereign yields, major equity indices, foreign exchange, energy, industrial and precious metals, and digital assets. Quarterly and full-year high-impact summaries are integrated; monthly quantitative series remain working values pending final audit, and that qualification is part of the preserved context.

Top 3 market shifts from 2025 Full-Year Global Market and World-Events Context
Top 3 Market Shifts From FileDateStatus
DeepSeek shock and AI economics reset2025-01-27OPEN ENDED TREND
US tariff regime escalation and trade-system rupture2025-02-01ACTIVE POLICY REGIME
Federal Reserve easing cycle after a prolonged hold2025-09-17ACTIVE POLICY REGIME

2026 Year-to-Date Global Market Context through 2026-05-31

Download Archived Snapshot

Coverage 2026-01-01 to 2026-05-31 · Knowledge cutoff 2026-05-31

File size
78K bytes
Words
10.9K words
Characters
78K characters

This year-to-date package described the geopolitical, macroeconomic, monetary-policy, technology, trade, energy, and cross-asset developments available through the batch knowledge cutoff of 2026-05-31.

It supplied dated market and policy context, including rates, sovereign yields, equities, foreign exchange, energy, metals, and digital assets, for the forecast generation workflow.

Top 3 market shifts from 2026 Year-to-Date Global Market Context through 2026-05-31
Top 3 Market Shifts From FileDateStatus
The Iran and Strait of Hormuz conflict shocked energy markets2026-02-28STARTED AND ONGOING
U.S. monetary policy entered the Warsh transition2026-01-30STARTED AND ACTIVE POLICY TRANSITION
Agentic AI and infrastructure spending kept expanding2026-01-01OPEN ENDED
02

Fundamental context

Income statement

34 fields

costOfRevenue · currency_symbol · date · depreciationAndAmortization · +30 more fields

Balance sheet

64 fields

accountsPayable · accumulatedAmortization · accumulatedDepreciation · accumulatedOtherComprehensiveIncome · +60 more fields

Cash flow

32 fields

beginPeriodCashFlow · capitalExpenditures · cashAndCashEquivalentsChanges · cashFlowsOtherOperating · +28 more fields

Outstanding shares

4 fields

date · dateFormatted · shares · sharesMln

annual: 2007-01-01–2026-01-01, 20 periods; quarterly: 2023-06-30–2026-03-31, 12 periods

Currencies cited: USD (quote USD; primary reporting USD; converted/valuation USD).