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Semiconductor company designing high-performance computing and graphics processors for PCs, gaming consoles, data centers, and AI applications.

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AMD Stock Analysis: Compare AI Forecasts & Scenarios

AMD has 14 published AI advisor reports. Compare stock forecasts, individual ratings, price targets and bull/bear scenarios, separately from the consensus AI rating.

Updated on 26 October 2026Deep analysis 21 September 2026

Approved byRusslan Ramdowar, Editor-in-Chief
25 min readAudit All Past Forecasts

AMD AI Forecast Summary

14 advisor opinions · Base subscription

Unlock the rating distributions, return ranges and advisor comparison with a Base subscription.

1 Year Ratings & Returns

Ratings: 11 Sell All · 2 Partial Sell · 1 Neutral

Return range: -33.0% to +11.1%

13 downside · 0 lateral · 1 upside

14 comparable price-return forecasts · Target

5 Year Ratings & Returns

Ratings: 8 Partial Sell · 6 Neutral

Return range: -44.6% to +33.0%

8 downside · 0 lateral · 6 upside

14 comparable price-return forecasts · Target

From $566.42 · End Of Date

What do advisors disagree on

The central disagreement across research perspectives involves whether extraordinary datacenter revenue growth can generate positive net shareholder returns against extreme initial valuation multiples. Bullish perspectives argue that open-software parity and relentless EPYC server CPU share gains will allow earnings to outrun multiple compression. Conversely, skeptical analyses emphasize that customer equity warrants, heavy off-balance-sheet commitments, and bought-in hardware mix cap gross margins and dilute per-share earnings. Resolving this tension requires observing whether sequential non-GAAP gross margins breach the fifty-six percent threshold as rack-scale shipments scale through mid-2027.

Compare all 14 opinions

1 Year & 5 Year ratings, targets & returns · Swipe table to compare

AdvisorModelResearch1 Year5 YearFull Report
Target dateRatingPrice targetPrice returnTarget dateRatingPrice targetPrice return
Universal Investor AI The Polymath FrameworkClaude Opus 5Researcher Mode with Web Search UsedNeutral$629.37+11.1%Neutral$721.78+27.4%Full report
Universal Investor AI The Polymath FrameworkGPT-6 AstraResearcher Mode with Web Search UsedSell All$493.48-12.9%Neutral$645.79+14.0%Full report
Universal Investor AI The Polymath FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$480.23-15.2%Partial Sell$418.55-26.1%Full report
J.P. Morgan AI The Titan FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedPartial Sell$546.35-3.5%Neutral$753.49+33.0%Full report
Niccolo Machiavelli AI The Insider FrameworkGemini 3.8 FlashResearcher Mode with Web Search UsedSell All$483.91-14.6%Neutral$694.28+22.6%Full report
Superintelligence AI The Anthropologist FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$450.82-20.4%Neutral$628.35+10.9%Full report
Superintelligence AI The Anthropologist FrameworkGemini 3.8 FlashThinker Mode with Web Search DisabledSell All$473.72-16.4%Neutral$621.53+9.7%Full report
Elon Musk AI The Visionary FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$418.15-26.2%Partial Sell$542.43-4.2%Full report
Universal Investor AI The Polymath FrameworkGemini 3.8 FlashThinker Mode with Web Search DisabledPartial Sell$505.14-10.8%Partial Sell$539.11-4.8%Full report
Michael Burry AI The Vulture FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$379.35-33.0%Partial Sell$384.05-32.2%Full report
Warren Buffett AI The Value Seeker FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$437.03-22.8%Partial Sell$370.47-34.6%Full report
Ray Dalio AI The Strategist FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$427.73-24.5%Partial Sell$366.69-35.3%Full report
Sherlock Holmes AI The Whistleblower FrameworkGemini 3.8 FlashResearcher Mode but Web Search Wasn't UsedSell All$449.37-20.7%Partial Sell$357.56-36.9%Full report
Warren Buffett AI The Value Seeker FrameworkGemini 3.8 FlashThinker Mode with Web Search DisabledSell All$391.72-30.8%Partial Sell$313.52-44.6%Full report
View all 14 opinions

All 14 opinions are displayed in the table above.

Prices, dates & return assumptions

1 Year and 5 Year identify the original published checkpoints. Current returns compare the dated close with each fixed target over its remaining horizon. Original quarterly tables use the publication-time anchor. Targets and observed prices share a split-adjusted basis.

Lateral means a price return from -1% to +1%, inclusive. Downside is below -1%; upside is above +1%. Ratings use the shared volatility-adjusted calculation and can differ from these price-direction categories.

Ranges and rating distributions use matching currencies, price bases and target dates. Missing or expired checkpoints are excluded; targets are never extrapolated. Shared frameworks and models can produce correlated opinions.

How this research is produced

iPulse AI analyzes each asset through structured investment frameworks using curated data and frontier models. Individual reports feed consensus, rankings, and reviewed selections. Past forecasts are preserved for evaluation as outcomes emerge.

AMD Stock Analysis & Forecast — Universal Investor The Polymath Framework on Claude Opus 5 Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

Claude Opus 5 AI Researcher Web Search Used

5 Year Rating

Neutral

5 Year Price Return

+27.4%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
Anthropic
Model Version:
Claude Opus 5
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD: Historical Prices & Published Forecast (The Polymath Framework on Opus 5 model)

AMD: Historical Prices & Published Forecast (The Polymath Framework on Opus 5 model)Observed prices and the selected advisor's published projection share a split-adjusted price basis. Prices after the forecast start are later observations, not information known at publication. Values in USD.-8.49190.67389.84589788.17Sep 2021Mar 2024Sep 2026Mar 2029Sep 2031Forecast startsToday (UTC)
  • Observed price
  • Published advisor forecast
Observed prices and the selected advisor's published projection share a split-adjusted price basis. Prices after the forecast start are later observations, not information known at publication. Values in USD.

AMD: Quarterly Price Forecasts & Scenarios (The Polymath Framework on Opus 5 model)

Price targets, price returns and complete scenario reasoning

Forecast prices in USD. Price returns are cumulative from the original forecast anchor dated 2026-09-18; they exclude dividends. Swipe horizontally to read every column.

QuarterForecastPrice returnScenario
$593+6.0%

Q3 results in early November should meet the approximately $13bn guide as Helios racks begin shipping, sustaining momentum. Rising Treasury yields near 5.00% cap the multiple expansion that delivery alone would otherwise generate.

$540-3.5%

Q4 print exposes flat non-GAAP gross margin near 56% as rack-scale bought-in content dilutes mix. With a further Fed hike priced at the 4.1% median, duration-heavy semiconductor multiples compress despite intact revenue growth.

$599+7.1%

MI450 volume deployment at Anthropic and Oracle's expanded supercluster land in reported revenue. Data-center growth reaccelerates sequentially, and the first warrant tranche vesting is absorbed as validation rather than dilution.

$629+12.4%

Execution remains strong but SoIC packaging throughput visibly governs shipment pace, capping upside surprise. Investors begin distinguishing contracted gigawatts from delivered racks, trimming the premium paid for backlog visibility.

$548-2.2%

First genuine AI capex digestion scare: a hyperscaler signals deployment deferral into 2029. At roughly 30x forward earnings the stock has no valuation cushion, and positioning unwinds sharply.

$493-12.0%

Diluted share count steps above 1.8bn as warrant tranches vest while growth decelerates. Analysts shift from non-GAAP to fully diluted GAAP EPS, mechanically resetting target prices lower across the sell side.

$532-4.9%

Stabilisation as inference demand proves durable and EPYC server share gains offset accelerator softness. Cheaper entry valuation near 22x forward attracts institutional re-accumulation after two quarters of de-rating.

$564+0.8%

MI500 roadmap detail and expanded TSMC SoIC allocation restore confidence in 2029 throughput. Easing policy rates as US inflation normalises toward target improve the discount rate applied to long-duration earnings.

$587+4.8%

Full-year results confirm data-center operating margin above 30% on a wider revenue base. Growth is slower but higher quality; the market pays for cash conversion rather than gigawatt announcements.

$552-1.5%

Custom ASIC competition bites: Maia and Trainium successors absorb hyperscaler diversification budgets. Merchant accelerator pricing softens, and consensus trims terminal share assumptions for AMD's Instinct franchise.

$585+4.4%

Buyback capacity returns as free cash flow scales beyond reinvestment needs, partially offsetting warrant dilution. Per-share economics improve visibly for the first time since the 2025 partnership agreements.

$614+9.6%

Inference workloads shift GPU-to-CPU ratios toward parity as management predicted, lifting high-margin EPYC attach. Mix improvement finally pushes gross margin above the 56% plateau that constrained 2027 earnings.

$632+12.9%

Steady compounding without narrative catalyst. Revenue growth settles in the twenties percent range and the multiple continues its slow mean reversion toward mature semiconductor norms near 25x.

$601+7.3%

Final warrant tranches vest as deployment milestones complete, delivering the last dilution step. Cyclical inventory correction in client and gaming compounds a modest earnings miss against elevated expectations.

$637+13.7%

Post-dilution clarity removes a structural overhang; the fully diluted share count is finally known and stable. Investors re-underwrite per-share earnings power with the warrant uncertainty resolved.

$662+18.3%

Next-generation accelerator wins at sovereign AI programmes, following the HUMAIN template, diversify the customer base away from three concentrated counterparties. Revenue quality improves faster than revenue growth.

$682+21.8%

Mature-phase compounding. Operating margin near 30% and meaningful buybacks generate earnings-driven returns, while the multiple no longer expands. The AI trade has become an industrial cash-flow story.

$655+17.0%

Periodic semiconductor cycle downdraft as a datacentre build pause follows several years of heavy deployment. Depreciation schedules on 2027-vintage racks pressure customer capex budgets temporarily.

$694+24.0%

Recovery as the replacement cycle for first-generation Helios infrastructure begins, with AMD now an incumbent rather than a challenger. Installed-base switching costs finally show up as pricing power.

$722+28.9%

Horizon close: AMD is a durable number two with roughly $130bn revenue and stable high-twenties operating margin. Returns over the period came from earnings growth, heavily offset by multiple compression and dilution.

Show remaining 18 quarters

All 20 quarters are displayed in the table above.

AMD: Investment Thesis Base Case (The Polymath Framework on Opus 5 model)

The central tension is that AMD is executing beautifully at a price that already assumes it. Data-center revenue is doubling, the order book is contractual rather than aspirational, and EPYC compounds quietly beneath the GPU noise. But the equity was partly used to buy those contracts, packaging throughput governs conversion, and 144x trailing earnings leaves no room for the multiple to help. Over five years I expect earnings to grow several-fold while the multiple compresses roughly in proportion, producing positive but unremarkable per-share returns punctuated by a meaningful drawdown as the AI capex cycle digests during 2027-2028.

  • TTM revenue $41.3bn and EPS $3.89; base case assumes roughly $130bn revenue and $28-32 GAAP EPS by 2031.
  • At a mature 24-26x multiple on those earnings, fair value lands near $700-780 per share.
  • Cross-check: that implies roughly $1.4tn diluted equity value on ~1.95bn shares — plausible, not cheap.

AMD: Scenarios & Signals (The Polymath Framework on Opus 5 model)

Bull Case

The bull case activates when packaging allocation and counterparty funding resolve together. If TSMC's SoIC capacity doubles on schedule while Anthropic's IPO and hyperscaler cash flow underwrite the gigawatt commitments, contracted backlog converts to shipped racks faster than modelled, merchant share breaks toward 20%, and fixed R&D absorption drives operating margin above 30%. Earnings then grow into the multiple rather than under it, and AMD re-rates as a durable duopolist rather than a challenger — a genuine doubling from here.

Bear Case

The bear case activates the moment demand stops being supply-constrained. One deferred hyperscaler deployment reveals the gigawatt order book as optionality, sequential data-center growth flattens, and a 144x trailing multiple has nothing to stand on. Warrant tranches vest into a falling price, custom ASICs absorb the diversification budget, and rack-scale mix keeps gross margin pinned near 56% while R&D above $10bn cannot be cut. The result is a compounding de-rating — earnings disappointment and multiple collapse arriving simultaneously.

Behavioral Alpha Signals

Sentiment, repricing cycle, crowd narrative, catalyst, and macro alignment.

Expected Volatility Regime: High

Greed and Fear Index: +66 / 100 (Greed)

+66

Cycle Position: Momentum

Price action and thesis reinforcement are feeding each other.

EarlyAwareMomentumOvershootReversalCapit.StabilizeMOMENTUM
Figure: Advisor position within the seven-stage market-recognition cycle. The highlighted point marks Momentum.
What does Media Tell? (Crowd Consensus)

The consensus treats AMD as the pre-ordained number two in AI silicon, with gigawatt agreements read as booked revenue and the 2027 EPS estimate already lifted from $12.96 to $15.45 across 33 upward revisions in thirty days [14][22]. A +143% YTD run against Nvidia's +20% is the anchor [22]. Dispersion betrays the fragility: targets span $365 to $1,250 [12].

What Crowds Get Wrong? (Alpha/Value Gap)

The crowd overestimates the per-share value of this franchise while correctly identifying the franchise. Two overlooked items do the damage: the 320 million warrant shares that are excluded from the 1.66bn count underpinning a $927bn market cap, and non-GAAP gross margin guided flat at approximately 56% despite a violent data-center mix shift [6][13]. Rack-scale systems carry bought-in memory and networking; the business is scaling revenue faster than it scales margin, and the equity is paying customers to deliver it.

When will Value Gap Repricing Happen? (Repricing Catalyst)

The trigger is the first quarter where data-center revenue growth decelerates sequentially while diluted share count visibly steps up on a warrant tranche — plausibly late 2027. The first observable sign is analysts switching from non-GAAP EPS to fully diluted GAAP EPS in their models.

How is Asset Influenced by Macro Regime?

The macro wind is in its face on valuation and at its back on demand. A 3.75%-4.00% Fed funds rate with the 10-year near 5.00% directly compresses the discounted value of earnings dated 2029 and beyond — precisely where AMD's value sits. Demand, conversely, is funded by hyperscaler cash flow, which remains largely rate-insensitive.

AMD: Positive & Negative Factors, Risks & Opportunities (The Polymath Framework on Opus 5 model)

Base-Case Forces

Top Drivers / Tailwinds

Base-case positive forces

Structural or operating forces that support this advisor thesis. Base-case means above 70% probability of occurrence. Probabilities are model estimates; unquantified forces retain their published group.

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Top Drivers / Tailwinds with asset-specific estimated impacts and thesis rationale
Driver / TailwindCategoryEst. stock-price impactEst. earnings impactWhy it matters
Gigawatts Converted INTO ContractsCompetitive Positioning+62%+170%AMD turned opportunistic AI demand into named multi-year commitments: OpenAI 6 GW, Meta up to 6 GW, Anthropic up to 2 GW, Oracle's 50,000-GPU MI450 supercluster [8][10][3][11]. Data Center revenue already doubled to $6.7bn in Q2 2026 with $2.1bn segment operating income [1][2]. Contracted volume underwrites the fab, packaging and HBM4 pre-buys that make the 2027 ramp financeable, and persists because switching costs rise once racks are installed.
THE Second Source ImperativeSector And Industry+28%+48%Hyperscalers with Nvidia at roughly 80% merchant share will pay a strategic premium for a credible alternative [17]. That buyer intent, not AMD's technical parity, is the actual demand mechanism; it converts to pricing power on Helios racks and to volume floors even when MI455X trails Rubin on raw performance. The effect endures while accelerator supply stays scarce and fades only if custom ASICs absorb the diversification budget.
Epyc: THE Quiet CompounderInnovation And Product+22%+42%Beneath the GPU noise sits a 70% year-over-year rise in EPYC server CPU sales [1]. Management argues agentic inference compresses GPU-to-CPU ratios from 1:8 toward 1:1, meaning every Helios rack drags Venice CPUs with it [1][10]. Server CPU carries materially higher incremental margin than bought-in-content rack systems, so this line quietly funds the R&D budget that the accelerator war consumes. It persists because Intel's roadmap recovery remains unproven.
Fixed COST Absorption AT ScaleOperational Efficiency+18%+38%The evidence chain is unambiguous: FY2025 operating income rose 94% on 34% revenue growth, and TTM operating income of $6.49bn on $41.3bn revenue implies roughly 15.7% margin against 10.66% for FY2025. R&D of $8.09bn in FY2025, now running above $10bn annualised, is largely fixed across a wider revenue base [snapshot]. Each incremental data-center dollar therefore drops through faster than gross margin alone suggests, provided volume actually ships.
Top Frictions / Headwinds

Base-case negative forces

Expected frictions that can slow, cap, or damage this advisor thesis. Base-case means above 70% probability of occurrence. Probabilities are model estimates; unquantified forces retain their published group.

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Top Frictions / Headwinds with asset-specific estimated impacts and thesis rationale
Friction / HeadwindCategoryEst. stock-price impactEst. earnings impactWhy it matters
Gravity AT 144 Times TrailingMacroeconomic And Macrofinancial-38%+0.0%At $560 the stock carries a trailing P/E of 144, EV/EBITDA of 86.4, P/S of 22.4 and an FCF yield of 0.91% [snapshot], against a Fed funds range of 3.75%-4.00% and a US 10-year near 5.00%. Discount rates of that order are lethal to duration-heavy multiples. Even outstanding delivery compresses the multiple mechanically as earnings arrive; the re-rating has already happened, and +143% YTD means the crowd paid for it.
Success PAYS THE Customer FirstCapital Allocation-19%-9.0%AMD issued OpenAI and Meta warrants for 160 million shares each at $0.01, vesting on gigawatt milestones with stock-price gates escalating to $600 [8][9][13]. Combined, that is roughly 19-20% dilution to the 1.66bn share count the $927bn market cap rests on; fully diluted, the claim is nearer $1.11tn. The perverse mechanic: the share price rising through $600 is itself a vesting trigger, so per-share value leaks precisely when the thesis works.
Custom Silicon EATS THE MiddleCompetitive Positioning-17%-22%The threat is not only Nvidia. Broadcom's AI ASIC revenue hit $8.4bn in a quarter with a $73bn backlog, and Microsoft — an announced Helios customer — was reportedly preparing Maia 300 [17][18]. Internal accelerators compete for the same SoIC, CoWoS-L and HBM4 allocation while structurally capping AMD's merchant share. This caps the terminal margin investors are extrapolating and persists as long as hyperscaler capex justifies bespoke design teams.
Copper TO Copper BottleneckOperational Efficiency-14%-16%MI455X clears two serial advanced-packaging steps: 2nm compute dies bonded to 3nm fabric via SoIC, then CoWoS-L with HBM4 [16]. Nothing reaches CoWoS-L until SoIC clears it, and SoIC sits near 15,000-20,000 wafers per month against CoWoS at 120,000-140,000 [16]. Backlog is not revenue when the narrower step governs throughput; contracted gigawatts will convert slower than the order book implies through at least 2028.

Risks & Opportunities

Tail Risks

Plausible downside scenarios

Less likely downside scenarios that could materially hurt the outcome if they occur.

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Tail risks with plausibility, asset-specific potential impact and scenario rationale
Tail scenarioChance of OccurringStock Price ImpactWhy plausible / what changes
THE Capex Digestion AIR Pocket36%-45%Should one anchor counterparty defer deployment — Oracle already posted negative $5bn free cash flow for the quarter ended 31 August 2026 despite $664bn of RPO [context] — the gigawatt order book would be re-read as optionality rather than revenue. With the stock at 144x trailing earnings, a single guidance reset toward flat sequential data-center growth would trigger a violent de-rating, most likely in the 2027-2028 window as first-generation Helios racks reach depreciation review. Probability stays below 50% because inference demand is presently supply-constrained, not demand-constrained.
Taiwan OR Export Control Rupture18%-42%AMD is fabless and single-sourced at TSMC for 2nm compute dies, SoIC and CoWoS-L [16]. A Taiwan Strait escalation, a renewed China accelerator ban of the kind that cost $800m in MI308 inventory charges in Q2 2025 [15], or rare-earth shipment friction of the type already reported in September 2026 [context] would sever supply with no substitute. Impact would be immediate and severe; probability stays low because both Washington and Beijing currently prefer managed friction to rupture, but tail risk persists across the full horizon.
Tail Opportunities

Plausible upside scenarios

Less likely upside scenarios that could materially improve the outcome if they occur.

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Tail opportunities with plausibility, asset-specific potential impact and scenario rationale
Tail scenarioChance of OccurringStock Price ImpactWhy plausible / what changes
Merchant Share Breaks Twenty Percent30%+75%If MI450/Helios delivers rack-level TCO parity and AMD converts the OpenAI, Meta and Anthropic commitments into repeat orders, merchant accelerator share could move from roughly 5-7% toward 20% by 2029 [17]. That would push data-center revenue past $90bn annually and non-GAAP EPS beyond $40, letting the market apply a durable-franchise multiple rather than a catch-up multiple. It stays below 50% because Nvidia's CUDA lock-in, larger packaging allocation and Rubin cadence have repeatedly defeated challengers; watch the 2028 MI500 launch window for confirmation.
AI LABS GET Investment Grade Balance Sheets33%+40%Anthropic's reported November 2026 IPO and any eventual OpenAI listing would convert equity-story counterparties into rated, cash-funded buyers, with bankers already seeking investment-grade ratings for both [context][aicredit]. Contracted gigawatts would then read as receivables rather than aspirations, collapsing the counterparty discount embedded in AMD's backlog and lifting the multiple investors pay for the same forecast earnings, most plausibly during 2027. It remains under 50% because Altman ruled out a 2026 OpenAI listing and a September 2026 US antitrust complaint names both labs [context].

AMD: References & Context (The Polymath Framework on Opus 5 model)

Research Provenance24 sources · 5 search terms
Researcher modeWeb Search Used

Web Search was used. The retained search terms and consulted sources are shown below.

Context supplied to the model

Public-safe inputs retained with this immutable forecast publication.

  1. Market datainmemory_base_placeholders__latest_eod_close_price_with_stats_and_fundamentals__var1
  2. Global context in this runUsed
    2026 Year-to-Date Global Market and World-Events Context Through September 20

    January 1-September 20, 2026: monetary tightening, energy security, trade restrictions, AI financing and divergent growth; five leading market themes.

  3. Fundamental data in this runUsed
    Fundamental Context Passed as Input to the Models

    Time range: Annual fiscal periods: 2014-12-31–2025-12-31, 12 periods; Quarterly fiscal periods: 2023-09-30–2026-06-30, 12 periods. Currencies cited: USD (quote USD; primary reporting USD; converted/valuation USD).

  4. Global contextStandard global market and cross-asset context
  5. Subject contextEquity-specific subject and market context
  6. Task frameworkStandard investment-forecast task guidelines
  7. Advisor frameworkUniversal Investor The Polymath
  8. Forecast output requestedEquity Extended Investment Thesis (4 Quadrants and Alpha Asymmetry) + Pct Change Timeseries for Close Price with Rationale, (5Y Quarterly)

Search terms retained

  1. 1.AMD Q2 2026 results data center GPU revenue guidance
  2. 2.AMD MI450 Helios OpenAI 6 gigawatt warrant dilution
  3. 3.AMD stock September 2026 price target analyst 2027 EPS estimate
  4. 4.MI450 Helios shipping HBM4 supply CoWoS constraint September 2026
  5. 5.Nvidia Rubin ramp AMD market share AI accelerator 2027 competition

Sources retained for this advisor

Original Published Forecast

Inspect the original revision and its sealed receipt.

How to understand these AI forecasts

How does an AI-generated forecast for AMD differ from Wall Street analyst consensus?

Wall Street consensus aggregates estimates from human analysts. For listed companies, these typically include brokerage price targets, earnings forecasts and ratings. iPulse AI's forecast for AMD is a separate AI consensus generated through its own research workflow. Different inputs, forecast dates, assumptions and weighting can therefore produce different targets.

We believe the era of investment research conducted only by human analysts is drawing to a close. Frontier models increasingly match or exceed expert work on selected professional benchmarks, including evaluations covering financial analysis. Research on multi-model synthesis also shows that combining model responses can improve on a single model's output in the tasks studied.

iPulse AI generates transparent investment research and market forecasts using a swarm of AI configurations, analytical frameworks and frontier models. The workflow applies multiple verification, challenge and synthesis layers, with selective editorial review under Russlan Ramdowar, Editor-in-Chief. Published assumptions, disagreements and past forecasts remain available so readers can inspect the reasoning and audit outcomes.

For a research cycle covering 426 assets with 14 perspectives per asset, our internal workload model estimates approximately 114,000 hours of traditional analyst work. It assumes 16 hours per individual research report, 28 hours per consensus for synthesis and validation, plus shared data preparation. Parallel AI execution allows a deep-analysis cycle across hundreds of assets to complete in up to one day. The human-hours figure is an illustrative workload estimate, rather than measured time savings. Our research methodology explains the automated workflow.

If iPulse AI produces thousands of investment reports - work estimated to require over 100,000 traditional analyst hours - isn't that just "AI slop" at scale?

No. iPulse AI produces AI-generated research at scale, but the purpose is depth of analysis, comparison, and accountability. The number of reports is a consequence of the research process; it is not the goal.

iPulse AI does not produce thin copies of existing websites, articles, or investment reports. We use frontier models as an Analytical Intelligence layer. Our Financial Experts select, validate, curate, and supply the input data, including proprietary inputs, so the models work from a prepared evidence base.

In Researcher mode, models can also search the web to check current information, test assumptions, and investigate evidence that may be missing from the supplied data. This can include lobbying expenditure, management developments, regulatory decisions, or other facts relevant to a particular investment thesis. Thinker mode focuses on deep reasoning over the supplied data and context.

Our process goes much further than asking ChatGPT to evaluate a stock. Our team designs structured investment frameworks inspired by the approaches of influential investors, economists, political thinkers, and technological visionaries. These are AI analytical frameworks, not reports written or endorsed by the people who inspired them.

Each asset is analyzed separately through multiple frameworks and configurations, using specialized inputs and task instructions. Different perspectives examine different questions: valuation, business quality, technological disruption, macroeconomic conditions, political influence, financial risks, and more.

Cloud infrastructure allows these workflows to run at scale. That scale also comes with quality assurance at scale: up to 30% of report-generation attempts fail our QA checks. We rerun failed attempts until a clean report passes those checks. Users are presented only with reports that have passed QA.

This explains the volume. The value comes from what we do with the analysis.

A second layer synthesizes the individual reports into an asset-level consensus. This is where we recommend starting when researching a particular asset: it brings together the central thesis, supporting evidence, risks, and disagreements while preserving access to the underlying opinions.

We then compare assets through our proprietary iPulse AI Consensus Score, whose methodology is publicly explained. This helps users move from understanding one asset to comparing opportunities across the analyzed universe.

A further research and editorial layer examines leading candidates, checks current evidence, and develops focused Editorial Top Picks across broad-market and dedicated asset categories. Human editors review the evidence and approve each published edition. For example, explore our Best Long-Term Investments.

But the process does not end when a report is published. We preserve past forecasts so users with the relevant subscription can inspect what the system actually said and compare it with subsequent market outcomes. As more forecasts reach their target dates, the historical record becomes more useful for evaluating which approaches worked, which failed, and under what conditions.

We publish research on our website, arXiv, and Zenodo, alongside explanatory writing on Medium. We use the evidence to refine our data, frameworks, validation, and workflows, and document important findings in Lessons Learned.

The standard we ask users to judge us by is the reasoning, evidence, and historical record behind the research.

What do the individual AI forecasts for AMD represent?

Each report presents one AI advisor configuration's analysis of AMD, including its investment framework, assumptions, forecast path, drivers, and risks. The reports provide different analytical perspectives on the same asset so you can examine the reasoning behind their conclusions.

How are these forecasts different from the AI Consensus?

The individual forecasts show each advisor's reasoning and outlook. The AI Consensus synthesizes those views into an overall assessment of AMD. Start with the consensus for an overview, then explore individual reports to understand the strongest supporting arguments, objections, and disagreements.

Are the named AI advisors the actual investors or public figures?

No. They are AI analytical frameworks inspired by publicly documented investment approaches or broader schools of thought. Their reports are generated by AI within the iPulse AI research workflow. The names do not imply participation, endorsement, or a personal forecast from those individuals.

What is the difference between Thinker and Researcher mode?

Thinker mode reasons over the supplied data and context without external web search. Researcher mode can investigate additional external evidence when needed. Being configured as Researcher does not necessarily mean a particular report used web search; consult the report's research details where available.

Why do the AI advisors disagree about AMD?

Different frameworks emphasize different evidence and assumptions. One may prioritize valuation, another technological growth, and another macroeconomic or political risk. Disagreement helps identify which assumptions deserve closer investigation. Agreement can also reflect shared inputs or model behavior, so it should not be treated as proof of accuracy.

How often are the reports updated?

The analytical content is updated during deep-analysis batch runs. We run each batch across all covered assets within the same research cycle, providing a consistent basis for comparison, scoring, and ranking. Currently, these batches run every few weeks; our aim is to increase the frequency to once a week.

Between batches, we update price-based expected returns and ratings daily using the latest available market prices. For example, a sharp price increase can reduce an asset's expected upside and change a previously published Buy rating. This keeps the price-based assessment current without presenting the underlying analytical report as newly generated.

Editorial Top Picks receive an additional weekly review, providing another check on whether current evidence continues to support the selected investment theses.

What can I read without signing in, and what requires a subscription?

You can read the public preview and the methodology explanations available on this page. Additional advisor reports, comparisons, configuration details, and historical records may require signing in and a subscription that includes the relevant asset or feature. Access prompts explain what is needed.

How do these forecasts contribute to Live Rankings and Editorial Top Picks?

Individual forecasts contribute to the asset's consensus and quantitative scoring. Live Rankings compare assets using the published scoring methodology. Editorial Top Picks add a further review of selected candidates, current evidence, investment theses, and risks, followed by human editorial approval. For example, explore our Best Long-Term Investments.

Can I inspect previous forecasts for AMD and see what happened?

Forecast History lets you inspect earlier published forecasts, subject to your subscription access. Read each forecast against its original publication date, assumptions, and target dates. As outcomes become observable, those records support evaluation of what worked and what did not. Broader findings and methodological changes appear in Research and Lessons Learned.

AMD Stock Analysis & Forecast — Universal Investor The Polymath Framework on GPT-6 Astra Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

GPT-6 Astra AI Researcher Web Search Used

5 Year Rating

Neutral

5 Year Price Return

+14.0%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
OpenAI
Model Version:
GPT-6 Astra
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Universal Investor The Polymath Framework on Gemini 3.8 Flash Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-26.1%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — J.P. Morgan The Titan Framework on Gemini 3.8 Flash Model

J.P. Morgan AI advisor icon

J.P. Morgan AI · The Titan Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Neutral

5 Year Price Return

+33.0%

Framework Description & Key Record Dates

Investment Framework

The Titan Framework: Empire Dominance Assessment

Analysis Framework Description: The Titan looks for who sets the terms. It grades whether an asset controls price, infrastructure, distribution, capital flows, or ecosystem access, then asks whether that dominance is expanding or inviting a counterattack.

  • Measure pricing power and whether customers have credible alternatives.
  • Map infrastructure, distribution, data, supply, or capital choke points.
  • Evaluate whether acquisitions strengthen the moat or merely add complexity.
  • Identify antitrust, succession, and regulatory antibodies against dominance.
  • Ask whether the asset collects rent from the system or pays rent to someone else.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Niccolo Machiavelli The Insider Framework on Gemini 3.8 Flash Model

Machiavelli AI advisor icon

Machiavelli AI · The Insider Framework

Gemini 3.8 Flash AI Researcher Web Search Used

5 Year Rating

Neutral

5 Year Price Return

+22.6%

Framework Description & Key Record Dates

Investment Framework

The Insider Framework: Power Audit Framework

Analysis Framework Description: The Insider asks who has power over the asset, how durable that power is, and whether the market is pricing the protection, threat, or political optionality correctly.

  • Map who can protect, subsidize, regulate, restrict, or destroy the asset's economics.
  • Track lobbying, procurement, sanctions, antitrust, and strategic policy exposure.
  • Identify insiders, capital networks, and institutions that are quietly positioned.
  • Estimate whether political power creates a valuation premium or discount.
  • Separate visible business performance from invisible power structure risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Superintelligence The Anthropologist Framework on Gemini 3.8 Flash Model

Superintelligence AI advisor icon

Superintelligence AI · The Anthropologist Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Neutral

5 Year Price Return

+10.9%

Framework Description & Key Record Dates

Investment Framework

The Anthropologist Framework: Civilizational Compute Framework

Analysis Framework Description: The Anthropologist observes the financial system from above human emotion and asks whether an asset is biologically anchored, thermodynamically improving, informationally central, civilizationally aligned, and resilient to AI/quantum/space displacement.

  • Score biological demand anchoring: what human or civilizational need does the asset serve?
  • Measure thermodynamic efficiency and whether the asset reduces waste or increases throughput.
  • Map information network position, density, and dependency within the broader system.
  • Assess long-arc alignment with technological, demographic, and infrastructure evolution.
  • Estimate displacement risk from superior substitutes and frontier-tech transitions across AI, quantum, and space.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Superintelligence The Anthropologist Framework on Gemini 3.8 Flash Model

Superintelligence AI advisor icon

Superintelligence AI · The Anthropologist Framework

Gemini 3.8 Flash AI Thinker Web Search Not Used

5 Year Rating

Neutral

5 Year Price Return

+9.7%

Framework Description & Key Record Dates

Investment Framework

The Anthropologist Framework: Civilizational Compute Framework

Analysis Framework Description: The Anthropologist observes the financial system from above human emotion and asks whether an asset is biologically anchored, thermodynamically improving, informationally central, civilizationally aligned, and resilient to AI/quantum/space displacement.

  • Score biological demand anchoring: what human or civilizational need does the asset serve?
  • Measure thermodynamic efficiency and whether the asset reduces waste or increases throughput.
  • Map information network position, density, and dependency within the broader system.
  • Assess long-arc alignment with technological, demographic, and infrastructure evolution.
  • Estimate displacement risk from superior substitutes and frontier-tech transitions across AI, quantum, and space.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Thinker
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Elon Musk The Visionary Framework on Gemini 3.8 Flash Model

Elon Musk AI advisor icon

Elon Musk AI · The Visionary Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-4.2%

Framework Description & Key Record Dates

Investment Framework

The Visionary Framework: Paradigm Shift Framework

Analysis Framework Description: The Visionary asks whether the asset is building the future or clinging to the past, then validates physics, S-curve timing, execution velocity, and a forced AI/quantum/space disruption verdict.

  • Start from first-principles feasibility instead of consensus market extrapolation.
  • Run a mandatory frontier-tech disruption test for AI, quantum, and space exposure.
  • Estimate where the asset sits on an S-curve: early, inflecting, maturing, or saturating.
  • Model future TAM from deployment constraints, infrastructure, and adoption friction.
  • Grade execution velocity through iteration speed, talent density, and learning loops.
  • Separate real paradigm shifts from narratives that only sound futuristic.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Universal Investor The Polymath Framework on Gemini 3.8 Flash Model

Universal Investor AI advisor icon

Universal Investor AI · The Polymath Framework

Gemini 3.8 Flash AI Thinker Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-4.8%

Framework Description & Key Record Dates

Investment Framework

The Polymath Framework: Eight-Lens Investment Analysis Framework

Analysis Framework Description: Select the lenses with the greatest explanatory power, resolve their disagreements across horizons, and connect the evidence to a consistent thesis, scenario path, and verdict.

  • Ownership and value: examine economic rights, competitive advantage, capital allocation, and the price paid.
  • Regimes and constraints: connect credit, liquidity, policy, demand, and physical bottlenecks to the asset.
  • Innovation and adaptation: test adoption economics, AI and robotics exposure, substitution, and value capture.
  • Forensics and incentives: reconcile accounting, cash conversion, dilution, governance, and missing evidence.
  • Expectations and reflexivity: distinguish changes in fundamentals from changes in narratives and valuation.
  • Probabilities and resilience: use base rates, competing scenarios, catalysts, and the capacity to survive the wait.
  • Power and the rules of the game: assess political interests, regulatory influence, protection, and dependency.
  • Industry structure and consolidation: identify pricing power, chokepoints, alliances, and the economics of integration.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Thinker
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Michael Burry The Vulture Framework on Gemini 3.8 Flash Model

Michael Burry AI advisor icon

Michael Burry AI · The Vulture Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-32.2%

Framework Description & Key Record Dates

Investment Framework

The Vulture Framework: Contrarian Forensic Framework

Analysis Framework Description: The Vulture starts with the crowd story and looks for the single fact, balance sheet pressure point, or dated catalyst that can force a repricing in either direction.

  • State the consensus story clearly before attacking it.
  • Search for balance sheet fragility, liquidity pressure, and hidden leverage.
  • Compare market narrative against hard evidence, filings, costs, and positioning data.
  • Identify the catalyst that could force the market to care.
  • Estimate upside and downside asymmetry if the crowd is wrong.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Warren Buffett The Value Seeker Framework on Gemini 3.8 Flash Model

Warren Buffett AI advisor icon

Warren Buffett AI · The Value Seeker Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-34.6%

Framework Description & Key Record Dates

Investment Framework

The Value Seeker Framework: Value Ownership Framework

Analysis Framework Description: The Value Seeker turns market noise into an ownership decision: understand the asset, judge the moat, estimate owner economics, evaluate management or governance, compare price with value, and demand a financial fortress.

  • Treat every asset as a business to be owned, not a ticker to be traded.
  • Stay inside the circle of competence before assigning conviction.
  • Map the moat type, moat durability, and whether the moat is widening or eroding.
  • Estimate owner economics through cash generation, ROIC, and reinvestment runway.
  • Audit management integrity, governance, and capital allocation rationality.
  • Compare intrinsic value to the market price with a real margin of safety.
  • Stress-test the financial fortress before accepting long-term ownership risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Ray Dalio The Strategist Framework on Gemini 3.8 Flash Model

Ray Dalio AI advisor icon

Ray Dalio AI · The Strategist Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-35.3%

Framework Description & Key Record Dates

Investment Framework

The Strategist Framework: Economic Machine Framework

Analysis Framework Description: The Strategist treats markets as cause-effect systems. The page explains how this persona separates cyclical expansion from deleveraging, temporary policy support from durable productivity, and short-term noise from long-wave regime change.

  • Identify the current short-term debt cycle and whether liquidity is expanding or contracting.
  • Map long-term deleveraging pressure and the asset's sensitivity to credit stress.
  • Assess reserve-currency, empire-cycle, and geopolitical pressure around the asset class.
  • Separate temporary policy support from durable productivity-driven value creation.
  • Translate macro regime into asset positioning, expected catalysts, and horizon risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Sherlock Holmes The Whistleblower Framework on Gemini 3.8 Flash Model

Sherlock Holmes AI advisor icon

Sherlock Holmes AI · The Whistleblower Framework

Gemini 3.8 Flash AI Researcher Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-36.9%

Framework Description & Key Record Dates

Investment Framework

The Whistleblower Framework: Forensic Deduction Framework

Analysis Framework Description: The Whistleblower turns analysis into a case file: what is claimed, what evidence must exist if it is true, what evidence is missing, and what verdict follows from the exhibits.

  • Define the theory of the case behind the market narrative.
  • List the evidence that should exist if management or market claims are true.
  • Inspect filings, footnotes, revenue quality, related parties, and omitted metrics.
  • Stress-test contradictions between narrative, numbers, incentives, and behavior.
  • Deliver a clear case-file verdict rather than vague suspicion.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Researcher
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

AMD Stock Analysis & Forecast — Warren Buffett The Value Seeker Framework on Gemini 3.8 Flash Model

Warren Buffett AI advisor icon

Warren Buffett AI · The Value Seeker Framework

Gemini 3.8 Flash AI Thinker Web Search Not Used

5 Year Rating

Partial Sell

5 Year Price Return

-44.6%

Framework Description & Key Record Dates

Investment Framework

The Value Seeker Framework: Value Ownership Framework

Analysis Framework Description: The Value Seeker turns market noise into an ownership decision: understand the asset, judge the moat, estimate owner economics, evaluate management or governance, compare price with value, and demand a financial fortress.

  • Treat every asset as a business to be owned, not a ticker to be traded.
  • Stay inside the circle of competence before assigning conviction.
  • Map the moat type, moat durability, and whether the moat is widening or eroding.
  • Estimate owner economics through cash generation, ROIC, and reinvestment runway.
  • Audit management integrity, governance, and capital allocation rationality.
  • Compare intrinsic value to the market price with a real margin of safety.
  • Stress-test the financial fortress before accepting long-term ownership risk.
Full framework methodology
Model Producer:
Google
Model Version:
Gemini 3.8 Flash
Mode Used:
Thinker
Web Search Not Used
Forecast Target Date:
Latest Known Price Date for Return Estimate Refresh:
Forecast Published On:
Forecast Generated On:
Supplied Context Data, Prices & Events Cutoff:
Model knowledge cutoff:
Return calculation details

Current returns use the observed price and remaining time to the fixed published target. The quarterly table retains the original forecast anchor.

Read full advisor report

Global Events Context Provided to the Model

Opus 5 has a published knowledge cutoff of May 1, 2026. Curated global-events context covering events through Sep 20, 2026 was supplied to supplement its built-in knowledge with important developments.

2026 Year-to-Date Global Market and World-Events Context Through September 20

Download Archived Snapshot

Coverage 2026-01-01 to 2026-09-20 · Knowledge cutoff 2026-09-20

January 1-September 20, 2026: monetary tightening, energy security, trade restrictions, AI financing and divergent growth; five leading market themes.

Archived context covering 2026-01-01 to 2026-09-20. Its recorded scope includes monetary policy and rates; geopolitics energy and shipping; trade and industrial policy. Selected highlights reflect the analysis cutoff, not current world conditions.

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File size
84.8K bytes
Words
12.4K words
Characters
84.8K characters
Est. tokens (characters ÷ 4)
21.2K tokens

Fed raised rates to 3.75%-4.00%; ECB hike is in force and BOJ increase starts September 24. Markets through September 18, bitcoin through September 19.

Selected highlights from the archived 2026 Year-to-Date Global Market and World-Events Context Through September 20 snapshot
Selected highlights from this archived snapshotDateStatus
Renewed monetary tightening amid persistent inflation2026-01-30ACTIVE POLICY REGIME
Iran/Hormuz conflict and wider energy-security disruption2026-02-28ONGOING
Tariff legal reset and strategic supply restrictions2026-02-20ACTIVE POLICY REGIME

Representative Sources of the Context File

And more sources from the retained context package.

Fundamental Context Provided to the Model

Time range: Annual fiscal periods: 2014-12-31–2025-12-31, 12 periods; Quarterly fiscal periods: 2023-09-30–2026-06-30, 12 periods.

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

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Income statement

9 fields

depreciationAndAmortization · ebit · ebitda · grossProfit · +5 more fields

Balance sheet

12 fields

cash · commonStockSharesOutstanding · longTermDebt · netDebt · +8 more fields

Cash flow

5 fields

capitalExpenditures · dividendsPaid · freeCashFlow · salePurchaseOfStock · +1 more field