Who mentions $MOD most on Surfaced?

@kaizen_investor (KaizenInvestor) mentions $MOD most — 3 mentions in the past 12 months.

When was $MOD first mentioned on Surfaced?

The earliest tracked mention of $MOD in the Surfaced archive was on Mar 4, 2026.

How many voices track $MOD?

5 FinTwit voices have mentioned $MOD 7 times in the past 12 months.

What kind of voices mention $MOD most?

$MOD is mentioned most by Long Research voices on Surfaced.

$MOD — FinTwit mentions on Surfaced

Recent mentions

7 mentions

@thevalueistMay 27, 2026 · 1:54 PM

$MOD (Bloomberg) -- Modine forecast adjusted Ebitda for 2027 of $650 million to $680 million. 2027 YEAR FORECAST Sees adjusted Ebitda $650 million to $680 million, estimate $658 million (Bloomberg Consensus) FOURTH QUARTER RESULTS Adjusted EPS $1.71, estimate $1.55 EPS $1.36 Net sales $954.4 million, estimate $920.8 million Adjusted Ebitda $146.1 million, estimate $143.9 million COMMENTARY AND CONTEXT Reports Sees Fy27 Net Sales Growth Between 20% & 35% "We anticipate another strong year for our Data Centers business, supported by our strong customer relationships and significant order book."

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@itsmichaelluuMay 25, 2026 · 1:11 PM

My notes on POWER AND COOLING bottleneck companies: ❄️AI POWER & COOLING PLATFORMS 1. $VRT ~$327 | Avg PT ~$600. Stock up 291% in a year; 25 analysts see more upside on record $15B backlog and 30% revenue growth. 2. $ETN ~$391 | Avg PT~$450. Record Q1 2026 revenue of $7.5B; raised full-year guidance on surging data center and electrical infrastructure demand. 3. $SU (Schneider) ~$276 | Avg PT ~$300. Strong Buy consensus; largest global AI power & cooling player by revenue, not directly accessible to most US retail investors. 🔋 POWER GENERATION — Grid/Utilities 4. $CEG ~$294 | Avg PT ~$366. Strong Buy; 24% upside seen as nuclear + Calpine acquisition creates America's largest private power producer. 5. $VST ~$156 | Avg PT ~$225. Strong Buy consensus with 44% implied upside; signed 2.1 GW nuclear deal with Meta, upgraded to investment grade by Fitch. 6. $NEE ~$90 | Avg PT ~$100. Buy rated; $67B Dominion acquisition pending, which would create the world's largest regulated utility. StockAnalysis 🔋 BACKUP GENERATION 7. $GEV ~$1036 | Avg PT ~$1,300. Buy consensus with 16% upside; $GS PT $1,328 on record $163B backlog and AI-driven electrification demand. 8. $CAT ~$880 | Avg PT ~$1000. Record $63B backlog; Power & Energy revenue up 41% in Q1 2026 — AI generator demand is the structural story now. 9. $CMI ~$640 | Avg PT ~$560. Buy consensus; data center generator exposure growing alongside core industrial recovery cycle. ⚡ DISTRIBUTED POWER 10. $BE ~$282 | Avg PT ~$217 Up 240% in 2026; record $20B backlog, Oracle 2.8 GW fuel cell deal but RBC still sees further upside despite stretched valuation. 11. $ENR (Siemens Energy) ~$134 Buy rated globally. Switchgear and grid equipment leader benefiting from same data center buildout tailwinds as peers. ⚡ELECTRICAL INFRASTRUCTURE & DISTRIBUTION 12. $ETN see above (dominant across switchgear, UPS, and power distribution) 13. $ABB (ABBNY) ~$60 Strong Buy from European desks. Switchgear/grid equipment key beneficiary of global power infrastructure buildout. 🌡️ COOLING INFRASTRUCTURE — Air Cooling 14. $TT (Trane) ~$450 | Avg PT ~$500. Buy consensus; data center HVAC bookings "off the charts" per CEO; Q1 organic bookings +24% with record backlog. 15. $CARR ~$64 | Avg PT ~$80. Buy rated, 19% upside seen; data center orders up 500%+ the market just hasn't rewarded it yet. 16. $JCI ~$139 | Avg PT ~$150. Stock up 44% in a year; Morgan Stanley PT $140 on surging data center cooling backlog and Q2 orders +30% YoY. ❄️ LIQUID COOLING 17. $NVT ~$169 | Avg PT ~$165 (stock above target). Strong Buy; Q1 revenue +53%, EPS +63%, raised growth guidance to 26-28% Citi PT $187, Barclays $190. 18. $MOD ~$261 | Avg PT ~$266. Strong Buy; data center sales up 78% YoY pure-play liquid cooling with GLJ Research street-high $290 PT. 🔌 POWER DISTRIBUTION 19. $LR (Legrand) ~$150 European Buy consensus. Data center PDU and power distribution specialist; underowned by US investors. ♻️ RESHARE this list and make 1 comment, I'll share you the #1 BOTTLE NECK stock under $10 right now.

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@mkt_sentimentMay 11, 2026 · 12:24 PM

6 boring stocks that got rerated during the AI buildout. Sterling Infrastructure $STRL — Civil contractor specializing in site development. Q1 2026 revenue jumped 92% YoY to $825.7M, with E-Infrastructure growing 174% on hyperscaler data center work. Comfort Systems USA $FIX — Installs and services HVAC, plumbing, piping, and power systems. Backlog hit a record $12.46B in Q1 2026, with technology projects (predominantly data centers) making up 45% of 2025 revenue. Modine Manufacturing $MOD — Experts in thermal management systems. Data-center cooling revenue surged 78% last quarter, and Airedale launched its TurboChill 3+MW hybrid chiller in Jan 2026, aimed at high-density GPU racks. Quanta Services $PWR — North American contractor for electric power infrastructure. Q1 2026 revenue rose to $7.87B (from $6.23B) and adjusted EPS climbed to $2.68 (from $1.78), with a record backlog of $48.5B GE Vernova $GEV — GE's energy spin-off focusing on heavy-duty gas and aeroderivative turbines, wind turbines, hydro, and nuclear systems. Q1 2026 orders surged 71% YoY to $18.3B, with backlog at $163B. The Electrification segment alone booked $2.4B in data-center orders in Q1, more than all of 2025. Caterpillar $CAT — World's largest maker of construction and mining equipment and a major player in reciprocating engines, gas turbines, gensets, and locomotives. Backlog hit a record $51B entering 2026 (+71% YoY) as Power & Energy crossed $10B in annual power-gen sales.

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@investmentguru_May 10, 2026 · 11:26 AM

The Complete Data Center Stack: Where the AI Infrastructure Money Flows Most investors still think AI is just about GPUs. That’s incomplete. AI is an infrastructure buildout, and the real opportunity spans the entire data center stack. Every inference, every training run, and every deployed model depends on multiple layers working together. Here’s the breakdown: 1. Compute Silicon (The Brain) Tickers: $NVDA, $AMD, $AVGO, $INTC This is the foundation. GPUs, CPUs, accelerators, and custom silicon power training and inference. Why it matters: - Compute demand keeps rising with larger models - AI workloads are forcing faster chip innovation - Custom ASICs are becoming a major trend 2. Server OEMs & Solutions (The Hardware Layer) Tickers: $SMCI, $DELL, $HPE, $VRT, $ETN, $MOD Chips need systems. These companies assemble and deliver the physical AI servers and power systems. Why it matters: - AI racks are denser and hotter - Power distribution is now critical - Cooling is becoming a competitive advantage 3. Memory & Storage (The Hidden Bottleneck) Tickers: $SNDK, SK Hynix, $MU, $WDC, $P, Samsung, $NTAP AI models consume massive amounts of memory bandwidth and storage. Why it matters: - High-bandwidth memory is becoming strategic infrastructure - Data storage demand rises with AI deployment - Faster access = better model performance 4. Networking & Connectivity (The Nervous System) Tickers: $ANET, $CSCO, $MRVL, $CRDO, $CIEN, $NOK AI clusters must communicate at ultra-high speed. Why it matters: - Faster networking reduces latency - Data movement is becoming expensive - Scale depends on interconnect efficiency Key idea: AI cannot scale without bandwidth. 5. Neoclouds & Physical Infrastructure (The New Builders) Tickers: $NBIS, $IREN, $CRWV, $APLD $CIFR $DGXX These companies provide specialized AI infrastructure and hosting. Why it matters: - Cloud alternatives are growing - AI-native infrastructure is becoming valuable - Capacity shortages create pricing power 6. Energy (The Ultimate Constraint) Tickers: $CEG, $NEE, $EOSE, $GEV, $EQT, $VST $OKLO $BE $FLNC AI consumes enormous electricity. Power availability is becoming a limiting factor. Why it matters: - Grid demand is surging - Battery storage is essential - Reliable baseload power matters Final Thought The market often focuses on one winner. But AI infrastructure is an ecosystem. If you want to understand where capital flows next, follow the stack: Compute → Servers → Memory → Networking → Infrastructure → Energy The biggest winners in the AI cycle may not always be the obvious names. Sometimes the best opportunities are in the supporting layers that make the whole system possible.

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@kaizen_investorApr 5, 2026 · 8:13 PM

War-driven energy inflation is inevitable, but companies solving the AI bottleneck are uniquely positioned to protect their margins. Their critical role gives them the pricing power to simply pass costs onto customers. As a result, these bottleneck companies could emerge as prime investment opportunities during market corrections. - Memory and HBM: SK Hynix, Samsung Electronics, $MU - Packaging and OSAT: ASE Technology, $AMKR, JCET Group - Server and Rack Integration: $SMCI, $DELL, $HPE, Foxconn - Networking Silicon: $AVGO, $MRVL, $CSCO, $ANET - Photonics and Optical Components: Ayar Labs, $ALAB, $CRDO, $COHR, $LITE - Power, Thermal management and Grid: $VRT, $MOD, $NVT, $SU.PA, $IREN, $CIFR

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@kaizen_investorMar 14, 2026 · 9:23 PM

Big companies are announcing huge layoffs. $AMZN to cut 30,000 jobs, $META potentially 15,000 jobs. $ORCL, $XYZ, $CRM, $ASML,… all announced cuts as well. It’s not that those companies are doing badly financially, they report record earnings with growing margins. They all expect a lot from AI. What we saw from perplexity yesterday is a first glimpse of what AI will be able to do in the near future. With jobs being cut, more and more money will flow towards R&D and more importantly the CapEx. Everyone is investing in AI and those numbers will only increase. We are still in the early stages of AI and the supply chain is still evolving. There are some major bottlenecks forming that are slowing down the progress of AI. The most profitable companies are spending billions to make sure they can control these bottlenecks. This creates possibilities for investors. The major bottlenecks at the moment: - Memory and HBM: SK Hynix, Samsung Electronics, $MU - Packaging and OSAT: ASE Technology, $AMKR, JCET Group - Server and Rack Integration: $SMCI, $DELL, $HPE, Foxconn - Networking Silicon: $AVGO, $MRVL, $CSCO, $ANET - Photonics and Optical Components: Ayar Labs, $ALAB, $CRDO, $COHR, $LITE - Power, Thermal management and Grid: $VRT, $MOD, $NVT, $SU.PA, $IREN, $NBIS, $CIFR No financial advice

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@kaizen_investorMar 4, 2026 · 4:47 PM

I have written a full article on the AI chip supply chain. The supply chain is structured into 4 different phases with 13 layers: 1. Raw Materials: $SHECY, $SUOPY, GlobalWafers, $WAF.DE, $SHWDF, $AXTI, $IQE 2. Manufacturing Equipment: $ASML, $ASM.AS, $AMAT, $LRCX, $KLAC 3. EDA & Core Intellectual Property: $SNPS, $CDNS, $ARM, $RMBS 4. Chip Design: $NVDA, $AMD, $INTC, $QCOM 5. Foundries: $TSM, Samsung Semiconductor, $SMIC 6. Memory and HBM: SK Hynix, Samsung Electronics, $MU 7. Packaging and OSAT: ASE Technology, $AMKR, JCET Group 8. Server and Rack Integration: $SMCI, $DELL, $HPE, Foxconn 9. Networking Silicon: $AVGO, $MRVL, $CSCO, $ANET 10. Photonics and Optical Components: Ayar Labs, $ALAB, $CRDO, $COHR, $LITE 11. Power, Thermal management and Grid: $VRT, $MOD, $NVT, $SU.PA, $IREN, $CIFR 12. Hyperscalers: $AMZN, $GOOGL, $MSFT, $META 13. AI Storage, platforms and Data: VAST data, Weka, NetAPP, $PLTR, Blue Yonder, $KXSCF The article covers it all.

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