$FPS 0.59 Developing

Notable posts

• @StockSavvyShay bullish 883 eng

  WHO GETS PAID TO TURN AI DATA CENTERS ON U.S. data centers above 100MW are set to explode this decade but power, interconnects and electrical infrastructure determine how much of that pipeline actually gets built: What gets the data center online • $GEV sits at one of hardest bottlenecks with a $176B backlog stretching into 2031 & equipment pricing already more than 20% above Q4 2025 • $VRT captures ~$3.8M per AI MW from grid to rack & is moving further upstream through UtilityInnovation Group adding behind the meter power that can bypass interconnection delays. • $BE attacks same problem differently with onsite fuel cells letting data centers generate power without waiting years for the grid. • $FPS sits between the substation & building through switchgear & transformers with utilization still around 30% against capacity being built to support as much as $5B of revenue. Who already owns the power • $IREN, $CIFR, $CORZ, $APLD & $WULF already control power and interconnects from their bitcoin infrastructure which is why IREN could go from 5MW of active AI capacity toward a 480MW target in roughly a year since the scarce asset is energized land. • $VST owns generation already connected to grid giving it direct exposure to rising data center power demand while others wait through interconnection queues. • $CEG pairs an already interconnected nuclear fleet with hyperscaler demand giving it one of cleanest ways to monetize 24/7 power scarcity through long term contracts. What gets paid inside the rack • $ON $NVTS become more important as AI moves toward 800V DC in 2028 since content per rack could rise from ~$15K toward ~$115K as more of the power conversion stack moves inside system. • $VICR is one of purest plays on converting and delivering power efficiently close to the processor as rack densities rise. The market can announce hundreds of new data centers but a ton of the value accrues to whoever controls the power, interconnects and electrical infrastructure required to make them operational.

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• @StockSavvyShay bullish 707 eng

  Morgan Stanley sees data center power demand growing ~9x to nearly 79 GW by 2029. At ~$25M of annual compute revenue per MW, that implies up to $2T of compute revenue and makes entire power stack a picks and shovels opportunity for the AI buildout: • Grid equipment: $GEV, $FPS, $HUBB • Busway & liquid cooling: $VRT, $ETN • Battery storage: $EOSE, $TSLA, $FLNC • Cooling & CDUs: $VRT, $TT, $MOD, $JCI, $NVT • Energized sites: $IREN, $CIFR, $CORZ, $APLD, $WULF

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• @StockSavvyShay bullish 425 eng

  THE 5 BIGGEST BOTTLENECKS POWERING THE AI ECONOMY The way I'm thinking about AI winners today is that the market is moving beyond the simple question of which mega-cap company spends most on AI and has been rewarding the companies that control the scarce inputs, contracted capacity, data movement, power infrastructure, edge compute and workflow layers that make the AI economy function. These are the five bottlenecks I'm watching most: • Memory | $MU, Samsung, SK Hynix Memory is the clearest scarce input because HBM feeds the accelerator, only a few companies can make it at volume and buyers are locking in supply through long-term agreements that create revenue visibility through the end of the decade. • Connectivity | $AVGO, $MRVL, $ALAB, $CRDO, $AAOI, $ANET Connectivity determines whether AI clusters can move data fast enough because training runs span tens of thousands of chips that need to act like one machine. Once copper runs out of reach that causes the cluster depends on optics, retimers, switches and custom silicon to keep the system moving. • Power | $CEG, $VST, $GEV, $FPS, $VRT, $NVTS, $TLN, $ON Power determines whether new AI data centers can actually come online because the binding constraint is shifting from getting chips to getting megawatts so the value flows to the companies that control generation, grid equipment, power delivery, thermal management and efficiency. • Compute | $NBIS, $CIFR, $IREN, $APLD, $WULF, $CORZ, $CRWV Compute capacity is overflow layer when hyperscalers are sold out. Capital alone doesn't guarantee GPU access which is why buyers are signing multi-year contracts for clusters before they are even fully built. • CPU | $NVDA, $AMD, $INTC, $ARM, $QCOM On-device CPU (edge compute) becomes next bottleneck as AI moves into inference, agents, PCs, phones, vehicles and physical devices.

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• @StockSavvyShay bullish 421 eng

  Bernstein shows data center modularization shifting more value toward power and cooling rewarding companies that can industrialize AI buildout while making already energized sites even scarcer: • Grid equipment: $GEV, $FPS, $HUBB • Busway & liquid cooling: $VRT, $ETN • Battery storage: $EOSE, $TSLA, $FLNC • Cooling & CDUs: $VRT, $TT, $MOD, $JCI, $NVT • Energized sites: $IREN, $CIFR, $CORZ, $APLD, $WULF

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• @wallstengine bullish 275 eng

  FORGENT $FPS Q4’26 EARNINGS HIGHLIGHTS 🔹 Revenue: $462M (Est. $430M) 🟢; +94% YoY 🔹 Adj. EPS: $0.25 🔹 Adj. EBITDA: $113M (Est. $108M) 🟢; +163% YoY 🔹 Adj. Net Income: $77.3M (Est. $73.6M) 🟢 🔹 Backlog: $3.0B; +256% YoY FY27 Guide: 🔹 Revenue: $2.4B-$2.6B (Est. $2.08B) 🟢 🔹 Adjusted EBITDA: $575M-$625M (Est. $511M) 🟢 🔹 Adj. EPS: $1.26-$1.40 Segment Performance: 🔹 Powertrain Solutions: +259% FY26 Other Q4 Metrics: 🔹 Bookings: $1.5B; +375% YoY 🔹 Book-to-Bill: 3.3x 🔹 Net Income Margin: 14.3% 🔹 Cash Flow From Operations: $74M Key Updates: 🔹 Revenue, Adj. EBITDA and Adj. Net Income all exceeded the high end of May guidance 🔹 Announced incremental Powertrain Solutions capacity expansion in Tijuana Comments: 🔸 “Our performance demonstrates that Forgent is not only benefiting from industry growth, but also gaining share and significantly outpacing the broader market,” 🔸 “we are making an incremental investment in dedicated e-House and Powerskid production in Tijuana.”

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• @Venu_7_ bullish 174 eng

  Forgent $FPS back on my radar! One of the critical pieces of the 2027 AI power infrastructure buildout - transformers, switchgear, E-Houses. 8 weeks building a new base + attempting a Stage 1 → Stage 2 transition. A 9/21-day EMA backtest would be a good spot. https://t.co/wFupv5E19O

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• @Venu_7_ bullish 163 eng

  Want to find potential market leaders BEFORE their major moves? Study CAN SLIM. CAN SLIM is one of my favorite frameworks for finding potential market leaders, introduced by legendary William O’Neil. C - Current earnings growth A - Annual earnings growth N - New products, catalysts or new highs S - Supply + demand L - Leaders, not laggards I - Institutional sponsorship M - Market direction I spent some time screening AI + Tech names using this framework, while also looking for strong relative strength + stocks still building bases rather than chasing extended breakouts. A few names on my radar: $NVDA $ANET $PLTR $CRWD $MU $RBRK $NET $FTNT $PANW $LITE $SMTC $AMD $CLS $NOW $SNOW $DDOG $CRDO $FORM $HPE $COHU $VICR $FPS $IMOS $ASML $TSM Not every name will check every CAN SLIM box at the same time - and even the best company can be a bad entry if you’re chasing it after a major move. The real edge is finding companies where earnings are accelerating + relative strength is improving + institutions are accumulating while the stock is still building a healthy base. That’s when I want the name on my radar Fundamentals tell me WHAT to own. Technicals tell me WHEN to build the position.

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• @wallstengine bullish 141 eng

  $FPS +13% https://t.co/q34YeHDeAn

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