We are excited to announce an exclusive #AI# Roundtable in partnership with @e27co on April 24. We are bringing together founders and business leaders to explore how companies can turn AI experimentation into real, sustainable business impact. The future of AI isn't just about more compute; it’s about how that compute is integrated and utilized. As we explore a full-stack, vertically integrated approach. During this roundtable, we’ll dive into: 🔹Open conversations on real AI scaling challenges. 🔹Strategies on moving from experimentation to impact. 🔹Peer-to-peer learning on moving from experimentation to impact. 🔹Explore how AI Cloud platforms can simplify infrastructure and accelerate AI initiatives Don’t miss out on this exclusive discussion. Register now👉: #necloud# #enterpriseai# #AIStrategy# #AILeadership#

Last Friday, together with @e27co, we hosted an exclusive roundtable with founders and senior leaders to discuss how businesses can move from #AI# experimentation to real-world deployment. The discussion was energetic and highly practical, with participants sharing real use cases, deployment challenges, and solution approaches across industries, from SME operators to enterprise teams. One message came through clearly: AI is moving beyond experimentation into execution, integration, and measurable business impact. A few key takeaways stood out: 🤖 AI adoption is gaining traction across customer service, coding, sales, productivity, robotics, IoT, and agentic AI. 🎯 Scaling AI still requires companies to address cost, infrastructure complexity, trust, regulation, data privacy, and internal readiness. 🚀 The fastest-moving companies are shortening planning cycles, launching practical solutions earlier, testing in real-world environments, and iterating quickly. At Bitdeer AI, we believe scalable AI infrastructure is essential to helping businesses turn AI ideas into production-ready outcomes. As our commitment is to eliminate infrastructure complexity and cost barriers, enabling teams to build, deploy, and scale AI with speed and confidence. Thank you to e27 and all participants for the insightful discussion. #AIAdoption# #necloud# #AIInfrastructure# #AI#

Moats in our neocloud theme: $APLD Ellendale, Macquarie financed $BTDR Bhutan power, SEALMINER ASIC $CIFR AWS anchor tenant validation $CLSK low cost power per MW $CORZ multi-billion hosting backlog $CRWV GPU operator, OpenAI $DGXX 400MW, Blackwell ready pods $HIVE BUZZ HPC GPU subsidiary $HUT vertically integrated power $IREN cleanest HPC revenue ramp $KEEL $533M liquidity, three campuses $MARA largest scale, biggest footprint $NBIS lowest cost per MW operator $RIOT Texas grid scale power assets $SLNH behind the meter, 4.3 GW $WULF Core42 deal, nuclear adjacent $WYFI $865M Nscale anchor contract The important stuff? Secured power capacity, signed anchor tenant contracts, cost per MW competitiveness, and execution speed on conversion. I think #SLNH# offers deep value. NFA. Will keep updating...

I'll make this super clear for people wondering if $DGXX or $SLNH is more asymmetric: They serve two completely different purposes, in different layers of the same supercycle. Both genuinely asymmetric in their own way. Both sit in the Neocloud ecosystem. $DGXX as a GPU-as-a-Service operator and $SLNH as the renewable powered data center beneath it. Different theses, different risks, same tailwind. $DGXX (~$600M MC) - GPU-as-a-Service operator deploying $NVDA Blackwell GPUs directly to customers. Initially shared at ~$4 (up 105%+ now). > Similar model as $CRWV (~$60B MC), $NBIS (~$45B), $IREN (~$20B). First AI revenue contract signed. $1.1B $CBRS colocation deal. Hans Vestberg / $BLK connection. > 1.9% institutional ownership leaves massive room for re-rating. Earnings tomorrow, GPU rental starts on Friday. Risks: Early stage, $750M shelf filed (dilution capacity), negative margins, execution heavy. $SLNH (~$250M MC) - Renewable powered AI data centers. Wind farm acquisition closes vertical integration loop. Initially shared at ~$1 (up 65% so far). > Same renewable power thesis as $TLN (~$17B), $CEG (~$106B), $VST (~$50B). 4.3GW development pipeline. Difference between them is instead of wind farm → grid → data center, $SLNH does wind farm → data center. > Dorothy campus operational and expanding. Nasdaq compliance just regained. Earnings May 19. Risks: Overhang from active dilution. Cash burning. Execution risk on Dorothy 3 (300MW+ campus). Both are very early stage at this point. Both have execution risk. But both have real catalysts incoming. As for dilution, that's a risk with any early stage company. Again, bears were saying the same thing about $PLTR at ~$15. Now the same bears would full-port if it ever dips to $100. Valuation gap between current MC and what their competitors are trading at is what makes both asymmetric in their own layers.

"An overnight success that took five years" @_ConorMoore at @CoinDesk Live on finding pmf: blockchain rails make loan origination and settlement atomic, neoclouds needed capital that tradfi couldn't move fast enough to provide, and the AI boom brought both together.

We are proud to share that Bitdeer AI has been ranked in the Bronze Tier SemiAnalysis GPU Cloud ClusterMAX™ Rating in its April 2026 report, standing out among more than 80 neocloud providers 🎉. SemiAnalysis looks beyond surface-level pricing, evaluating the technical pillars that determine true business value: 🔹 Reliability & Support 🔹 High-Performance Networking & Storage 🔹 Orchestration Excellence 🔹 Total Cost of Ownership (TCO) As AI workloads scale in complexity, raw hardware is only half the story - operational efficiency and architectural integrity are the only way forward. This recognition validates our commitment to building enterprise-grade AI infrastructure ready for the most demanding training and inference workloads. Thank you, @SemiAnalysis_ team, for the deep dive and the recognition. We are already hard at work on our next phase of optimizations, further refining our cloud capabilities and O&M excellence to stay ahead of the curve. 🔗 Read the full SemiAnalysis breakdown: #BitdeerAICloud# #SemiAnalysis# #AIInfrastructure# #GPUCloud# #neocloud# #CloudComputing#

What does it really take to move AI from pilot projects to production at scale? At Echelon Singapore 2026, one of Asia’s leading startup and tech events, Bitdeer AI will join the ecosystem shaping the next wave of AI implementation. Meet our team to explore how businesses are turning AI into real-world outcomes, from infrastructure to model deployment and agent workflows🤖. Attending the event? Book time in advance with our solutions experts, Eric Si or Yong, to connect on-site: #AIInfrastructure# #EnterpriseAI# #neoCloud# #echelonsingapore#

Right now, the majority of AI workloads are executed on GPUs, but that could soon be changing with market adoption of AI application specific integrated circuits (ASICs). @Luxor's Mike San Miguel said that hyperscalers have been utilizing these ASICs for some time, but now neoclouds are making moves to leverage them as well. "They're just starting to hit the open market now The hyperscalers have been doing versions of these for about a decade. What's different now is that, as opposed to being bespoke chips for hyperscalers, we're starting to see new manufacturers that are producing these AI ASICs, and as a result these are going to start hitting the neoclouds. "You're probably going to see them hitting the market in scale in 2027 when they're going to start entering fleets for a lot of these neoclouds." This raises a a business question for AI companies, however, because unlike GPUs which can be used for inference and training, AI ASICs can only be used for one or the other. "A GPU can be used for inferencing training. But with ASICs, they're purpose built for one or the other -- they can't do both. "But that's the advantage. You get a much higher output...what it's probably going to look like in the next 2 to 5 years -- beecause these things take time to roll out -- is a hybrid approach."

A quick peek at our NVIDIA GB300 NVL72 deployment process. NVIDIA #GB300# NVL72 brings next-generation rack-scale AI performance to the era of reasoning, with ultra-dense compute optimized for large-scale training and high-throughput inference. More to come. Stay tuned👐! #GPU# #AIInfrastructure# #neocloud#