Tolga Kilicli

Amazon is rolling out Trainium3 chips in 2026 which can deliver 4.4x more compute and 4x greater energy efficiency than Trainium 2. We expect customer adoption to expand beyond Anthropic in 2026, possibly providing the Street with more proof points that Amazon has a compelling hardware offing. Also press reports have suggested that Amazon and OpenAI on in discussions for a potential deal to use Trainium chips to run AI workloads.

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Podonos just raised $2.4 million in pre-seed funding, and the signal is loud and clear: the voice AI industry finally has someone measuring the sound, not just making the noise. Based in Los Gatos, Podonos is building the infrastructure layer that decides whether voice AI feels real, human, and ready for primetime. The industry is racing toward a projected $47.5 billion by 2034, but growth without standards is chaos. Podonos is making sure the hype actually holds up when you press play. At the center is ✦Soohyun Bae, PhD in Computer Engineering from Georgia Institute of Technology and Y Combinator W22 alumnus. His track record stretches from engineering roles at Google Maps to leading AR mapping at Niantic, plus co-founding Bobidi and TickTock AI. He has seen what happens when technology scales without proper #guardrails. With Podonos, he's betting the future of AI isn't just about how smart the models get, but how believable and trustworthy they sound. Podonos evaluates models across naturalness, similarity, emotion, recognition accuracy, pronunciation, tone, and resilience. Add in #personaconsistency, and suddenly you're not just testing machines, you're testing their ability to pass as human. The twist? They deliver results in under twelve hours, when the legacy approach drags on for two months. In AI time, that's the difference between relevance and irrelevance. Investors caught the beat. Serac Ventures, led by Kevin Moore, took the lead, backed by NAVER D2SF, the venture arm of South Korea's NAVER, and KAIST Venture Investment Holdings. Together, they've now backed Podonos twice, with this $2.4 million round stacking on top of a $750,000 raise in March 2025. Total funding sits at $3.15 million, and every dollar is pointed toward scaling #engineering, refining #analysistools, and launching into the U.S., Europe, and Southeast Asia. This isn't theory. Podonos already serves Resemble AI, Play AI, and Sanas AI, hitting six-digit ARR by November 2024 with just four employees. Their secret weapon is reach: 150,000 evaluators spread across nine languages and thirteen locales, forming a human-in-the-loop platform that blends speed, rigor, and global coverage. That reach lets Podonos deliver evaluations at a scale and pace that no competitor has matched. The strategy is ambitious: expand into high-demand sectors like #healthcare, #finance, #gaming, and #advertising, then move beyond voice into #multimodal model evaluation, spanning video AI and large language models. The mission isn't just to check if AI works, it's to decide if AI works for people. #Startups #StartupFunding #EarlyStage #VentureCapital #PreSeed #AI #AIInfrastructure #Voice #VoiceAI #VoiceTech #Infrastructure #Technology #Innovation #TechEcosystem #StartupEcosystem #Hiring #TechHiring If software engineering peace of mind is what you crave, Vention is your zen.

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I was honored to deliver a keynote at this year’s Marconi Society event today at UCLA. I opened with how control (AI), compute, and connectivity technologies each began in the 1980s as monolithic, centralized, and specialized silos, gradually evolving over decades toward distributed, composable fabrics. Today, they are undergoing a deeper transformation toward “distributed convergence,” where these three layers increasingly operate as a "distributed, composable, integrated, and intelligent fabric". Control (AI/ML): symbolic, rule-based AI → neural networks → deep learning → foundational models/LLMs → multi-agent systems Compute: mainframes → PCs → client/server → virtualization → central cloud → distributed cloud (edge) Connectivity: 1G–2G (circuit-switched) → 3G (hybrid) → 4G (packet-switched) → 5G (cloud-native) → toward 6G (AI-native) The next decade will be defined by distributed convergence, where control (AI), compute, and connectivity function as one cohesive system. This is the essence of the AI-native era. Building on this foundation, I reflected on the connectivity progression: We advanced from circuit-switched 2G, to IP-native 4G that unified transport for voice, video, and data, and then to cloud-native 5G, which transformed networks into programmable platforms for distributed workloads. We are now entering the AI-native era, where a new class of devices and applications demands a new class of networks, aligned to the dominant unit of computation: first packets, then workloads, now agents. This set the stage for the architectural continuum I discussed: - Packets move bits - Workloads run services - Agents pursue goals These layers are additive, not replacements. AI-native systems continue to rely on packet-switched substrates and cloud-native orchestration, while adding agentic and semantic capabilities on top. I framed this evolution as a progression from programmability → orchestrability → autonomy → sustainability, guided by new architectural constructs for AI-native networks , including a dual-stack model: today’s syntactic Internet alongside an emerging semantic Internet. My sincere thanks to my colleague, Sheryl Genco, Ph.D. and the Marconi Society for the invitation and for its leadership in bringing the community together to advance the future of connectivity for societal impact.

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🚀 The $100B OpenAI–NVIDIA deal is more than just numbers. For NVIDIA, it secures long-term demand and strengthens its moat in the AI infrastructure race. For OpenAI, it diversifies beyond Azure but also brings massive pressure to commercialize faster. For the supply chain, from TSMC to Quanta, this signals huge opportunities but also greater geopolitical risks. 👉 The deeper question: does this scale of investment mark AI’s transition from experimentation to industrialization? And if so, what does it mean for smaller innovators? #AI #Semiconductors #NVIDIA #OpenAI #Taiwan

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"Microsoft's cloud technology is helping to facilitate an unprecedented Israeli surveillance system, according to a joint investigation by the Guardian, +972 Magazine, and Local Call. They report that in late 2021, Microsoft CEO Satya Nadella met with the head of Israel's Unit 8200, the country's elite military intelligence arm, to discuss giving the unit access "to a customized and segregated area within Microsoft's Azure cloud platform." Per the report, Nadella wasn't told what the storage would be used for—retaining and analyzing millions of phone calls from Palestinians each day, something the military's own servers couldn't handle from a storage or computing perspective." - Newser https://lnkd.in/e7AqJAsK

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Microsoft’s Satya Nadella recently noted that compute is no longer the bottleneck in AI. The real limits now lie in energy and data-center capacity — even as GPU inventories grow. This marks a structural shift: the challenge has moved from scaling compute to optimizing energy systems, cooling, and spatial design. Futurist Peter Leyden foresees a convergence of artificial intelligence, clean energy, and bioengineering that could transform not only technology but also agriculture and sustainability — a pathway toward a civilizational singularity. The new frontier of innovation is therefore not in silicon, but in how efficiently intelligence, energy, and biological systems are integrated. #SatyaNadella #Microsoft #AI #CleanEnergy #DataCenters #Singularity #FutureofTechnology #Sustainability

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NVIDIA strategic equity stakes of recent months: $1 billion to Nokia (targets former Lucent/Bell Labs business) $5 billion to Intel (INTC) in September $100 billion to OpenAI. $500 million in autonomous vehicle startup Wayve based in London $667 million in UK cloud provider Nscale.

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Code on Wheels: The SDV Era in China & Europe It’s an honor to be part of this panel hosted by Blu EV in collaboration with AUC Innovation Hub. Speed vs. rigor: In China, the product development lifecycle is typically ~18 months end-to-end; in Europe it’s closer to ~48–60 months due to deeper validation, compliance, and supply-chain governance. Cybersecurity: Europe leads with enforceable frameworks (UNECE R155/R156, ISO/SAE 21434) that make secure OTA and incident response non-negotiable. Many Chinese programs still lag behind on cybersecurity governance, especially for domestic launches—alignment is essential for global exports. Big thanks to our panelists - Ahmed Soltan, Mohamed Hegazy, Muhammad Kamal #BluEV #AUCInnovationHub #SDV #Automotive #EV #Cybersecurity #BatterySwapping #OTA #ProductDevelopment #China #Europe #EgyptTech #EVBattery

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