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Five months ago, I wrote a love letter to meeting assistants. In that letter, I called them the wedge into something much bigger. …I underestimated how fast that wedge would move 😱 That’s why, today, we're publishing the sequel: the fall edition of our Meeting Assistant Market Map 🍂 Since May: • Superhuman acquired Fathom.ai to put meeting intelligence at the heart of its AI suite. Congrats to both teams! • Granola got valued at $1.5B for capturing meetings without a bot. • Otter.ai secured $320M from General Catalyst to become the enterprise voice system of record. • Wispr Flow shipped a notetaker, then raised $280M at a $2B valuation. In the previous edition, I said these tools would become much more than notetakers. 🚀 It’s already happening: they have become the company’s memory and its biggest productivity lever, from mid-call reminders to real-time CRM updates. All of it runs on voice, the most underused data source in every company. And we bet Gladia on that. The last five months made me more sure of that bet. Read the fall edition: https://lnkd.in/emSjU2mf

Is multilingual a silent churn risk? 48% of meeting assistant users are in multilingual meetings frequently or in almost every meeting. Far fewer list it as a top priority. A need users rarely name is easy to miss on a roadmap. See where the gap is widest, market by market.

Your team doesn’t need to switch tools to try open models. Keep Codex and add open models served by Baseten. Same picker, nothing new to learn.

Open models served by Baseten are now in OpenAI’s marketplace. Get more model options in Codex without changing tools or workflows.

AI agents are starting to act on production systems faster than security standards can keep up. Our free checklist covers five pillars and 24 core standards every multi-user agent should meet before acting on production systems, plus 5 advanced standards for higher-security environments.

It was a pleasure to meet the Greek Prime Minister Kyriakos Mitsotakis last week at the AI Impact Summit. Greece is doing pioneering work - ambitious, tech-forward initiatives like gov gr are already making it much simpler for citizens to access the public services they need. It was great to talk about how responsibly deployed AI can build on that foundation, and about the future of AI in government, European innovation, and Greece’s trajectory - building on an impressive pace of progress over the past few years, with even more to come. And we already have some incredible team members from Greece - and it’s easy to see where that talent and ambition come from. Excited to be back in Greece in May to continue the conversation - and, of course, to enjoy a bit of the weather too!

The Polish state is taking a stake in ElevenLabs. Through Vinci, part of the BGK Group, the Government of Poland is joining our latest round investors Sequoia, Andreessen Horowitz, ICONIQ and others as one of our backers. The teams at BGK and Vinci are great. More so than capital, they bring expertise and are phenomenal partners as we continue building this ecosystem. Together, we will help power AI Lab Poland: a new joint undertaking bringing together technology, science, talent and investment to support the next generation of AI innovation in Poland. We are already honoured to work with established partners such as Centrum e-Zdrowia, LOT Polish Airlines, InPost, Uniwersytet Warszawski and the iconic Piotr Fronczewski. Across all of them, one thing stands out: the ambition to build global technology from the start, lead with innovation and show what is possible from here. Piotr Dabkowski and I met in high school in Warsaw. Building on top of incredible foundations set by the generations that came before us, we had the privilege to study abroad, learn from many of the best people around the world and build a company with global impact from the start - from Europe. We now have an incredible talent network in Warsaw, London and across many capitals in the region and we’re committed to strengthening it even further in the years to come. We believe Europe has the potential to lead on technology and applied AI. Recently, we hosted the ElevenLabs Summit in Warsaw, and it felt like a homecoming - with such an incredible scene brewing here. With the Polish state now investing in ElevenLabs, we are proud to keep strengthening the connections between Poland, Europe and the global technology ecosystem. Thank you to Minister Andrzej Domański, to the teams at BGK and Vinci who believed in this, and to everyone who helped make it happen.

Discover enterprise-ready infrastructure, models, and AI workflows.

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Frontier-quality results don’t need frontier-model prices. Fireworks brings fast, cost-efficient open-model inference to Microsoft Foundry. Run open or custom models for less per token, and put that spend toward your existing Azure commitment. Billed through your existing Azure account and MACC-eligible.

Find out where a lower-cost model can deliver the quality you need. The Specialized Intelligence Index compares models on benchmarks built by domain experts. Explore the trade-offs between quality, cost, and speed, and see where training gives specialized models an edge. Explore the index.

Today we're launching AssemblyAI-TTS-1, our first text-to-speech model: frontier naturalness, super fast, instant cloning, and just $15 per 1M characters. On the combination of accuracy, naturalness, and cost... no other TTS model is better. It's perfect for conversational voice agents running at scale. We trained AssemblyAI-TTS-1 from scratch on our own proprietary data, and we serve it on our proprietary voice inference stack. The same inference stack that already handles 3M+ hours of voice/day for the 1M+ developers building with our transcription models. This means AssemblyAI-TTS-1 is frontier quality + super fast (80ms TTFB), and supports unlimited scale with no rate limits or capacity to reserve up front - just like our leading transcription models. AssemblyAI-TTS-1 is live TODAY with: - Frontier naturalness - #1 in accuracy on independent benchmarks - $15 per 1M chars - 90ms latency (TTFA) - Unlimited concurrency/rate-limits - Instant voice cloning - A library of 4,000+ voices to pick from Available today in our API. Watch Johnny order a pizza with our new TTS model in the video below 👀 And stay tuned... we have a ton of improvements and updates on the way.

Every founder is told to find product-market fit in 18 months. Dylan Fox waited five years for the voice AI market to exist. Now his company runs the voice AI behind Granola - 100M API calls a day! When we met at their office, I wanted to know how you tell conviction apart from stubbornness when the market says no for half a decade. Between 2017 and AssemblyAI's Series A in 2022, he had two choices: take early voice models and sell them to court reporting agencies, or keep refining the core speech models until developers finally needed them. He chose to wait. He kept the team capped at four people, kept personal expenses near zero, and treated those five years as an apprenticeship in deep learning. When I was building Paloma, I lived on the opposite side of that equation. I knew that AI matchmaking was bound to be a thing, but chased immediate market signals, and eventually ran out of steam. Watching Dylan build proved that surviving the quiet years requires obsessive faith in the future itself. We also get into: -> Why the last 10% of an AI product is the whole product -> “Everyone’s still just figuring it out” -> 180% net dollar retention with zero contracts -> Married to a founder whose company took off while his didn’t -> Why he deleted X in 2023 (I think he’s back tho) -> “I’m just a builder” Full episode link in the comments.

You already wire LLMs into apps. The Nebius Agentic AI Builder exam tests the harder parts: fine-tuning vs RAG, rate limits, cost per task. 60 minutes online, Credly badge valid 3 years. Early-bird pricing until December 31.

LIVE from NVIDIA GTC Berlin with Nebius!! 🎬✌🏼 I had the honor of joining Nebius Live to share my takeaways from Jensen’s keynote, together with the wonderful Deirdre Bosa, former CNBC journalist. One thing I was particularly excited about: Europe has something the AI revolution desperately needs: INDUSTRIAL EXPERTISE. What I loved most about Jensen Huang’s keynote today? Europe has a seat at the table. And Jensen clearly sees its potential! Looking at the stage today, I saw some of Germany’s biggest technology and industrial players: Deutsche Telekom, Siemens, Bosch, Agile Robots, NEURA Robotics, and our automotive industry. We have decades of industrial knowledge. We have the data. We understand manufacturing, engineering, automation and complex physical systems. And that’s exactly what we need to take AI to the next level! NVIDIA has committed to invest nearly $10 billion across Europe’s tech ecosystem and is working with more than 12,000 partners here. That’s a pretty strong signal! And there’s something else I always love about Jensen’s keynotes: He makes incredibly complex technology understandable. Today, he broke down the AI economy again into five layers: 1. Energy 2. Compute 3. AI Factories 4. AI Models 5. Applications He also explained what it actually takes to build autonomous AI agents: a model that reasons and plans, a harness that coordinates its actions, a secure sandbox that controls what it can access, and the knowledge and tools it needs to get things done. Because the more autonomous AI becomes, the more important security becomes! Then there was Vera Rubin, NVIDIA’s next-generation AI computing platform, connecting 72 GPUs through NVLink into one massive, liquid-cooled system. And of course, PHYSICAL AI! One of my favorite moments was actually at the very end, when Jensen brought out the tiny Hugging Face robots and started explaining to them what he had just presented on stage. It was such a fun way to show something incredibly important: Robots don’t just learn in the real world anymore. They can be trained in simulated environments using synthetic data, combined with real-world data and feedback, before transferring those capabilities into physical machines. And that brings me right back to Europe. Because when AI moves from our screens into factories, machines and robots, our industrial expertise becomes a huge advantage. I’m really proud to be working with Nebius, one of NVIDIA’s major neocloud partners, and to have been part of this conversation. And a little women power at the table with Deirdre made it even better!! If you’re still at NVIDIA GTC Berlin tonight, you might find me at Nebius House! Later I’ll be at the creator evening & the Deloitte SOHO house party. Looking forward to a great evening here in Berlin and more exciting collaborations with the Nebius team! 💚 How did YOU like Jensen’s keynote today? What was your biggest takeaway?

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The frontier of AI isn't a better model. It's a better business. WRITER puts on-brand agents to work across your buyer journey, acting on real-time signals to grow revenue faster.

This is what Bengaluru’s physical AI scene looks like in one room. Last Saturday, 30+ researchers and engineers joined us to work through one question: why does egocentric hand tracking break in the wild? They challenged each other’s assumptions, disagreed openly and took the problem further together than any one of them could have alone. We’re building more rooms like this, where top minds in physical AI share their work, debate open problems and build together. If you want to be in the next one, join the waitlist. Link in the comments 👇 Ishank Gupta Manish Agarwal Sandeep Reddy Sabbella, PhD Pushpak Agrawal

Dove vengono elaborati i dati che il vostro team inserisce negli strumenti di AI? Su quali server, in quale paese? Il check-up riservato misura l'esposizione su quattro fattori: dati, governance, fornitori, continuità. In due minuti ricevete l'indice da 0 a 100 e le sei domande da fare al vostro fornitore di AI prima del prossimo rinnovo. Gratuito e senza impegno.

Quanti forfettari sforeranno la soglia quest’anno? Oggi serve una serata su Excel. Fai il conto in 30 secondi: scegli il tuo ruolo, inserisci persone e ore, e scarica gratis il calcolo dettagliato in PDF, con il confronto oggi / con l’Assistente Studio di Brainyware. Chiedi in italiano al gestionale e ricevi numeri e grafici, con la query sempre a vista. In sola lettura: il database non esce dallo studio.

We're giving engineering teams at enterprises $20,000 to train and deploy their own model on Arena. One grant per company, with our experts on hand at no extra cost, so the first experiments don't have to wait for budget approval. If your ML team has a task it wants a specialised model for, apply here and we'll get back to you within a week: https://lnkd.in/eMDCYpfV

+5.4 AP on EO. +2.94 AP on thermal. A leading German drone manufacturer added 30,000 AI Verse synthetic images to its own training data. Its vehicle detector improved on both cameras. The EO gain beat the +3 mAP benchmark from our last project with them, on a camera we had never built for. Small, far-away targets gained the most: +7.97 AP and +11.27 recall. Read more in our article, link in the comments ⬇️

If you lead AI at a bank or an insurer and you're planning to go agent-first, the model underneath it deserves a close look. Here is why: Runaway agents in a business can do great harm to a company's fiscal position whilst using a frontier model. With a frontier API, cost scales with usage, so as your usage grows, your token cost grows in a linear fashion, and you're always at the mercy of the provider if they change their token price. Open-weight, fine-tuned models on a company's own infrastructure allow them to really take advantage of an agent-first implementation. The initial cost is a little bit higher, but after that you pay for the hardware you run, so the more your agents do, the cheaper each unit gets. That only pays off if your agents are doing work that matters, which is why I would start where the data is richest. For banks and insurers, that's the repetitive work a few people do by hand, where you already hold years of previous outcomes. - Settling claims, where you know the value claimed, the correct value and what the policy covers. - Customer-service escalations. - A coding agent trained on your own codebase, so no third-party tool touches where your secrets live. Pick one of those, let the agent help the people doing it first, and over time let it do the work while they oversee it. This is exactly what we help enterprises do at AgileRL: fine-tune open models on their task and run them on their own infrastructure, so the cost of going agent-first stays predictable. Happy to chat if you're working through this.

Tracing shows what went wrong. Opik turns those failures into pass/fail test suites and reruns them on every prompt or tool change.