In our 2026 telecom AI survey: • 89% of respondents said open source models and software are important to their AI strategy. • 66% said their company is actively using AI, up from 49% last year. Read the report: https://t.co/qxRZ6dxG1O https://t.co/ak66W8E4Xs
Telcos need AI agents that understand their networks. We provide the full stack to turn open models into specialized agents while keeping operators in control. @ATT, @SoftBank and @Indosat are adopting open models for use cases like autonomous network operations and adapting https://t.co/1fxnyZaVA
Grok 4.7 is excellent at dealing with large code repositories 引用 @XFreeze:Grok 4.7 takes the TOP 3 spots on VulcanBench Frontier v4, outperforming Fable 5.1, Opus 5.5, GPT-6 Astra, GPT-6.1 Sol and other leading frontier models • Grok 4.7 xHigh - 93.15 • Grok 4.7 High - 92.71 • Grok 4.7 Medium - 92
Grok @Bot can be your personal chief financial officer! 引用 @Teslaconomics:I literally hooked up ALL my finance accounts to Grok Bot. I really mean all of them: 1/ Chase 2/ Charles Schwab 3/ E*TRADE 4/ Fidelity 5/ Robinhood 6/ Wealthfront 7/ Apple Card 8/ 𝕏 Money FYI, this is a total of 30 acco
We’re expanding our Cyber Verification Program to give security professionals broader access to our most capable models. Through this program, verified security professionals can access Claude Mythos 5.1, Opus 5.5, and Sonnet 5.5 with safeguards designed for defensive work.
IoT helps people learn. Industrial AI helps systems learn. Physical AI helps the process learn. Azure brings the loop together, and Microsoft has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Global Industrial AIoT Platforms. https://t.co/1e0QHyd5F7 https://t.co/NtqjgDszA0
Kinda interesting that everyone focused on the rolling 12W changes in open vs. closed and when it got most pronounced everyone was freaking out. last two weeks with really good model releases (Opus 5.5, Sol 6.1) closed actually TOOK back share on volume. It's 52% open - 48% https://t.co/cDMNZtwUcd
Wages are at all-time lows, and profits at historic highs, as a percent of GDP. The backlash against A.I. is a backlash against unchecked inequality. My @nytopinion Chart: https://t.co/idu3fyDrgx
I had no idea how brave everyone would get just a few years later. 引用 @ByrneHobart:Who will be brave enough to post the first GPT-3 edition of their blog/column/newsletter? Coolest version of this would be a piece in a prestige publication about how AI will never replace writers.
Mistral 4 is pretty good at lawyer bench and cyber security bench, but give it's European heritage, one can only assume it's also good at regulation bench 引用 @MistralAI:Meet Mistral Large 4, aka Le Chonk. • 1T parameters, natively multimodal. 49B active. It is the best open weights model from US
People talk a lot about how the models are extraordinary in verifiable domains (math, coding etc), while not getting much better at things like writing. But is that actually true? Don’t AI-generated stories keep winning prizes and publication in prestigious outlets? 引用 @generativist:the models are
This is from the SA Tokenomics model which is the only granular split I've seen but again this is the 30-40% inference I cite in terms of monetized compute one could make the argument post-training/RL is inference too but if still used for training/internal purposes, I think it 引用 @socialcapital:~
Frontier model subscriptions are heavily subsidised. I made a tool to see how much my API cost would have been. It cost me $35 (and counting) to build this tool. I'm on the $100/mo business premium plan. So far I've spent $4853 in code, not counting chats. It's been three https://t.co/XNldSvjdIN
Don’t these charts suggest that the monthly subs are still just heavily subsidized by the companies compared to enterprise APIs and that’s unsustainable 引用 @kimmonismus:Holy sh*t, Anthropic’s subscriptions offer so much more value, even compared to the new and efficient GPT-6.1 Sol. It’s not even c
300 sensors per car. 24 races. Zero margin for error. AWS & @f1 built an agentic AI solution that resolves critical issues up to 86% faster, so engineers focus on the future, not the logs. https://t.co/KNvREXn1wH
The model behind this result is now on @huggingface 🤗 Nemotron-Labs-3-Competitive-Coding is a competitive-programming specialist model based on Nemotron-3-Ultra. https://t.co/xEegK23pQ0
Again if you start from the premise that having the most advanced AI is a matter of national security — clearly a view that’s held by many in DC and SF — then the right policy to tame inflation is not to raise rates, but to raise taxes on everything except datacenters. 引用 @colbyLsmith:NEW w/ @benca
Apple and a Hacker's Future I was happy for years in Apple's walled garden; with AI, however, their protections feel like limitations. https://t.co/aoNmGjTjp0
People sharing this as bullish are not reading the following lines.. They're very much in my camp of the mid-teens being a reasonable number this year mid-teens to high-teens next year. Need to be very optimistic to be in that 20-30GW/yr camp. Some semis guys are in 30GW+/yr. BTM
$AAOI is... finally... done... With their $600m ATM program, turns out it's ~$105.36 average. I do think AOI outperforms $LITE, as long as they stop filing these ATM programs. Just due to their sheer magnitude of ramp with $471m/month transceiver revenue + 400K/month ELSFP https://t.co/uq4WwGAe
Per reports, both Microsoft and Meta with significant cuts. Given Anthropic’s revenue concentration and contribution from top 2 customers (of which many people assume Meta was), this could be a meaningful drag on the trajectory of growth. Token cost optimization era has began in 引用 @amir:Microsoft
The difference in what Anthropic and OpenAI as well as many others give you in value for a subscription is insane. We tested Meta, Minimax, Kimi, Grok, and Cursor subscriptions 引用 @SemiAnalysis_:It doesn't make sense to say a subscription plan is worth $ X in isolation. The way to think about subs
OAI and Ant are also at ~30-40% inference with the rest allocated for training It’s one of the most important assumptions in all of compute/tokenomics because it dictates how much compute is monetized and the monetization rate As these guys all scale from 1-2 gigs to 5 GW and 引用 @jukan05:Appare
I get the call out on aggressive valuations for the labs (which should be litigated in public and we’ll see how the IPOs do) but comparing multiples or against historical revenue without accounting for growth (amongst the fastest we’ve ever seen in capitalism) and the rapid 引用 @craigweiss:if meta w