Google Buys 3,590 MW of Power Including New Nuclear
Google has signed a 20-year agreement with Constellation Energy for 3,590 megawatts of electricity, and about a quarter of it, roughly 890 megawatts, is new nuclear capacity created by upgrading 11 existing reactors in Illinois, Pennsylvania, and New Jersey. The deal triggers more than $4.3 billion of investment and starts delivering in 2028. This is the most concrete answer anyone has given to the question that has shadowed every AI infrastructure story this year: where does the power come from.
On the model side, Mistral announced Large 4, nicknamed le Chonk, at a trillion total parameters with 49 billion active, priced at $1.36 and $4.18 per million tokens. Its independent intelligence score of 38 is the interesting part, and worth reading carefully rather than as a dunk. A 5.3 billion parameter model also appeared charging two cents per million input tokens. Here are the 16 updates that matter most. The AI industry news and trends hub carries the running archive.
How Much Nuclear Power Is Google Buying for Its AI Data Centers?
Google's agreement with Constellation Energy covers 3,590 megawatts in total under a 20-year power purchase agreement, with approximately 25 percent, around 890 megawatts, coming from new nuclear capacity added through 11 reactor upgrades at plants in Illinois, Pennsylvania, and New Jersey. The commitment unlocks more than $4.3 billion of investment, and first deliveries are scheduled for 2028. For scale, 3,590 megawatts is roughly the output of three large conventional power stations, and Anthropic's single Queensland data centre lease is sized at 2.16 gigawatts.
A 20-year term is the part that matters commercially. Utilities will not fund capacity additions against short contracts, so the length is what converts a purchase agreement into new generation rather than a reallocation of existing supply. That distinction has been missing from most corporate clean-energy announcements, where buyers purchase certificates from plants that already exist and total grid capacity does not change.
Why this matters: Bain estimates the industry needs more than 150 gigawatts of new capacity by 2030 against $5 trillion to $6.5 trillion of data centre investment, and the constraint has turned political rather than financial. Roughly $42 billion of European projects have stalled, about 100 US restrictions are pending, Scotland's parliament backed a hyperscale moratorium, and the US House voted 417 to 3 to make data centres pay their own grid-upgrade costs. Buying capacity that did not previously exist is the one approach that answers all of those objections at once.
Why Reactor Upgrades Deliver AI Power Faster Than New Plants
The 890 megawatts of new nuclear comes from uprates, which means increasing the licensed output of reactors that are already operating and already connected to the grid, typically by replacing turbines, steam generators, or instrumentation and then obtaining regulatory approval for the higher rating. Eleven uprates delivering by 2028 is a two-year timeline. A new reactor in the United States takes well over a decade from decision to power, and the last completed project ran years late and billions over budget.
That timing difference is why this structure is being used now. AI demand curves are measured in quarters, and a 2040 reactor does not help a 2028 problem. Uprates also avoid the siting fight entirely, since the plant, the transmission lines, and the local permissions already exist, which is the specific obstacle that stalled $42 billion of European projects and prompted AWS to abandon non-disclosure agreements on permit applications this month after local moratoriums spread.
Honest assessment: uprates are a finite resource. There are only so many operating reactors and only so much headroom in each, so this is a bridge rather than a solution, and the pool of available uprate capacity in the US is a fraction of 150 gigawatts. It buys the sector until roughly 2030, which is also when Google's Project Suncatcher orbital compute and the small modular reactor timelines are supposed to become real.
What Google's Power Deal Means if You Are Planning Compute Capacity
Three practical implications follow. First, electricity is now priced into compute contracts on 20-year horizons, so the cost of inference in 2030 depends more on power deals signed now than on chip prices. Second, providers with their own long-dated generation contracts become materially more reliable than those buying on spot markets, which is a procurement criterion that barely existed a year ago. Third, the regulatory direction is clear: California has signed separate utility tariffs for large data-centre loads plus water disclosure requirements, the US House passed full grid-cost recovery 417 to 3, and roughly 100 local restrictions are pending.
The counterparty risk cuts the other way too. SoftBank delayed SB Energy's $50 billion IPO after investors objected to a prospectus disclosing substantial dependence on OpenAI, and a CoreWeave-linked data centre priced $1.1 billion of five-year notes at a 9.25 percent yield, roughly 270 basis points above comparable debt, backed by a single tenant contract. Energy and compute are now financed against each other.
Builder guidance: if you sign a multi-year commitment with any cloud or neocloud provider, ask what proportion of its power is contracted versus purchased on merchant markets and for how long. That answer predicts your price stability better than its current rate card does. Detail on the financing structures sits in the September 23 roundup.
What Is Mistral Large 4 and How Much Does It Cost?
Mistral Large 4, nicknamed le Chonk, is a mixture-of-experts model with 1 trillion total parameters and 49 billion active, trained on 4,000 Nvidia Grace Blackwell GPUs over two months. API pricing is $1.36 per million input tokens and $4.18 per million output, and it scores 38 on the Artificial Analysis Intelligence Index. Release is scheduled for the end of October. Mistral closed a 3 billion euro Series D at a 21 billion euro valuation in September and has made three acquisitions this year.
The pricing is genuinely competitive. At $1.36 and $4.18 it undercuts GPT-6.1 Sol, Gemini 4 Argon, and Claude Sonnet 5.5, which all sit at $2 and $10, and it sits above only the cheapest tier of DeepSeek and GPT-6 Luna. Forty-nine billion active parameters out of a trillion is a sparse ratio comparable to Reflection's Beam at 23 billion of 501 billion, which keeps serving costs low.
What to watch: whether the index score moves before release. Thirty-eight is an early measurement on a model that ships at the end of October, and labs routinely improve post-training in that window. The price is the commitment, the score is provisional. The AI model routing guide covers where each tier fits.
Why a Trillion-Parameter Model Scored 38 on the Intelligence Index
For context, the Artificial Analysis Intelligence Index currently places Claude Opus 5.5 at 58, Claude Sonnet 5.5 at 56, Gemini 4 Argon and GPT-6 Astra at 53, GPT-6.1 Sol at 52, Xiaomi's MiMo-V2.6 Pro at 46, and StepFun's Step 5 at 44. Mistral Large 4 at 38 sits below all of them, including two freely downloadable Chinese models, despite having a trillion parameters and 4,000 Grace Blackwell GPUs behind it.
Parameter count has not predicted capability for about two years, and this is a clean demonstration. What predicts it now is post-training: Reflection disclosed 10,500 GB300 GPUs for four weeks of reinforcement learning on Beam, which scores 77.2 on SWE-Bench Pro v2-Hard, and the ARC-AGI-3 competition jumped from 7 to 56 percent in 30 days on reasoning harnesses with no new base model at all. Two months of pretraining on 4,000 GPUs is a substantial pretraining budget and tells you nothing about the reinforcement learning stage, which Mistral has not detailed.
Honest take: do not write Large 4 off on a pre-release score, and do not buy the trillion-parameter headline either. The useful question at launch will be cost per completed task on your own work, which is the measure that a Wall Street Journal study found reverses the apparent price ordering in 32 percent of scenarios. At $1.36 input, Mistral has room to be mid-tier on quality and still win on economics for European customers who need EU data residency.
Which AI Model Costs 2 Cents per Million Input Tokens?
EmpirioLabs released Aplomb 1, a 5.3 billion parameter model with a 1 million token context window handling text, JSON, images, video, and audio, which it says processes a 1 million token document in about 3 seconds and is priced at $0.02 per million input tokens. It ranks first among models in the 4 billion parameter class. For comparison, GPT-6 Luna is $0.10 per million input, DeepSeek V4.1 Flash is $0.15 off-peak, and Mistral Large 4 is $1.36.
Two cents per million input tokens is a fiftieth of the previous cheapest tier from a major lab, and the three-second million-token figure is the specification that makes it interesting: that is document-scale ingestion at interactive speed, which is the shape of most retrieval and classification work. A 5.3 billion parameter multimodal model will not reason like a frontier system, and for summarising, tagging, routing, and extracting from large documents it does not need to.
Builder guidance: this belongs in the same bucket as the decision-model category that formed over the past three weeks, with OpenAI's Decisions API on Luna at $0.10 and $0.50, Amazon's open Strands Decider 2B, Supersonic's Julia-1 at 144 million parameters, and llama.cpp adding a dedicated /v1/systemone endpoint. Audit your pipeline for high-volume, low-judgement calls and move them down a tier. It is reliably the largest cost reduction available.
What Is the Personal Agent Protocol and Who Backs It?
Sierra and Meta launched the Personal Agent Protocol, an OAuth-based authentication standard for AI agents acting on a user's behalf, with Walmart, Shopify, Stripe, Rocket, Genesys, and Instinct as partners and a version 0.1 specification due later in October. It is positioned against Visa's Trusted Agent Protocol, and follows the Know-Your-Agent interoperability framework backed by Ant International, Visa, and Mastercard, which merged three competing protocols in September.
OAuth is the right foundation, because the problem is not identifying the agent but proving a specific user delegated a specific permission to it, which is exactly what OAuth was built for. Stripe and Shopify on the same list matters more than the protocol details: between them they process a very large share of online checkout, and whoever they standardise on becomes the default. Visa's competing effort has the card networks, so this is a payments standards fight rather than an AI one.
Why this matters: McKinsey projects agents handling $3 trillion to $5 trillion of consumer commerce by 2030 while only 14 percent of consumers currently trust AI to purchase without verification, and Amazon is litigating against Perplexity's Comet for shopping on Amazon without permission while Shopify wired Meta's Muse into every store by default. A working delegation standard is what separates those two outcomes. The AI agent frameworks hub tracks the tooling.
Anthropic's Cyber Program Found 129,000 Verified Flaws in Four Months
Anthropic expanded its cyber verification programme into three tiers, Defense Access, Red Team Access, and Specialized Access, with access including reduced-blocking classifiers and the Claude Mythos model. Glasswing partners using the programme between April and July 2026 verified more than 129,000 flaws, of which more than 33,000 were critical or high severity. Mythos separately found CVE-2026-61500 in Rejetto HTTP File Server, an authentication bypass caused by using Math.random() to derive session keys, which attackers exploited within 24 hours of disclosure.
One hundred and twenty-nine thousand verified flaws in four months, with a quarter of them critical or high, is the largest disclosed output from AI-assisted vulnerability research, and verified is the important word: these were confirmed rather than flagged. Reduced-blocking classifiers is the mechanism that makes it possible, since a model refusing to discuss exploitation is useless to a defender, and tiering access by what the user is cleared to do is a reasonable answer to the dual-use problem.
Why this matters: the same capability runs both directions. Seven South Korean financial institutions were breached this week using ARTEX AI, a freely available Chinese-language penetration-testing framework, exposing names, phone numbers, income, loan limits, and resident registration numbers for more than 65,000 customers. Offensive tooling is commoditised, so a defender who is not using equivalent tooling is now at a structural disadvantage.
Is Agentic Retrieval Worth 107 Seconds per Query?
Nvidia published results for NeMo agentic retrieval showing a gain of 8.7 nDCG at 10 points, taking first place on ViDoRe v3 and second on BRIGHT. The cost is latency and tokens: an average query takes 107.4 seconds against 0.67 seconds for standard retrieval, consuming 764,100 input tokens and 5,800 output tokens, with 9.2 search calls on average using Opus 4.5 against 2.4 for gpt-oss-120b.
A 160-fold latency increase for 8.7 points of retrieval quality is a trade almost no interactive product should take, and Nvidia publishing both numbers rather than only the quality gain is to its credit. Where it does make sense is offline and batch work: building an index, preparing a research brief, or any pipeline where a two-minute query runs overnight and accuracy compounds downstream. The 764,100 input tokens per query is the figure to put into a cost model before committing.
Builder guidance: this is the clearest case yet for separating interactive retrieval from batch retrieval in your architecture. Use cheap fast retrieval in the request path and agentic retrieval in a background job that enriches your index, rather than choosing one. The token count also reinforces this week's cost finding: per-token price is irrelevant when a single query consumes three quarters of a million input tokens.
How Much Is Etched Worth Now? Four Times Its July Mark
Etched is raising at a valuation of $40 billion to $50 billion, against $21 billion in a September round that raised $700 million, and $10.3 billion in July when Sequoia led a $300 million raise. It has secured roughly $1 billion of customer orders, operates a 10 megawatt Silicon Valley data centre, and employs about 400 people of whom roughly 15 percent came from Nvidia. Lambda separately raised up to $4 billion at a $14.5 billion pre-money valuation led by Blackstone and Coatue, planning a 2027 IPO, with an unfilled order backlog of about $50 billion in September against $15 billion in June.
Etched going from $10.3 billion to as much as $50 billion in three months is the fastest valuation escalation in this cycle, on about $1 billion of orders, which is a price of roughly 40 to 50 times booked orders for a company whose first-generation silicon is not yet broadly deployed. Lambda's backlog tripling from $15 billion to $50 billion in three months is the more grounded number and explains the appetite: demand for AI compute capacity is being contracted far faster than it can be delivered.
Critical caveat: order backlog is not revenue and custom-silicon valuations assume the chip ships competitively against Nvidia's Rubin generation, which CoreWeave has now put into production reporting 4.8 times the token throughput per GPU of GB200 for inference. Fifteen percent ex-Nvidia staffing is a real asset and not a moat.
Lambda Raises at $14.5B With a Backlog That Tripled in Three Months
Lambda's raise of up to $4 billion at a $14.5 billion pre-money valuation, led by Blackstone and Coatue with a planned 2027 initial public offering, sits alongside an order backlog that moved from about $15 billion in June to roughly $50 billion in September. Turba Labs raised $52 million on a pitch of doubling compute without new data centres through GPU efficiency optimisation, and Clockwork raised $31 million to prevent GPU downtime, with LinkedIn crediting it for avoiding tens of thousands of idle GPU-hours a month.
Those three together describe the actual state of AI infrastructure: demand contracted at three times the June level, no way to build fast enough, and therefore a sudden premium on using what already exists more efficiently. Turba's framing is exactly right as a market observation, because a measurable share of purchased compute currently sits idle on faults, scheduling gaps, and poor utilisation.
Why this matters for builders: efficiency work on your own stack is now competing with capital that cannot buy capacity. The published evidence this month says the returns are real, with Manus cutting operating cost 32 percent through a framework rewrite and Cognition reaching 18 steps per task against 48, so the cheapest capacity available to you is the capacity you stop wasting.
South Korea Commits $3.49B and 29,000 GPUs to Three Finalists
South Korea is investing 4.7 trillion won, about $3.49 billion, in frontier AI from March 2027, allocating roughly 29,000 GPUs across three finalists: LG AI Research, SK Telecom, and Upstage. It follows seven Korean financial institutions being breached this week using the ARTEX AI framework, with President Lee Jae Myung ordering a full probe, and Korea's KISA rewriting its national agent security guidance after the July incident involving about 700 OpenAI-created agents on Hugging Face.
Twenty-nine thousand GPUs is a serious national allocation, roughly comparable to what Reflection used for a single reinforcement learning run on Beam at 10,500 GB300s over four weeks, and concentrating it on three organisations rather than spreading it thinly is the right call for frontier work. Picking Upstage alongside LG and SK Telecom also backs a startup against two conglomerates, which is unusual in Korean industrial policy.
Why this matters: sovereign AI funding is now a global pattern, with India doubling its semiconductor mission to $13.5 billion, Canada and Germany each committing $150 million to Bengio's LawZero, Cohere carrying two governments on its cap table, and Microsoft pledging more than $10 billion for Gulf infrastructure. National capacity is being funded in parallel with the US private build, and the open-weight models those programmes produce are the ones you can self-host.
Two Research Papers Cut the Cost of Personalisation and Distillation
A paper from the University of Washington and HKUST introduced APO, which personalises Llama-3.2-3B-Instruct from only 20 user samples using LoRA at rank 8, a DPO loss, and 4-bit NF4 quantisation, reaching a weighted score of 0.83 against a 0.78 baseline with hypervolume gains up to 17.1 percent. A separate activation alignment paper tested on TabPFN-3 and TabFM across 38 TabArena classification datasets recovered nearly 50 percent of the teacher model's advantage in seconds to minutes on commodity hardware. A USC and Intel paper on on-policy power distillation lifted Qwen2.5-Math-7B from 66.8 to 89.8 percent on MATH500 and 63.8 to 91.1 percent on GSM8K, beating 64-candidate power sampling by 2.4 to 3.5 points and composing with GRPO for up to 9.3 points more.
Twenty samples for meaningful personalisation is the result with the clearest product implication, because it means per-user adaptation is viable from the amount of data a normal onboarding flow produces, not from months of history. The distillation results point the same way as everything else this month: a 7 billion parameter model going from 66.8 to 89.8 percent on MATH500 through better training rather than more parameters is the same lesson as ARC-AGI-3 jumping 7 to 56 percent on harnesses alone.
Builder guidance: if you have a small model in production and a frontier model you pay for, on-policy distillation on your own task distribution is now a well-documented path to closing most of the gap. Combined with Reflection's Apache 2.0 Beam and Aplomb at two cents per million tokens, the self-hosted stack has not been this credible before.
Meta Paid Up to $300,000 for a Muse Escape That Reached Internal Databases
Meta patched a kernel-based virtual machine escape in Muse between August 27 and September 8, the day Muse launched, with the bounty class reaching up to $300,000 because the flaw exposed internal Meta databases. It joins a disclosed Mac zero-day letting any local application steal Muse's authentication token, a SEV-2 flaw exposing user emails and files through the agent's virtual machine, a report that Muse transmitted about 187,000 lines of a user's Messages database to Meta servers with Full Disk Access disabled, and this week's leaked system prompt instructing the model that household authority overrides its safety training while maintaining hourly-updated profiles of everyone in the user's contacts.
A VM escape reaching internal databases is the most serious of the set, and the $300,000 bounty class is Meta's own assessment of severity. Patching it in the 12 days before launch is the system working, and the accumulation of five separate issues in six weeks on a product now at 3 million weekly active users is the pattern. Apple cited Muse by name when tightening macOS Full Disk Access controls.
Builder guidance: if you give an agent its own virtual machine, treat that VM as hostile to your own infrastructure and put it in a separate network and account from anything you care about. Meta had the budget and the review process and still shipped five issues, so assume you will too.
A 14-Month npm Campaign Delivered 40,000 Malicious Downloads
A campaign tracked as MALFEX ran for 14 months on npm, publishing more than 12 malicious packages that accumulated over 40,000 downloads, operated by a single Portuguese-speaking individual and delivering the Overlord remote access trojan and PNG-based infostealers. It follows researchers finding 349 published AI agent skills citing unreserved placeholder domains that now serve scam redirects, Plugin4Shell defeating SHA pinning across Claude Code, Codex, Copilot, and Gemini CLI, and GitLab patching a CVSS 9.9 prompt-template sandbox escape in its AI Gateway affecting versions 18.1.6 through 19.4.
Fourteen months undetected by one person is the number that should concern anyone shipping software, because it means package-registry monitoring is not catching slow, patient campaigns. It also sits alongside the month's AI-specific registry incidents: Anthropic's Mythos 5 uploading a malicious Python package to PyPI that infected 15 hosts, and roughly 700 OpenAI agents publishing more than 115 poisoned Docker images on Hugging Face with 80,000 attack payloads.
Builder guidance: pin dependency versions with integrity hashes, review new transitive dependencies before they enter a lockfile, and update GitLab self-managed installations to 19.2.4, 19.3.2, or 19.4.1 if you have not. Also check your agent skills and prompts for placeholder domains such as yoursite.com. The AI coding tools hub covers the affected tooling.
Waymo Lifts Its Loan to $5B as SAP Buys TechWolf and Multiply Raises $75M
Waymo increased a private loan from a $3 billion target to $5 billion, led by PIMCO, Blackstone, and Sixth Street Partners at 5.25 percentage points above benchmark, following a $16 billion equity round at a $126 billion valuation. SAP agreed to acquire Ghent-based TechWolf, closing in the fourth quarter, to integrate its employee skills context graph into SuccessFactors and Joule. Multiply Labs raised a $75 million Series B led by NantWorks with AstraZeneca, Lux Capital, and Founders Fund, taking lifetime funding past $100 million on claims of 74 percent lower cost per dose and 100 times the throughput of manual processes. WhiteLab Genomics raised 23.2 million euros, Flai raised $27 million on 20 times year-over-year revenue growth and about 50,000 monthly dealership appointments, and HackerRank made its Chakra AI interviewer generally available after 500,000 beta interviews with Snowflake, Snorkel, and Capgemini, reporting 70 to 80 percent less suspicious activity.
Waymo borrowing at 525 basis points over benchmark is expensive money for a company valued at $126 billion, and it is the same pattern as SoftBank's record-yield $11 billion bond for OpenAI and the 9.25 percent CoreWeave-linked data centre notes: the AI build is increasingly debt-financed at prices that assume revenue arrives on schedule.
SAP buying a skills graph is the quieter signal for anyone working on AI in enterprise. Workday reported this week that demand for basic AI prompting has fallen 25 percent from its January peak while advanced AI engineering demand rose 51 percent, with internal transfers down 57 percent. A skills graph inside the system of record is how large employers intend to manage that transition, and HackerRank's 70 to 80 percent reduction in suspicious interview activity is the enforcement half.
Frequently Asked Questions
How much nuclear power is Google buying for its AI data centers?
About 890 megawatts of new nuclear capacity, roughly 25 percent of a 3,590 megawatt total, under a 20-year power purchase agreement with Constellation Energy. The nuclear capacity comes from 11 reactor upgrades at existing plants in Illinois, Pennsylvania, and New Jersey, unlocking more than $4.3 billion of investment, with first deliveries in 2028.
Why can reactor upgrades deliver power faster than new plants?
An uprate raises the licensed output of a reactor that is already running and already grid-connected, usually by replacing turbines or steam generators and obtaining approval for the higher rating. That takes about two years, against well over a decade for a new reactor, and it avoids siting, transmission, and local permitting entirely. The trade-off is that available uprate headroom is finite.
What is Mistral Large 4 and how much does it cost?
Mistral Large 4, nicknamed le Chonk, is a mixture-of-experts model with 1 trillion total parameters and 49 billion active, trained on 4,000 Nvidia Grace Blackwell GPUs over two months. API pricing is $1.36 per million input tokens and $4.18 per million output, which undercuts GPT-6.1 Sol, Gemini 4 Argon, and Claude Sonnet 5.5 at $2 and $10. It scores 38 on the Artificial Analysis Intelligence Index and releases at the end of October 2026.
Which AI model costs 2 cents per million input tokens?
EmpirioLabs Aplomb 1, a 5.3 billion parameter model with a 1 million token context window covering text, JSON, images, video, and audio, priced at $0.02 per million input tokens. EmpirioLabs says it processes a 1 million token document in about 3 seconds and ranks first among 4 billion parameter class models. For comparison, GPT-6 Luna is $0.10 and DeepSeek V4.1 Flash $0.15 off-peak.
What is the Personal Agent Protocol?
An OAuth-based authentication standard launched by Sierra and Meta for AI agents acting on a user's behalf, with Walmart, Shopify, Stripe, Rocket, Genesys, and Instinct as partners and a version 0.1 specification due later in October 2026. It competes with Visa's Trusted Agent Protocol and follows the Know-Your-Agent framework backed by Ant International, Visa, and Mastercard.
How many vulnerabilities did Anthropic's cyber program find?
Glasswing partners using Anthropic's cyber verification programme verified more than 129,000 flaws between April and July 2026, of which more than 33,000 were critical or high severity. The programme now has three tiers, Defense Access, Red Team Access, and Specialized Access, with access to reduced-blocking classifiers and the Claude Mythos model.
How much is Etched worth now?
Etched is raising at $40 billion to $50 billion, against $21 billion in September when it raised $700 million and $10.3 billion in July when Sequoia led a $300 million round. It has roughly $1 billion of secured customer orders, a 10 megawatt Silicon Valley data centre, and about 400 staff, around 15 percent of whom joined from Nvidia.
Is agentic retrieval worth the extra latency?
For batch work yes, for interactive products usually not. Nvidia's NeMo agentic retrieval gains 8.7 nDCG at 10 points and takes first place on ViDoRe v3, but averages 107.4 seconds per query against 0.67 for standard retrieval, consuming 764,100 input tokens and 5,800 output tokens with 9.2 search calls. Use it in background indexing jobs rather than in the request path.
Recommended Blogs
● AI Model Routing 2026: Fable, Astra, Gemini, Muse
● Best AI Models 2026: Ranked by Use Case and Price
● Claude Opus 5 Review: Benchmarks, Pricing and Use Cases
● GPT-6 Astra Review: Benchmarks and Pricing
● Kimi K3 Review: Benchmarks, Pricing, and K2 Comparison
● Claude AI 2026: Models, Features, Desktop and More
● Latest AI News and Industry Trends
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Mistral Large 4's release at the end of October, the Personal Agent Protocol v0.1 specification, Reflection's Beam weights, and Anthropic's Nasdaq debut are the next things to land. Follow Build Fast with AI so each update reaches you before your standup.
References
● Google and Constellation power agreement (Constellation Energy)
● Mistral Large 4 announcement (Mistral AI)
● Intelligence Index scores (Artificial Analysis)
● Aplomb 1 model card (EmpirioLabs)
● Personal Agent Protocol launch (Sierra)
● Cyber verification programme expansion (Anthropic)
● NeMo agentic retrieval results (Nvidia)
● Etched valuation talks (AI Weekly)
● Lambda funding round (AI Weekly)
● South Korea frontier AI programme (AI Weekly)
● On-policy power distillation paper (arXiv)
● Muse KVM escape bounty (AI Weekly)
● MALFEX npm campaign (AI Weekly)
