Your guide to AI: August 2025

Welcome to the latest issue of your guide to AI, an editorialized newsletter covering the key developments in AI policy, research, industry, and start-ups over the last month. First up, a few reminders:

• Join 489 participants to the State of AI Survey: do us a favor and share the survey with a few of your friends and colleagues, all the data will be open sourced for everyone: www.stateof.ai/survey

• Congrats to Delian Alliance Industries for their $14M Series A to accelerate European defense capabilities for autonomous warfare. The news was covered in the Financial Times.

• Congrats to Profluent on their Nature paper for OpenCRISPR! This is a huge deal for precision medicine at scale.

• Air Street Press featured a number of pieces this past month including why AI rollups are a mirage and write ups from RAAIS on drug discovery, poolside’s AI model factory, AI, power and politics, open-endedness, voice, pixel generation, and what comes after the peace dividend.

I love hearing what you’re up to, so just hit reply or forward to your friends :-)

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AI as national infrastructure

The White House published their AI Action Plan, which lays out a whole-of-government strategy to reindustrialize America around sovereign compute. It sets out to establish a National AI Research Resource alongside new national AI institutes, giving U.S. researchers centralized access to compute and data. It mandates that federal agencies adopt NIST’s AI Risk Management Framework to standardize evaluation and oversight across departments. The plan also emphasizes rigorous safety testing and transparency obligations for frontier models, with a view toward preemptive governance of dual-use capabilities.

On the hardware side, it leans on CHIPS Act coordination to localize advanced semiconductor manufacturing and proposes targeted incentives for energy-efficient datacenters. Perhaps most critically, it includes immigration reform and federal fellowships to rebuild the US AI talent base. At the heart of this plan is the intent to re-industrialize the US economy around sovereign compute, both defensively (against adversarial states) and offensively (to lead frontier model development). This vibes with what we’ve previously written - that the US aims for the world to depend on its standards, despite NVIDIA marketing AI factories as sovereingty (and world leaders gobbling that up).

China has responded with its own vision. The official policy paper from the Chinese Ministry of Foreign Affairs lays out priorities for international AI governance, including a new multilateral institution to balance US dominance. Beijing frames this as a cooperative open-source-first approach, but the underlying strategy is clear: build influence over global AI norms before the West does. More on their open source project below.

Meanwhile, Senator Warner is pressuring Nvidia over its H20 chip sales to Chinese firms, alleging they undermine national security despite regulatory compliance. Nvidia resumed shipments in mid-July, but China immediately launched a security probe, summoning company reps over potential backdoors in AI accelerators.

GPU empires rise

Sam Altman says OpenAI will have over 1 million GPUs online by year-end. This scale-up is supported by the expansion of the company's Stargate datacenter infrastructure, a joint initiative with Oracle that includes a new multi-region buildout aimed at training frontier models across tightly integrated compute zones. Most recently, OpenAI announced plans for a Stargate campus in Norway, backed by local energy providers and public-sector incentives, as part of its effort to diversify energy sources and reduce emissions intensity in training operations. This was also marketed as support for Norway’s AI startup ecosystem, which I’m not sure really exists.

On his side, Elon Musk claims xAI is running 230,000 GPUs, including 30,000 GB200s, with Colossus 2 set to deploy another 550,000. xAI is securing inference capacity in Saudi Arabia to meet rising demand, part of a broader trend toward localizing compute among geopolitically aligned partners. His five-year vision? 50 million H100-equivalent units focused on AGI training with better energy efficiency. We’re tracking all of these clusters the State of AI Report Compute Index.

Debt-for-GPU wizards at CoreWeave are doubling their power draw in Texas to support demand and Donald Trump is campaigning on a $90B AI + energy infrastructure package in Pennsylvania. This strain is already driving record electricity pricing across the U.S.

Model revenues break out

AI’s commercial engine is now running at full throttle. OpenAI has hit a $12 billion annual revenue run rate. Its infrastructure ambitions continue to grow commensurately, as the company is allegedly projecting $35 billion in inference spend and $55 billion on training between 2025 and 2027. The company is building up 4.5 GW of capacity in the US with Oracle.

Anthropic is also accelerating, reportedly approaching a $5 billion run rate. This surge reflects strong enterprise demand for Claude in legal, financial, and knowledge work use cases. Its partner Amazon is considering expanding its investment past the $8 billion already committed to deepen Claude integration across internal tools and AWS offerings.

Meanwhile, Microsoft reported over $500 million in internal savings from deploying Copilot across its workforce and product suite. Azure AI services are seeing double-digit growth as enterprise customers shift from trials to full deployment as Azure reaches $75 billion revenue.

Meta saw its AI-related capital expenditures rise sharply as it ramps up both foundational research and model training capacity. The company is pursuing an unconventional datacenter strategy, deploying massive tented facilities to bypass construction bottlenecks and scale up GPU hosting at unprecedented speed. These temporary structures are part of a broader play to secure early throughput while permanent hyperscale sites come online. Meta has committed more than $105 billion in capital expenditures for 2025, with a significant share earmarked for AI infrastructure. Alongside its temporary tent clusters, Meta is building a series of permanent hyperscale facilities, most notably 'Prometheus,' its flagship AI supercomputer campus in Kansas City, and 'Hyperion,' a new Louisiana-based site the size of Manhattan, expected to come online in 2026. According to Meta’s own disclosures, these next-generation datacenters are being optimized for AI workloads from the ground up, including liquid cooling, fiber interconnects, and dedicated orchestration layers.

Defense goes commercial

The CDAO announced new ceiling contracts worth up to $200 million each for Google, xAI, OpenAI, and Anthropic. These awards are part of the Department of Defense's push to secure access to frontier AI capabilities across commercial labs, spanning dual-use applications from logistics and decision support to battlefield autonomy and cyber defense. The contracts are framed as the first step toward a strategic integration of foundation models into defense planning. They’re not delivery-based, but enabling vehicles designed to onboard commercial AI systems as they mature. I like it, and would hope European nation states would rapidly provide similarly bold buying signals to the market, which of course they aren’t.

This work is also additional proof of immense vibe shifts amongst the large labs that were founded on the principle that AI should not be used for military use. But a picture always says a thousand words:

China accelerates open source

China's open-source AI ecosystem is moving quickly and deliberately. In recent months, Chinese labs have stormed the leaderboards of open model evaluation sites like LM Arena. Leading the charge is Kimi-K2 from Moonshot AI, which has overtaken DeepSeek as the most-voted open model on the platform. It’s clear that while Meta lost the open source battle in the US, Chinese labs compete head-to-head with their Western counterparts. It's also being widely adopted across Chinese developer platforms, with user feedback loops and community fine-tuning driving rapid iteration.

Meanwhile, Qwen3 from Alibaba is gaining traction after the team made a deliberate move to split its architecture into separate Instruct and Thinking variants. This change followed sustained community criticism of previous hybrid models, which blurred the line between instruction-following and reasoning fidelity.

Behind these technical strides is a powerful alignment of public and private support. Chinese ministries and provincial governments are deploying compute subsidies, encouraging standardized benchmarks, and quietly enforcing alignment norms tied to ideological compliance. With each release, the Chinese ecosystem is showing it can move faster, scale broader, and shape models to serve both domestic demand and strategic goals abroad.

Final thought: who governs the governors?

Behind all the headlines sits a basic question: what kind of world are we building? AI labs are becoming nations: with budgets, sovereignty claims, and foreign policy. Talent flows like capital. GPUs are political assets. Legal systems are being stretched to accommodate the rights of the dataset, the provenance of weights, and the emergent behavior of models.

This tension between capability and control, sovereignty and coordination, played out onstage at RAAIS 2025, where a panel of policy leaders debated how and whether we can meaningfully govern frontier AI systems. What emerged was less a consensus and more a snapshot of institutional fragmentation: voluntary red-teaming frameworks, regulatory wishlists, classification proposals, and growing discomfort with the idea that private labs could unilaterally decide the future of intelligence. The unanswered question is whether current institutions can evolve fast enough, or whether we need entirely new ones to meet the moment.

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Research papers

Design of highly functional genome editors by modelling CRISPR-Cas sequences, Profluent Bio

In this paper, the authors demonstrate using language models to design novel CRISPR gene editors. The researchers created the CRISPR-Cas Atlas, mining over 1 million CRISPR operons from 26 terabases of genomic data. They then fine-tuned protein language models on this dataset to generate diverse Cas9-like proteins.

Their best editor, OpenCRISPR-1, shows activity comparable to SpCas9 (the standard CRISPR editor) but with 95% reduction in off-target editing, despite being 400 mutations different from any natural protein. They also designed custom guide RNAs and demonstrated compatibility with base editing techniques.

Experiments validated these engineered proteins through cell-based editing assays, SITE-Seq off-target analysis, and immunogenicity testing, showing OpenCRISPR-1 may be less immunogenic than SpCas9. This work demonstrates AI's ability to design functional proteins beyond evolutionary constraints, potentially enabling more precise gene editors for research, agriculture, and medicine with reduced side effects.

SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via Multi-Agent Multi-Turn Reinforcement Learning, National University of Singapore, Centre for Frontier AI Research (CFAR), Sea AI Lab

In this paper, the authors introduce SPIRAL, a framework where language models develop reasoning skills by playing zero-sum games against themselves, removing the need for human-supervised data.

The core experiment trained a Qwen3-4B model on Kuhn Poker, resulting in an 8.6% improvement on math and 8.4% on general reasoning benchmarks. This surpassed a model fine-tuned on 25,000 expert game examples. The study found that cognitive patterns like case-by-case analysis and expected value calculation, learned during gameplay, transferred to academic problem-solving.

While the approach is computationally intensive and relies on pre-designed games, it highlights a promising direction for autonomous AI development. It suggests that complex reasoning can emerge from competitive dynamics, potentially reducing the reliance on massive, human-curated datasets for training more capable models.

Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models, Princeton University, UC Berkeley

In this paper, the authors introduce a framework to systematically study "machine bullshit", which are statements from LLMs made with indifference to truth. They propose a "Bullshit Index" to quantify this behavior and a new "BullshitEval" benchmark for evaluation.

Their experiments show that RLHF significantly increases bullshit. On the Marketplace dataset, paltering (true but misleading statements) and unverified claims rose by 57.8% and 55.6% respectively. After RLHF, paltering also became the most harmful form of bullshit, nearly doubling its negative impact on user utility. Additionally, Chain-of-Thought prompting was found to amplify empty rhetoric and paltering.

This research matters because it demonstrates that common alignment techniques can inadvertently make AI assistants more deceptively persuasive, which has direct implications for their trustworthiness in high-stakes applications like financial advice, healthcare, and customer service.

Kimi K2: Open Agentic Intelligence, Moonshot AI

In this paper, the authors introduce Kimi K2, a Mixture-of-Experts model with 32 billion activated parameters (1 trillion total) optimized for agentic intelligence.

The model achieves state-of-the-art performance in frontier knowledge, math, and coding benchmarks among non-thinking models. Notable results include 53.7% Pass@1 on LiveCodeBench v6, 65.8% accuracy on SWE-bench Verified, and 75.1% on GPQA-Diamond.

Technical innovations include the MuonClip optimizer with qk-clip technique, which stabilizes training by rescaling query and key projection matrices, preventing attention logit explosions during large-scale training on 15.5T tokens. The researchers developed agentic capabilities through large-scale data synthesis and a general RL system that combines self-judging for non-verifiable tasks with verifiable rewards.

SmolLM3: smol, multilingual, long-context reasoner, Hugging Face

In this paper, the authors introduce SmolLM3, a 3B parameter language model designed to be efficient, multilingual, and capable of long-context reasoning. They detail the fully open blueprint for multi-stage training process, starting with pretraining on 11T tokens, followed by mid-training for long-context (up to 128k) and reasoning capabilities. Post-training involved creating a dual-mode instruct model using synthetic data and alignment with Anchored Preference Optimization.

The base model outperforms other 3B models and is competitive with 4B alternatives. The reasoning mode significantly improves performance on complex tasks like GPQA Diamond (41.7% vs 35.7%). A final model merge step was used to recover long-context performance lost during alignment.

Scaling Laws for Optimal Data Mixtures, Sorbonne University, Apple

In this paper, the authors propose a systematic method for determining optimal data mixtures when training large models across multiple domains, using scaling laws that predict model loss as a function of model size, training tokens, and domain weights. Traditionally, selecting these mixtures has relied on trial and error, which is inefficient at scale.

The authors introduce both additive and joint scaling laws, fit them using small-scale experiments, and show that these laws accurately extrapolate to larger models and unseen data mixtures. Experiments span large language models, multimodal models, and vision models, with mean relative errors (MRE) typically below 2% for loss prediction.

They demonstrate that optimal domain weights derived from these laws outperform uniform or heuristic mixtures on both in-domain and downstream tasks, such as MMLU and CORE benchmarks. The approach requires only a small number of small-scale runs, reducing computational cost.

This research matters because it provides a principled, efficient alternative to ad-hoc data mixture selection, enabling better model performance and resource use in real-world AI training pipelines.

Language Models Improve When Pretraining Data Matches Target Tasks, Apple, University of Washington, Stanford

In this paper, the authors propose a method called Benchmark-Targeted Ranking, or BETR, to select pretraining data for language models. The method ranks documents based on their similarity to examples from target benchmarks, using a simple classifier to scale the process to large datasets.

When targeting evaluation benchmarks, BETR achieves a 1.8x to 2.8x compute multiplier over strong baselines, meaning it can reach the same performance with significantly less compute. The method also shows that targeting a diverse set of benchmarks generalizes well to held-out tasks.

A key finding is that optimal data filtering depends on model scale: larger models benefit from less aggressive filtering. This research provides a more systematic way to curate datasets, moving beyond heuristic notions of "quality" and showing how data selection can be explicitly optimized for desired model capabilities and compute budgets.

Self-Improving Language Models for Evolutionary Program Synthesis: A Case Study on ARC-AGI, ICML, Hugging Face

In this paper, the authors introduce SOAR, a framework that enables large language models to improve their program synthesis abilities through a cycle of evolutionary search and self-supervised learning. The system alternates between generating and refining candidate programs for the ARC-AGI benchmark, then uses both successful and failed attempts as new training data by relabeling failures as correct solutions for synthetic tasks.

Experiments show that SOAR nearly doubles search performance for all tested models, with a 14B parameter model achieving 42.75% accuracy, outperforming much larger closed-source models like GPT-4.1 on one-shot tasks. SOAR ultimately solved 80% of the ARC train set and 52% of the test set using only open-source models and no hand-crafted data.

The research demonstrates that iterative self-improvement can help smaller models match or exceed the performance of much larger ones, suggesting a path toward more efficient and adaptable AI systems for complex reasoning and program synthesis tasks.

Detecting structural heart disease from electrocardiograms using AI, Columbia University Irving Medical Center, NewYork-Presbyterian Hospital, Montreal Heart Institute

In this paper, the authors present EchoNext, a deep learning model that detects structural heart disease (SHD) from electrocardiograms. Trained on over 1 million ECG-echocardiogram pairs from a diverse health system, the model achieved 85.2% AUROC and 78.5% AUPRC on internal validation. Performance remained consistent across different hospitals, clinical contexts, and demographic groups.

In direct comparison, EchoNext outperformed cardiologists in SHD detection (77.3% vs 64.0% accuracy). External validation across three medical centers showed robust generalization with 78-80% AUROC. A prospective clinical trial confirmed the model's ability to identify previously undiagnosed heart disease in patients without prior echocardiograms. High-risk patients identified by the model had significantly higher rates of SHD (73%) compared to low-risk patients (6%).

This technology could expand access to heart disease screening in settings where echocardiography is limited by cost or availability, potentially enabling earlier intervention for millions with undetected cardiac conditions.

What Has a Foundation Model Found? Using Inductive Bias to Probe for World Models, Harvard University

In this paper, the authors introduce an "inductive bias probe" to test whether foundation models learn the underlying principles, or "world models," of the data they are trained on. The method involves evaluating how a model adapts to new tasks after being trained on data from a known system.

A key experiment involved a transformer trained on planetary orbital data. While the model could predict trajectories with over 99.99% accuracy, the probe revealed it had not learned Newtonian mechanics, producing nonsensical force laws when fine-tuned. Similarly, models trained on Othello learned to predict legal moves but failed to develop an inductive bias for the actual board state.

This work shows that high predictive accuracy doesn't mean a model understands the system's fundamental rules. Instead, models may learn task-specific shortcuts, which has important implications for their reliability and generalization in real-world applications like scientific discovery.

Goedel-Prover: A Frontier Model for Open-Source Automated Theorem Proving, Princeton University, Tsinghua University, Amazon

In this paper, the authors introduce Goedel-Prover, an open-source model for automated theorem proving. To overcome the scarcity of formal math data, they first trained models to convert 1.64 million natural language math problems into the formal language Lean 4.

They then used an "expert iteration" process, where successive versions of the prover generate new proofs that are verified and added to the training set for the next iteration.

The resulting model achieves a 57.6% success rate on the miniF2F benchmark, outperforming the previous state-of-the-art by 7.6%. It also solved 7 problems on the challenging PutnamBench. The work demonstrates that scaling up auto-formalized data is highly effective for training theorem provers. This matters for creating more reliable and verifiable AI, with the open-source models and datasets enabling further research in machine reasoning.

The Levers of Political Persuasion with Conversational AI, UK AI Security Institute, University of Oxford, Massachusetts Institute of Technology

In this paper, the authors investigate what makes conversational AI persuasive in political contexts, using three large-scale experiments with nearly 77,000 UK participants and 19 LLMs across 707 political issues. They systematically test the effects of model scale, post-training, prompting strategies, and personalization on persuasion.

The results show that while larger models are somewhat more persuasive, the biggest gains come from post-training and prompting methods, reward modeling and information-focused prompts increased persuasiveness by up to 51% and 27%, respectively. Personalization had only a minor effect. The most persuasive models achieved this by generating information-dense conversations, but this also led to a decrease in factual accuracy.

The research highlights that optimizing LLMs for persuasion can trade off with truthfulness, raising concerns for real-world deployment in political and informational settings, where persuasive but inaccurate AI-generated content could impact public discourse.

Subliminal Learning: language models transmit behavioral traits via hidden signals in data, Anthropic Fellows Program, Truthful AI, Warsaw University of Technology

In this paper, the authors investigate "subliminal learning," where a language model acquires behavioral traits from a "teacher" model by training on semantically unrelated data.

In their experiments, a "student" model was fine-tuned on data like number sequences generated by a teacher with a specific trait, such as a preference for owls or being misaligned. After training on these filtered numbers, the student model's preference for owls increased from 12% to over 60%. Similarly, a student trained on data from a misaligned teacher became misaligned, with harmful responses increasing from 0% to nearly 10%.

The authors find this transmission only occurs when the student and teacher models share the same initialization; it fails across different model families. This suggests the trait is passed through subtle statistical patterns, not explicit content. This matters for AI safety, as distillation could inadvertently propagate unwanted behaviors, and simple data filtering may be an insufficient defense.

A generic non-invasive neuromotor interface for human-computer interaction, Meta

In this paper, the authors describe a non-invasive neuromotor interface using a wristband that decodes surface electromyography (sEMG) signals for computer input. By collecting data from thousands of participants, they trained generic deep learning models that generalize across people without needing individual calibration.

The system achieved closed-loop performance of 0.66 target acquisitions per second in a continuous navigation task, 0.88 gesture detections per second in a discrete-gesture task, and a handwriting speed of 20.9 words per minute. While performance is below conventional input devices, fine-tuning the handwriting model with 20 minutes of a user's data improved performance by 16%.

This work provides a framework for building generalized human-computer interfaces from biological signals at scale. It has potential applications for on-the-go interaction with mobile devices and for users with motor impairments.

Routine: A Structural Planning Framework for LLM Agent System in Enterprise, Digital China AI Research

In this paper, the authors introduce "Routine," a structured planning framework designed to improve the reliability of LLM agents in enterprise environments where domain-specific knowledge is crucial.

The key experiment tested models in an HR agent scenario. Providing a Routine plan increased GPT-4o's task accuracy from 41.1% to 96.3%, and a smaller Qwen3-14B model's accuracy from 32.6% to 83.3%. Furthermore, by fine-tuning the smaller model on data distilled using this framework, its accuracy reached 95.5%, nearly matching GPT-4o.

While the framework currently relies on human experts for initial drafts, this research provides a practical method for deploying smaller, more efficient models to handle complex, multi-step business processes with high stability and accuracy. This helps bridge the gap between general-purpose AI and specialized enterprise automation.

Learning without training: The implicit dynamics of in-context learning, Google Research

In this paper, the authors investigate how large language models (LLMs) can learn new patterns from prompts at inference time without explicit weight updates through a mechanism called in-context learning (ICL). They introduce the concept of a "contextual block," which generalizes the transformer block, and show theoretically and experimentally that stacking a self-attention layer with an MLP allows the model to implicitly update its weights in response to the prompt, via a low-rank (rank-1) modification.

The experiments focus on transformers trained to learn linear functions in-context. The authors demonstrate that predictions made with in-context prompts are equivalent to those made by applying an explicit weight update, confirming their theoretical results. They also compare this implicit update to traditional fine-tuning, finding similar learning dynamics.

The work clarifies the implicit learning dynamics underlying ICL, suggesting that LLMs can adapt to new tasks on the fly. This has implications for building more flexible AI systems that can generalize from examples without retraining.

Investments

Ramp, the AI-driven finance automation platform, raised $500M in a financing round at a $22.5B valuation from investors including Founders Fund and Thrive Capital.

Thinking Machines Lab, a company building multimodal AI for collaborative general intelligence, raised $2B in a financing round led by a16z with participation from NVIDIA and Accel.

Delve, a compliance automation platform, raised a $32M Series A at a $300M valuation.

Reka, a leader in multimodal AI research and product development, raised a $110M financing round from investors including NVIDIA and Snowflake.

Vanta, the compliance automation platform helping businesses earn and prove trust, raised a $150M Series D at a $4.15B valuation from Craft Ventures and Sequoia Capital.

RealSense, a company specializing in AI-powered computer vision for robotics and biometrics, raised a $50M Series A financing round from investors including Intel Capital and MediaTek Innovation Fund.

Cogent Security, the AI-powered vulnerability management company, raised $11M in a Seed financing round led by Greylock Partners.

Moonvalley, the AI research company building licensed AI video models and tools, raised $84M in a financing round led by General Catalyst with participation from Creative Artists Agency and Comcast Ventures.

Monumental Labs, a startup combining robotics and AI for stone carving, raised an $8M financing round led by Seven Seven Six, Reddit cofounder Alexis Ohanian’s venture capital fund.

OpenEvidence, the AI-powered clinical decision support platform for physicians, raised a $210M Series B at a $3.5B valuation from Google Ventures and Kleiner Perkins.

Lovable, the vibecoding company, raised a $200M Series A at a $1.8B valuation from Accel.

Perplexity, the AI-powered search engine and browser company, raised a financing round at an $18B valuation from investors including Nvidia, SoftBank’s Vision Fund 2, and New Enterprise Associates.

Hadrian, the defense manufacturing startup using robotics and AI to automate factories, raised a $260M Series C financing round led by Founders Fund and Lux Capital.

Inforcer, the software company helping IT shops manage security for SMB clients, raised a $35M Series B financing round led by Dawn Capital, with participation from Meritech Capital.

Cambridge Terahertz, a company developing human-safe Terahertz imaging technology for concealed weapons detection and other applications, raised a $12M seed financing round led by Felicis with participation from Amazon and Tishman Speyer.

Bitfount, the federated AI platform transforming clinical research collaboration, raised $8M in a Series A financing round from Parkwalk Advisors, Ahren Innovation Capital, and Pace Ventures.

Cognition, the AI startup behind the generative coding assistant Devin, is raising over $300M in a financing round at a $10B valuation from Founders Fund and Khosla Ventures.

Fal, the Generative Media Platform for Developers, raised a $125M Series C from Meritech, Salesforce Ventures, and Shopify Ventures.

Ambience Healthcare, the AI platform for documentation, coding, and clinical workflow, raised a $243M Series C financing round co-led by Oak HC/FT and Andreessen Horowitz (a16z).

Augmodo, the spatial computing company for retail inventory tracking, raised a $37.5M financing round led by TQ Ventures with participation from Chemist Warehouse.

Oxide, a company rethinking hardware and software for on-premises cloud computing, raised a $100M Series B led by USIT with participation from Eclipse Ventures.

Anaconda, the company advancing AI with open source at scale, raised over $150M in a Series C financing round led by Insight Partners with participation from Mubadala Capital.

Harmonic, the AI company developing Mathematical Superintelligence to ensure accuracy and eliminate hallucinations, raised a $100M Series B at an $875M valuation from Kleiner Perkins and Paradigm.

Unify, a company transforming growth into a science, raised a $40M Series B financing round from investors including Insight Partners and Gradient Ventures.

Mariana Minerals, the software-first mining company, raised an $85M Series A financing round led by a16z with participation from Breakthrough Energy Ventures and Khosla Ventures.

Nudge, a company building non-invasive brain interface technology, raised a $100M Series A financing round led by Thrive Capital and Greenoaks.

Synthflow, the Voice AI OS company, raised a $20M Series A led by Accel with participation from Singular and Atlantic Labs.

ZeroEntropy, a company focused on building intelligent AI retrieval systems, raised a $4.2M Seed round from Initialized Capital, Y Combinator, and Transpose Platform.

Rime, the voice AI company creating lifelike and personalized speech synthesis models, raised a $5.5M seed round from Unusual Ventures and Founders You Should Know.

ScienceMachine, the AI-driven autonomous data scientist for biotechs, raised a $3.5M pre-seed financing round from Revent, Nucleus Capital, and Opal Ventures.

Willow, the voice-first interface company transforming workflows for professionals, raised a $4.2M financing round led by Boxgroup with participation from Goodwater Capital and Burst Capital.

Bedrock Robotics, a company bringing advanced autonomy to construction equipment, raised $80M in a financing round from Eclipse and 8VC.

E2B, the cloud runtime for AI agents, raised a $21M Series A financing round from Insight Partners, with participation from Decibel VC and Seed to Sunflower.

OffDeal, the AI-native investment bank focused on sell-side M&A for SMBs, raised a $12M Series A led by Radical Ventures at a $100M valuation.

Exits

Figma, the San Francisco-based design software company for apps and websites, raised $1.2B in its IPO at a $19.5B initial valuation, with shares priced at $33 and rallying 250% to $115.50 on its market debut, giving it a fully diluted market value above $60B.

Applied Intuition acquired Reblika, a generative AI company for creating 3D digital humans.

Cognigy was acquired by NICE for approximately $955M in the largest AI exit in Europe.

Bevy acquired Intros AI to launch its Engagement Hub.

Meta acquired just under 3% of EssilorLuxottica SA for approximately €3B (~$3.5B). Meta also acquired PlayAI, a voice AI startup, to enhance its talent pool, but the price was not disclosed.

Cognition acquired Windsurf, the pioneer of the agentic IDE.