🤖 Daily Inference
Happy Valentine's Day! While the rest of the world is focused on hearts and flowers, the AI world just had its own blockbuster romance - Anthropic closed a $30 billion funding round at a staggering $380 billion valuation. But that's just the appetizer. Google claims its Gemini 3 model has shattered humanity's hardest reasoning benchmark, OpenAI released a coding model that generates over 1,000 tokens per second, and AI freight tools are sending logistics stocks into freefall. Here's everything that matters in AI today.
🏢 Anthropic Secures $30B in Largest AI Funding Round
Anthropic, the AI safety-focused company behind Claude, just closed a Series G funding round raising $30 billion, valuing the company at $380 billion. This massive injection of capital represents one of the largest single funding rounds in AI history and positions Anthropic as a serious heavyweight in the race toward artificial general intelligence.
The timing is significant. Just yesterday, Anthropic announced it would donate $20 million to a US political group backing AI regulation, signaling the company's commitment to shaping the policy landscape as it scales. The funding will likely accelerate Anthropic's research into AI safety and alignment while enabling more compute-intensive training runs for future Claude models.
The $380 billion valuation puts Anthropic in rarefied company - rivaling established tech giants and far exceeding earlier estimates. With competitors like OpenAI and Google pushing boundaries on multiple fronts, this capital infusion ensures Anthropic can compete at the cutting edge of AI research while maintaining its emphasis on responsible development.
🚀 Google's Gemini 3 Achieves 84.6% on ARC-AGI-2 Benchmark
Google DeepMind dropped a bombshell yesterday: its new Gemini 3 Deep Think model scored 84.6% on the ARC-AGI-2 benchmark, a test specifically designed to measure artificial general intelligence capabilities. This is the closest any AI system has come to matching human-level performance on abstract reasoning tasks that can't be solved through pattern memorization.
The ARC-AGI (Abstraction and Reasoning Corpus) benchmark has been called "humanity's last exam" because it tests an AI's ability to handle novel problems requiring genuine reasoning rather than recycling training data patterns. The test presents visual puzzles that demand understanding of core concepts like object permanence, counting, and spatial relationships - the kind of reasoning humans naturally excel at but machines have historically struggled with. Gemini 3's 84.6% score dramatically narrows the gap, though it still falls short of the human baseline of approximately 95%.
What makes this achievement particularly noteworthy is the "Deep Think" aspect - the model appears to use extended reasoning chains similar to OpenAI's o1 approach, taking more time to "think" through problems before responding. This suggests Google has successfully implemented test-time compute scaling, where models become more capable by spending additional computational resources on inference. The implications for AI research are profound: we may be approaching systems that can genuinely reason about unfamiliar problems rather than simply matching patterns from training data.
⚡ OpenAI's GPT-5.3-Codex-Spark Generates 1000+ Tokens Per Second
Speaking of speed breakthroughs, OpenAI released a research preview of GPT-5.3-Codex-Spark, a specialized coding model that delivers over 1,000 tokens per second on Cerebras hardware - making it approximately 15 times faster than previous coding assistants. This isn't just an incremental improvement; it's a fundamental shift in how developers might interact with AI coding tools.
The breakthrough comes from the combination of model optimization and specialized hardware. Cerebras systems use wafer-scale integration, essentially building an entire AI chip from a single silicon wafer rather than connecting multiple smaller chips. This architecture dramatically reduces latency and increases throughput for large language model inference. For developers, this means near-instantaneous code generation and completion - fast enough that the AI could keep pace with human typing in real-time collaborative coding scenarios.
The practical implications are significant. At this speed, coding agents could handle more complex multi-file refactoring tasks, generate entire applications in seconds, or provide real-time suggestions as developers work. The model is currently in research preview, meaning OpenAI is testing it with select partners before a wider release. If you're building AI-powered tools and need to create websites quickly, check out 60sec.site, an AI website builder that can generate professional sites in under a minute. For more OpenAI developments, visit dailyinference.com for comprehensive coverage.
📉 Trucking and Logistics Stocks Plunge After AI Freight Tool Launch
While AI companies celebrate funding rounds and benchmarks, traditional industries are experiencing the sharp end of AI disruption. Shares in trucking and logistics firms plunged yesterday following the launch of a new AI-powered freight tool from startup Semicab, which uses the Algorhythm platform to automate freight brokering and route optimization. Investors are betting that AI will eliminate significant portions of the human labor currently required in logistics coordination.
The AI system handles tasks traditionally performed by freight brokers - matching available trucks with cargo, negotiating rates, optimizing routes for fuel efficiency, and managing scheduling conflicts. What once required experienced logistics professionals making dozens of phone calls can now be accomplished by AI agents in seconds. The technology promises to reduce costs and improve efficiency, but it also threatens to displace thousands of workers in an industry already facing pressure from automation.
The market response was swift and severe. Major logistics firms saw share prices drop as investors reassessed the value of companies whose competitive advantage has been human expertise and relationships. This mirrors similar disruptions we've seen in property services and other white-collar sectors. The economic impact extends beyond stock prices - it raises fundamental questions about how quickly AI will reshape traditional business models and what happens to workers caught in the transition.
🛠️ Google DeepMind's Aletheia Moves from Math Competitions to Research
In another development from Google DeepMind, the company introduced Aletheia, an AI agent designed to transition from solving math competition problems to conducting fully autonomous professional research. This represents a significant evolution in AI capabilities - moving from constrained problem-solving to open-ended scientific discovery.
Aletheia builds on DeepMind's earlier work with systems like AlphaGeometry and AlphaProof, which achieved impressive results on International Mathematical Olympiad problems. But competition math, while challenging, operates within well-defined rules and has clear success criteria. Real research requires identifying interesting questions, designing experiments, interpreting ambiguous results, and connecting findings to broader scientific contexts. Aletheia attempts to handle this entire pipeline autonomously.
The system combines several AI techniques: language models for hypothesis generation and paper comprehension, specialized reasoning modules for mathematical proofs, and learning algorithms that improve through interaction with research environments. DeepMind envisions Aletheia eventually contributing original mathematical discoveries, potentially accelerating progress in fields where human researchers are currently bottlenecked by the sheer complexity of modern mathematics. If AI agents can autonomously advance human knowledge, we're entering genuinely unprecedented territory.
🏢 Spotify Says Top Developers Haven't Written Code Since December
In a striking example of how AI is transforming software development, Spotify revealed that its best developers haven't written a line of code since December 2025, thanks to AI coding assistants. Instead, these senior engineers now focus on system architecture, code review, and strategic technical decisions while AI handles the actual implementation.
According to Spotify's engineering leadership, the shift has been surprisingly smooth. Senior developers describe their new role as more like "conducting an orchestra" than playing individual instruments. They define requirements, review AI-generated code for correctness and efficiency, and make high-level architectural decisions. The AI tools - likely including systems like GitHub Copilot and custom internal assistants - handle the repetitive aspects of coding: boilerplate, standard patterns, and routine implementations.
This transformation raises important questions about the future of software development careers. If senior developers at major tech companies no longer need to write code directly, what skills will be most valuable for the next generation of programmers? Spotify's experience suggests the answer lies in understanding systems holistically, making good architectural decisions, and effectively collaborating with AI tools - skills that require deep expertise but look very different from traditional coding.
💬 What Do You Think?
With Anthropic raising $30 billion at a $380 billion valuation while traditional logistics and property services companies see their stocks crater, we're witnessing two very different sides of the AI economy. On one hand, investors are betting enormous sums on AI's potential. On the other, entire industries face displacement within months or years.
Here's my question for you: Do you think the economic benefits of AI will flow primarily to a few massive AI companies and their investors, or will the productivity gains eventually spread broadly enough to offset job displacement? I'm genuinely curious about your perspective - especially if you work in an industry facing AI disruption. Hit reply and let me know what you're seeing on the ground. I read every response!
That's all for today! If you found this valuable, forward it to a colleague who's trying to keep up with AI developments. And remember to visit dailyinference.com for daily AI news and analysis.
Until tomorrow,
The Daily Inference Team