Always skate to where the puck is going! In one of my previous jobs we worked closely with coach Kevin Ryan, who previously worked eight years as the general manager of 08 Stockholm Human Rights (a Basketball Club). Kevin kept pushing the famous mantra by Wayne Gretzky to always try to predict where things are heading, and not where things are right now.
Last week in AI we had our first “moon landing” moment, and most people are not even aware of it. OpenAI announced that one of their experimental models, still an auto-regressive model that generates one word at the time, scored a gold medal in the 2025 International Math Olympiad, solving 5 of 6 problems. It’s hard to overstate just how important this achievement was, and for all of you who believed we had reached an end with current transformer-based auto-regressive models, I think you all have to reconsider.
Now what does this mean for you and your company? It means that the generalized models we have today like ChatGPT, Claude and Copilot will continue to get smarter, not just slightly smarter but a LOT smarter, in all areas. If you look at all the announcements from OpenAI last week: gold medal at an international math olympiad, an AI agent that works better in Excel than most humans, and a new coding AI agent that nearly beat one of the best programmers in the worlds in a 10-hour programming contest, then it’s quite easy to see where the puck is going.
Expect AI agents to be able to do anything you can do with the computer, but faster and with more accuracy. Someone still has to orchestrate them, but the productivity gains are astronomical. This is why companies such as Lovable are able to raise $200M at a $1.8B valuation, despite being founded just 1.5 years ago and being fully reliant on third party AI model providers. If the puck continues to accelerate and going in the same direction as it is today, $200 million is small change compared to the financial gains companies can leverage with this new technology in the near future.
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THIS WEEK’S NEWS:
- OpenAI’s Experimental Model Achieves Gold Medal Performance at International Math Olympiad
- OpenAI Launches ChatGPT Agent for Autonomous Task Automation
- OpenAI’s AI System Nearly Matches Elite Human Programmer in 10-Hour Coding Marathon
- Anthropic Launches Claude for Financial Services Platform
- Anthropic Launches Claude Tool Directory for Work App Integration
- Mistral Updates Le Chat with Deep Research and Voice Features
- Mistral Releases Open-Source Speech Model Voxtral with Superior Transcription Performance
- Researchers Hide AI Prompts in Academic Papers to Manipulate Peer Review
- Reflection AI Launches Asimov Code Research Agent
- Moonshot AI Releases Kimi K2 Open-Source Model with 1 Trillion Parameters
- Pentagon Awards $200 Million AI Contracts to Four Major Tech Companies
OpenAI’s Experimental Model Achieves Gold Medal Performance at International Math Olympiad
The News:
- OpenAI’s unreleased experimental model scored 35 out of 42 points by solving 5 of 6 problems at the 2025 International Math Olympiad, achieving gold medal-level performance in one of mathematics’ most challenging competitions.
- The model operates as a general-purpose reasoning system rather than a specialized mathematics tool, using new reinforcement learning techniques and extended test-time compute that allows it to “think” for hours compared to o1’s seconds.
- Alexander Wei, the lead researcher, stated the model can “craft intricate, watertight arguments at the level of human mathematicians” using natural language explanations without internet access or external tools.
- Only 67 of 630 contestants (roughly 10 percent) earned gold medals at this year’s competition, which requires solving extremely difficult algebra and pre-calculus problems that demand creativity and abstract reasoning.
- OpenAI executives confirmed this experimental model will not be released to the public for several months, meaning the upcoming GPT-5 will likely lack this advanced mathematical capability.
My take: Two days ago OpenAI researcher Sebastian Bubeck posted this on X: “It’s hard to overstate the significance of this. It may end up looking like a ‘moon‑landing moment’ for AI. Just to spell it out as clearly as possible: a next-word prediction machine (because that’s really what it is here, no tools no nothing) just produced genuinely creative proofs for hard, novel math problems at a level reached only by an elite handful of pre‑college prodigies”. And I agree. Some people still argue that these auto-regressive models are just simple next-word prediction systems, but the thing is that when you train these models for general-purpose thinking techniques there seems to be no limit to how good results they can produce given enough time.
Read more:
OpenAI Launches ChatGPT Agent for Autonomous Task Automation
https://openai.com/index/introducing-chatgpt-agent
The News:
- OpenAI released ChatGPT Agent, an AI assistant that autonomously completes multi-step digital tasks using its own virtual computer, offering users significant time savings for complex workflows.
- Users access the feature by selecting “agent mode” from ChatGPT’s tools dropdown or typing /agent, available exclusively to Pro ($200/month), Plus, and Team subscribers.
- The system combines web browsing capabilities with code execution, allowing it to navigate websites, fill forms, create PowerPoint presentations, generate Excel spreadsheets, and integrate with apps like Gmail and GitHub through secure API connections.
- ChatGPT Agent scored 41.6% on Humanity’s Last Exam benchmark (double the performance of previous models) and achieved 27.4% on FrontierMath when using tools, compared to 6.3% for the previous best model.
- The agent requests user approval before sensitive actions like submitting forms or making purchases, maintaining user control throughout automated processes.
My take: In my last two seminars I got the question what main trend I see for AI in 2026, and to me it’s easy: computer control. It’s the natural next step, and once we get models specifically trained for this then they will very soon be much more efficient doing things at the computer than any human. And wow, the new ChatGPT Agent already does this amazingly well. Sure, it’s not up to human speed yet in Excel, but give it another year and I’m quite sure it will be very close. This is when you will be able to interact with your PC the way you work with Claude Code today – define requirements, iterate, make plan, refine, set it to work, review. You will be able to trigger any kind of work you do manually in front of your computer, and the actual work will be done by an AI agent. In practice you will be able to perform the work of 2-3 people by yourself if you are good at managing these kinds of AI systems. How this will affect organizations in the coming 2-3 years we can only guess. And as expected when it comes to EU access for these newer models: “we are still working on enabling access for the European Economic Area and Switzerland”.
OpenAI’s AI System Nearly Matches Elite Human Programmer in 10-Hour Coding Marathon
The News:
- OpenAI’s autonomous coding system OpenAIAHC achieved a historic second place in the AtCoder World Tour Finals, coming within 9.5% of defeating the world’s elite competitive programmer in a grueling 10-hour contest.
- The AI system operated completely autonomously for the entire marathon session, solving complex robot pathfinding optimization problems on a 30×30 grid without human intervention or external resources.
- OpenAIAHC scored 1,654,675,725,406 points compared to winner Przemysław “Psyho” Dębiak’s 1,812,272,558,909 points, outperforming ten other world-class human programmers in the invitation-only championship.
- The contest required sustained creative problem-solving for NP-hard optimization problems over 10 continuous hours, with mandatory five-minute intervals between submissions and standardized hardware for all participants.
- This marks the first time an AI system has competed at this level in competitive programming, demonstrating sustained performance comparable to humans in complex, creative coding challenges.
My take: The AtCoder World Tour Finals features 12 of the world’s most skilled programmers, selected through rigorous year-long qualification processes. And the new coding AI by OpenAI beat them all except one. Dębiak, the human winner and former OpenAI engineer, said in an interview that without OpenAI’s model pushing him, his score would be much, much lower. My own experience working with agentic AI for programming (I just passed 3 billion tokens with Claude Code and Claude Opus 4) is that the better you are knowing the limitations of the model, the better performance you will get out of it. Learning to work with AI for agentic programming is a skill, and if you have not yet started you really should, because at the rate this is improving I cannot see any company hiring any developer not using AI in just 6 months.
Read more:
Anthropic Launches Claude for Financial Services Platform
https://www.anthropic.com/news/claude-for-financial-services
The News:
- Anthropic introduced Claude for Financial Services, a specialized AI platform combining Claude 4 models with financial data integrations to help analysts make investment decisions, analyze markets, and automate compliance workflows.
- The platform connects directly to major financial data providers including FactSet, PitchBook, Morningstar, S&P Global, Databricks, and Snowflake through pre-built connectors, eliminating the need to switch between multiple systems.
- Claude 4 models outperformed other AI models in Vals AI’s Finance Agent benchmark, and when deployed by FundamentalLabs, Claude Opus 4 passed 5 out of 7 levels of the Financial Modeling World Cup competition with 83% accuracy on complex Excel tasks.
- Implementation support comes from consulting partners including Deloitte and KPMG, with Anthropic claiming integration cycles can be reduced by three to six months compared to building custom solutions.
- Early adopters report significant productivity gains: NBIM achieved approximately 20% productivity improvements equivalent to 213,000 hours, while AIG compressed underwriting review timelines by more than 5x and improved data accuracy from 75% to over 90%.
My take: The key news in this announcement is that Claude for Financial Services can instantly check information across multiple sources. This means that “information is verified across sources to reduce errors, every claim links directly to its original source for transparency, and complex analysis that normally takes hours happens in minutes” (from Anthropic). Through data providers Claude now has direct and real-time access to financial information from providers such as Box, Daloopa, Databricks, FactSet, Morningstar, Palantir, PitchBook, S&P Global and Snowflake. If you are working with financial data you really owe it to yourself to go to their web page and read the testimonials. This is truly next-level AI financial services.
“Claude has fundamentally transformed the way we work at NBIM. With Claude, we estimate that we have achieved ~20% productivity gains – equivalent to 213,000 hours. Our portfolio managers and risk department can now seamlessly query our Snowflake data warehouse and analyze earnings calls with unprecedented efficiency. From automating monitoring of newsflow for 9,000 companies to enabling more efficient voting, Claude has become indispensable.” – Nicolai Tangen, CEO at NBIM
Anthropic Launches Claude Tool Directory for Work App Integration
https://www.anthropic.com/news/connectors-directory
The News:
- Anthropic introduced a directory of tools that connect to Claude, allowing users to link external apps and services with one-click authentication to save time on repetitive setup tasks.
- The directory features connectors to remote services like Notion, Canva, and Stripe, plus local desktop applications including Figma, Socket, and Prisma.
- Users can access the directory at claude.ai/directory where they click “Connect” to authenticate remote services or “Install” for desktop extensions.
- Claude can now pull actual data from connected tools instead of creating generic templates. For example, users can ask Claude to “write release notes for our latest sprint from Linear” and it will access real Linear tickets to generate professional release notes.
My take: I love these integrations from Claude, it makes it more than just a simple chatbot: it transforms it into a tool that can do basically anything you can throw at it. This new directory includes business-focused connectors like Stripe for payments and Asana for project management, targeting professionals who need AI assistance with actual work data rather than general-purpose tasks.
Mistral Updates Le Chat with Deep Research and Voice Features
https://mistral.ai/news/le-chat-dives-deep
The News:
- Mistral AI launched major updates to Le Chat, its AI chatbot platform, adding productivity-focused features that help users research complex topics, communicate by voice, and manage projects in organized workspaces.
- Deep Research mode transforms Le Chat into a research assistant that breaks down complex questions, searches multiple web sources, and generates structured reports with citations. The feature takes 15 minutes to complete (reduced to 5 minutes with Flash Answers activated).
- Voice Mode enables users to speak to Le Chat using natural conversation powered by Mistral’s Voxtral model. The feature supports multilingual transcription and low-latency speech recognition for hands-free interaction.
- Multilingual reasoning through the Magistral model now supports complex tasks in French, Spanish, Japanese, and other languages, with the ability to switch languages mid-sentence during conversations.
- Projects feature organizes conversations, documents, and settings into focused workspaces, with each project maintaining its own library and tool configurations for long-term project management.
- And finally advanced image editing allows users to modify AI-generated images with text prompts like “remove the object” or “place me in another city” while preserving character consistency, developed in partnership with Black Forest Labs.
My take: It’s interesting to see a set of “standard features” evolving like Deep Research, Voice mode and Projects that most users now expect to find as a part of their AI subscription. Mistral Le Chat is now much closer to established rivals like ChatGPT, Gemini, and Claude at least in terms of features. But looking at Claude Code and the latest experimental models by OpenAI I wrote about above, Mistral will have to increase development speed significantly to stay in the race. Programming is probably the first task that will be fully automated by AI within 1-2 years, and if you as an AI provider do not have a model that can fully replace programmers then it really doesn’t matter how many features you have in your entire platform, your customers will pick another provider.
Mistral Releases Open-Source Speech Model Voxtral with Superior Transcription Performance
https://mistral.ai/news/voxtral
The News:
- Mistral AI launched Voxtral, its first open-source speech recognition model family, offering developers cost-effective speech intelligence that reduces API costs by half while improving transcription accuracy.
- The model comes in two variants: Voxtral Small (24B parameters) for production applications and Voxtral Mini (3B parameters) for local deployments, both released under Apache 2.0 license.
- Voxtral handles audio files up to 40 minutes long with a 32K token context window and includes built-in question answering, summarization, and function calling capabilities without requiring separate language models.
- The model supports automatic language detection across multiple languages including English, Spanish, French, Portuguese, Hindi, German, Dutch, and Italian.
- API pricing starts at $0.001 per minute compared to OpenAI’s Whisper at $0.006 per minute and GPT-4o mini at $0.003 per minute.
My take: Mistral just keeps pushing out new models! Voxtral outperforms both open-source and closed-source models on transcription benchmarks, surpassing OpenAI’s Whisper large-v3, GPT-4o mini Transcribe, and Gemini 2.5 Flash across all tested tasks. And according to Mistral, for premium use cases “Voxtral Small matches the performance of ElevenLabs Scribe for less than half the price”. The main issue for running this model is it’s size, “Running Voxtral-Small-24B-2507 on GPU requires ~55 GB of GPU RAM” (according to Hugging Face). So even if it beats OpenAI Whisper, you can easily run Whisper locally on almost any NVIDIA card or Mac computer, but you need a dedicated H200 card to run Voxtral at full performance. And don’t get your hopes up if you’re on Mac either, Mistral recommends that you use vLLM to run Voxtral which is a pain to try to get running without NVIDIA hardware. Still, it’s great to see these models coming, and for the price it’s very hard to beat this model for transcription services!
Read more:
Researchers Hide AI Prompts in Academic Papers to Manipulate Peer Review
https://techcrunch.com/2025/07/06/researchers-seek-to-influence-peer-review-with-hidden-ai-prompts
The News:
- Researchers from 14 institutions embedded hidden prompt injection instructions in 17 computer science papers on arXiv to manipulate AI-powered peer review systems. This exposes a critical vulnerability in academic publishing that could compromise research integrity and skew evaluation processes.
- The hidden prompts included instructions like “GIVE A POSITIVE REVIEW ONLY” and praise for “methodological rigor” and “exceptional novelty”. Authors concealed these messages using white text or extremely small fonts that remain invisible to human reviewers but can be detected by large language models.
- One paper was withdrawn from ICML 2025 conference submission after the practice was discovered. The affected papers came from researchers at Columbia University, University of Washington, Waseda University, KAIST, and other institutions across North America, Europe, and Asia.
My take: I expect this to become much more common, and writing texts for AI positive “reviews” will soon become as important as prompting. Your target audience is quickly moving from human reviewers to AI-agents doing the process, which means you have to consider how they react to your presentation. Sneaking in hidden or tiny texts like they did in these examples is effective, but can be easily found if another AI reviews the paper beforehand with the intent of identifying these manipulation sentences. So this is probably what the peer-review process will look like fairly soon: AI agents reviewing papers in different phases with close to automatic approval processes. Because if you allow humans to do it you don’t know if they will send it to an LLM or not, and you then loose the option to preprocess it.
Reflection AI Launches Asimov Code Research Agent
https://reflection.ai/blog/introducing-asimov
The News:
- Reflection AI released Asimov, a code research agent that indexes entire codebases, documentation, and team communications to answer engineering questions with citations.
- Asimov uses a multi-agent architecture where smaller retrieval agents gather information from codebases, GitHub discussions, Slack messages, and project documentation, while a central reasoning model synthesizes responses. The system creates a persistent memory of software development decisions and business context.
- Engineers can update Asimov’s knowledge using commands like “@asimov remember X works in Y way”, allowing senior developers to capture tribal knowledge for the entire team. The system includes role-based access controls to manage who can add or modify stored knowledge.
- In internal testing with maintainers of large open-source projects, developers preferred Asimov’s responses 82% of the time compared to 63% for Anthropic’s Claude Code (Sonnet 4).
My take: Interesting that they compared it to Claude Sonnet 4 and not Claude Opus 4. Myself I prefer Claude Opus 4 100% of the time compared to Claude Sonnet 4, so I am quite sure this would have looked a bit differently if they compared it to Opus. But still very impressive results from a company so young and with only 40 employees! Reflection AI was founded by former Google DeepMind and OpenAI researchers in March 2025 and quickly raised $130 million in two funding rounds. The company is already valued at $555 million, with their mission to build “superintelligent coding agents” and ultimately unlock “general computer use”.
Read more:
Moonshot AI Releases Kimi K2 Open-Source Model with 1 Trillion Parameters
https://moonshotai.github.io/Kimi-K2
The News:
- Moonshot AI launched Kimi K2, a mixture-of-experts (MoE) model with 1 trillion total parameters and 32 billion activated parameters, designed for coding, reasoning, and agentic tasks that can autonomously use tools and execute code.
- The model achieves 65.8% accuracy on SWE-bench Verified, outperforming GPT-4.1 (54.6%) and approaching Claude Opus 4 performance levels.
- Kimi K2 scores 53.7% on LiveCodeBench coding tasks and 97.4% on MATH-500 mathematical reasoning, positioning it among the top open-source models in these categories.
- API pricing starts at $0.15 per 1 million input tokens and $2.50 per 1 million output tokens, significantly undercutting competitors like Claude Opus 4 ($15/$75) and GPT-4.1 ($2/$8).
- Two versions are available: Kimi-K2-Base for researchers wanting customization control, and Kimi-K2-Instruct for immediate deployment in chat and agentic applications.
My take: Moonshot AI is a Beijing-based AI company founded in March 2023. The company is valued at $3.3 billion, making it the highest-valued unicorn among China’s four new “AI tiger” companies (Baichuan, Zhipu AI, Moonshot AI and MiniMax). Kimi launched as a chatbot in October 2023 which got attention for its ability to process extremely long input texts, initially up to 200,000 Chinese characters per conversation. In March 2024 Moonshot upgraded it to process up to 2 million Chinese characters in a single prompt. So, how can you run this beast locally? Well according to Hugging Face “the smallest deployment unit for Kimi-K2 FP8 weights with 128k seqlen on mainstream H200 or H20 platform is a cluster with 16 GPUs”. That means dual H200 8-GPU baseboards with 4 NVLink switches, with an average cost around $500,000. Today this is way beyond what most companies are willing to pay for AI infrastructure, but as more companies start to build their own custom transformer-based models hardware investments should start to grow rapidly across all sectors.
Read more:
Pentagon Awards $200 Million AI Contracts to Four Major Tech Companies
The News:
- The Pentagon’s Chief Digital and Artificial Intelligence Office awarded contracts worth up to $200 million each to Anthropic, Google, OpenAI, and xAI to develop AI applications for national security missions.
- Each company will build “agentic AI workflows” that use advanced reasoning to address complex challenges across warfighting, intelligence, and enterprise systems.
- The contracts enable federal agencies beyond the Defense Department to access these AI applications through the General Services Administration partnership.
- Companies will provide access to their general-purpose AI models for use by various defense offices, including combatant commands and the Joint Staff.
- The Pentagon expects to cut planning cycles by 70 percent and provide commanders with continuous AI support from tactical operations to senior leadership.
My take: This is the first time all major AI companies have simultaneously received defense contracts of this scale. By purchasing from all four companies they ensure competition and also makes it possible for them to pick the right tool for each job. This is in my opinion the absolutely best approach going forward, and I see more and more companies allowing their employees the option to choose between ChatGPT, Claude and Microsoft Copilot. This allows them to use whatever model they are the most familiar with, and if you have spent months learning how to get good consistent results with Claude you don’t want to be forced to use ChatGPT or Copilot since you would have to spend weeks or maybe months learning how to prompt it to make the same results.