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- AI Moves Into Robots and Gets Cheaper to Run
AI Moves Into Robots and Gets Cheaper to Run
PLUS: Perplexity turns Spaces into Projects, and Anthropic lines up a $15 billion data-center loan.
Wes Roth
July 31, 2026
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Today:
Gemini Robotics 2 Gives Humanoids Full-Body Control
Inkling-Small Shrinks Open AI to 12B Active Parameters
GPT-5.6 Luna Cuts API Prices by 80%
Projects Unify Research, Files, and Computer Tasks
$15 Billion Loan Talks Back a Texas AI Campus
AI Moves Into Robots and Gets Cheaper to Run
Gemini Robotics 2 coordinates entire humanoid bodies, Inkling-Small reduces the amount of active computing needed, and GPT-5.6 becomes cheaper to operate.
AI progress is spreading beyond larger general-purpose models.
The newest releases focus on physical movement, efficient open models, and lower operating costs. Together, they show the industry trying to make AI useful in more places without requiring maximum computing power for every task.
Google DeepMind introduced Gemini Robotics 2, a model that coordinates the full body of a humanoid robot instead of controlling only its arms and upper body.
Demonstrations show robots walking, crouching, stretching, reaching, and handling objects in one continuous movement. An Apptronik Apollo 2 robot used five-fingered hands for tasks such as selecting shelf items, sealing plastic bags, tying trash bags, and unscrewing lightbulbs.
Gemini Robotics ER 2 handles the higher-level planning. It interprets a person’s request, understands the surrounding space, divides longer assignments into steps, and can coordinate more than one robot.
DeepMind also updated its On-Device model, which runs locally without a constant internet connection and can adapt to robots with different shapes and sensors. That could reduce delays and keep a robot working when cloud access is unavailable.
Safety features include detecting nearby people and automatically stopping unsafe motion. DeepMind also introduced ASIMOV-Agentic, a test designed to measure whether robot agents choose safe actions during longer tasks.
These are research demonstrations rather than a broadly available consumer product. DeepMind has not announced public pricing or a general release date, and performance outside controlled test settings remains unproven.
Thinking Machines introduced a dedicated release for Inkling-Small, a lighter version of its customizable Inkling model. It contains 276 billion total settings but uses about 12 billion for each piece of text it processes, reducing the computing work needed for each response.
The model accepts text, images, and audio and lets developers adjust how much processing it spends on harder questions. It is designed for work where response speed and cost matter, including coding, grading AI outputs, and producing training examples for other models.
In the company’s tests, Inkling-Small stayed close to the larger Inkling on several evaluations. It scored 77.4% versus 77.6% on SWE-bench Verified and led the larger model on instruction following, but fell behind on Terminal Bench 2.1, banking tasks, factual questions, and some audio tests.
Those results are company-reported benchmarks and may not predict performance in a developer’s own software. “Open weights” means developers can inspect and customize the model files, but it does not mean the full training data and training process are public.
Thinking Machines did not publish clear standalone pricing in the indexed announcement material. Teams should also evaluate the hardware and hosting cost before assuming the smaller active size makes the model inexpensive to deploy.
OpenAI cut the API price of GPT-5.6 Luna by 80% and GPT-5.6 Terra by 20%. Luna now costs $0.20 per million input tokens and $1.20 per million output tokens, while Terra costs $2 for input and $12 for output.
Tokens are small pieces of text that models read and produce. Lower token prices make high-volume work—such as sorting customer messages, reviewing documents, and running routine software tasks—less expensive.
Luna and Terra remain available through the OpenAI API, ChatGPT Work, and Codex. ChatGPT and Codex subscription prices and quota budgets did not change, but Luna and Terra now consume fewer paid credits.
OpenAI also introduced Fast mode for GPT-5.6 Sol. It can run up to 2.5 times faster than Standard processing at twice the price, without changing the model’s stated intelligence.
The company says GPT-5.6 Sol helped engineers reduce model-serving costs by 20% and improve text-generation efficiency by more than 15%. These are OpenAI’s internal measurements and have not been independently verified.
🧠RESEARCH
Researchers tested whether AI agents could independently advance two unpublished machine-learning projects over six days. The agents handled routine coding but failed to make meaningful research progress, and both submissions were rejected. They showed weak judgment, poor backtracking, limited resource awareness, and drifting instructions, revealing barriers beyond improving model intelligence.
OmegaUse-OfficeVal measures AI agents on 100 realistic office tasks, each paired with human completion time and market price. The average task takes a person 2.32 hours. Current models finished faster and cheaper, but produced much worse work, showing that low operating cost does not yet equal consistently useful professional quality.
MemSecBench tests whether AI agents can be attacked through saved memories, such as notes from earlier tasks. Across 310 cases, malicious content survived memory updates 84.2% of the time, and full attacks succeeded 50.3%. Targeted cleanup worked only 56.1%, showing memory needs stronger screening, safer storage controls, and repair tools.
📲SOCIAL MEDIA
🗞️MORE NEWS
Perplexity renamed Spaces as Projects and combined Ask conversations, Computer tasks, files, custom instructions, and collaboration inside one workspace. Projects can inherit shared context, pin important material, search past work, and connect Enterprise files, although project-specific connectors and skills are not yet available.
Banks led by Morgan Stanley are reportedly in advanced talks to lend $15 billion to Nexus Data Centers for a Texas campus built to serve Anthropic. The proposed site would include 1.6 gigawatts of local power, while Google would guarantee Anthropic’s lease and power obligations in exchange for a reported 20% stake; the financing has not been finalized.
OpenAI chief executive Sam Altman was expected to discuss voluntary cybersecurity tests for advanced AI models with senior White House officials after a recent rogue-agent incident. The administration had directed advisers to finalize a voluntary testing framework by August 1, but the requirements and participating companies were not yet settled.
Scale AI hired Google Cloud operating chief Francis deSouza as its new chief executive, replacing interim CEO Jason Droege. DeSouza starts after leaving Google on August 7 and will lead Scale as it expands from training-data services into software that helps businesses deploy AI.
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