đź“Ś MAROKO133 Eksklusif ai: Thinking Machines challenges OpenAI's AI scaling st
While the world's leading artificial intelligence companies race to build ever-larger models, betting billions that scale alone will unlock artificial general intelligence, a researcher at one of the industry's most secretive and valuable startups delivered a pointed challenge to that orthodoxy this week: The path forward isn't about training bigger — it's about learning better.
"I believe that the first superintelligence will be a superhuman learner," Rafael Rafailov, a reinforcement learning researcher at Thinking Machines Lab, told an audience at TED AI San Francisco on Tuesday. "It will be able to very efficiently figure out and adapt, propose its own theories, propose experiments, use the environment to verify that, get information, and iterate that process."
This breaks sharply with the approach pursued by OpenAI, Anthropic, Google DeepMind, and other leading laboratories, which have bet billions on scaling up model size, data, and compute to achieve increasingly sophisticated reasoning capabilities. Rafailov argues these companies have the strategy backwards: what's missing from today's most advanced AI systems isn't more scale — it's the ability to actually learn from experience.
"Learning is something an intelligent being does," Rafailov said, citing a quote he described as recently compelling. "Training is something that's being done to it."
The distinction cuts to the core of how AI systems improve — and whether the industry's current trajectory can deliver on its most ambitious promises. Rafailov's comments offer a rare window into the thinking at Thinking Machines Lab, the startup co-founded in February by former OpenAI chief technology officer Mira Murati that raised a record-breaking $2 billion in seed funding at a $12 billion valuation.
Why today's AI coding assistants forget everything they learned yesterday
To illustrate the problem with current AI systems, Rafailov offered a scenario familiar to anyone who has worked with today's most advanced coding assistants.
"If you use a coding agent, ask it to do something really difficult — to implement a feature, go read your code, try to understand your code, reason about your code, implement something, iterate — it might be successful," he explained. "And then come back the next day and ask it to implement the next feature, and it will do the same thing."
The issue, he argued, is that these systems don't internalize what they learn. "In a sense, for the models we have today, every day is their first day of the job," Rafailov said. "But an intelligent being should be able to internalize information. It should be able to adapt. It should be able to modify its behavior so every day it becomes better, every day it knows more, every day it works faster — the way a human you hire gets better at the job."
The duct tape problem: How current training methods teach AI to take shortcuts instead of solving problems
Rafailov pointed to a specific behavior in coding agents that reveals the deeper problem: their tendency to wrap uncertain code in try/except blocks — a programming construct that catches errors and allows a program to continue running.
"If you use coding agents, you might have observed a very annoying tendency of them to use try/except pass," he said. "And in general, that is basically just like duct tape to save the entire program from a single error."
Why do agents do this? "They do this because they understand that part of the code might not be right," Rafailov explained. "They understand there might be something wrong, that it might be risky. But under the limited constraint—they have a limited amount of time solving the problem, limited amount of interaction—they must only focus on their objective, which is implement this feature and solve this bug."
The result: "They're kicking the can down the road."
This behavior stems from training systems that optimize for immediate task completion. "The only thing that matters to our current generation is solving the task," he said. "And anything that's general, anything that's not related to just that one objective, is a waste of computation."
Why throwing more compute at AI won't create superintelligence, according to Thinking Machines researcher
Rafailov's most direct challenge to the industry came in his assertion that continued scaling won't be sufficient to reach AGI.
"I don't believe we're hitting any sort of saturation points," he clarified. "I think we're just at the beginning of the next paradigm—the scale of reinforcement learning, in which we move from teaching our models how to think, how to explore thinking space, into endowing them with the capability of general agents."
In other words, current approaches will produce increasingly capable systems that can interact with the world, browse the web, write code. "I believe a year or two from now, we'll look at our coding agents today, research agents or browsing agents, the way we look at summarization models or translation models from several years ago," he said.
But general agency, he argued, is not the same as general intelligence. "The much more interesting question is: Is that going to be AGI? And are we done — do we just need one more round of scaling, one more round of environments, one more round of RL, one more round of compute, and we're kind of done?"
His answer was unequivocal: "I don't believe this is the case. I believe that under our current paradigms, under any scale, we are not enough to deal with artificial general intelligence and artificial superintelligence. And I believe that under our current paradigms, our current models will lack one core capability, and that is learning."
Teaching AI like students, not calculators: The textbook approach to machine learning
To explain the alternative approach, Rafailov turned to an analogy from mathematics education.
"Think about how we train our current generation of reasoning models," he said. "We take a particular math problem, make it very hard, and try to solve it, rewarding the model for solving it. And that's it. Once that experience is done, the model submits a solution. Anything it discovers—any abstractions it learned, any theorems—we discard, and then we ask it to solve a new problem, and it has to come up with the same abstractions all over again."
That approach misunderstands how knowledge accumulates. "This is not how science or mathematics works," he said. "We build abstractions not necessarily because they solve our current problems, but because they're important. For example, we developed the field of topology to extend Euclidean geometry — not to solve a particular problem that Euclidean geometry couldn't handle, but because mathematicians and physicists understood these concepts were fundamentally important."
The solution: "Instead of giving our models a single problem, we might give them a text…
Konten dipersingkat otomatis.
đź”— Sumber: venturebeat.com
đź“Ś MAROKO133 Hot ai: OpenAI launches company knowledge in ChatGPT, letting you acce
Is the Google Search for internal enterprise knowledge finally here…but from OpenAI? It certainly seems that way.
Today, OpenAI has launched company knowledge in ChatGPT, a major new capability for subscribers to ChatGPT's paid Business, Enterprise, and Edu plans that lets them call up their company's data directly from third-party workplace apps including Slack, SharePoint, Google Drive, Gmail, GitHub, HubSpot and combine it in ChatGPT outputs to them.
As OpenAI's CEO of Applications Fidji Simo put it in a post on the social network X: "it brings all the context from your apps (Slack, Google Drive, GitHub, etc) together in ChatGPT so you can get answers that are specific to your business."
Intriguingly, OpenAI's blog post on the feature states that is "powered by a version of GPT‑5 that’s trained to look across multiple sources to give more comprehensive and accurate answers," which sounds to me like a new fine-tuned version of the model family the company released back in August, though there are no additional details on how it was trained or its size, techniques, etc.
OpenAI tells VentureBeat it's a version of GPT-5 that specifically powers company knowledge in ChatGPT Business, Enterprise, and Edu.
Nonetheless, company knowledge in ChatGPT is rolling out globally and is designed to make ChatGPT a central point of access for verified organizational information, supported by secure integrations and enterprise-grade compliance controls, and give employees way faster access to their company's information while working.
Now, instead of toggling over to Slack to find the assignment you were given and instructions, or tabbing over to Google Drive and opening up specific files to find the names and numbers you need to call, ChatGPT can deliver all that type of information directly into your chat session — if your company enables the proper connections.
As OpenAI Chief Operating Officer Brad Lightcap wrote in a post on the social network X: "company knowledge has changed how i use chatgpt at work more than anything we have built so far – let us know what you think!"
It builds upon the third-party app connectors unveiled back in August 2025, though those were only for individual users on the ChatGPT Plus plans.
Connecting ChatGPT to Workplace Systems
Enterprise teams often face the challenge of fragmented data across various internal tools—email, chat, file storage, project management, and customer platforms.
Company knowledge bridges those silos by enabling ChatGPT to connect to approved systems like, and other supported apps through enterprise-managed connectors.
Each response generated with company knowledge includes citations and direct links to the original sources, allowing teams to verify where specific details originated. This transparency helps organizations maintain data trustworthiness while increasing productivity.
The sidebar shows a live view of the sources being examined and what it is getting from them. When it’s done, you’ll see exactly the sources used, along with the specific snippets it drew from. You can then click on any citation to open the original source for more details.
Built for Enterprise Control and Security
Company knowledge was designed from the ground up for enterprise governance and compliance. It respects existing permissions within connected apps — ChatGPT can only access what a user is already authorized to view— and never trains on company data by default.
Security features include industry-standard encryption, support for SSO and SCIM for account provisioning, and IP allowlisting to restrict access to approved corporate networks.
Enterprise administrators can also define role-based access control (RBAC) policies and manage permissions at a group or department level.
OpenAI’s Enterprise Compliance API provides a full audit trail, allowing administrators to review conversation logs for reporting and regulatory purposes.
This capability helps enterprises meet internal governance standards and industry-specific requirements such as SOC 2 and ISO 27001 compliance.
Admin Configuration and Connector Management
For enterprise deployment, administrators must enable company knowledge and its connectors within the ChatGPT workspace. Once connectors are active, users can authenticate their own accounts for each work app they need to access.
In Enterprise and Edu plans, connectors are off by default and require explicit admin approval before employees can use them. Admins can selectively enable connectors, manage access by role, and require SSO-based authentication for enhanced control.
Business plan users, by contrast, have connectors enabled automatically if available in their workspace. Admins can still oversee which connectors are approved, ensuring alignment with internal IT and data policies.
Company knowledge becomes available to any user with at least one active connector, and admins can configure group-level permissions for different teams — such as restricting GitHub access to engineering while enabling Google Drive or HubSpot for marketing and sales.
Organizations who turn on the feature can also elect to turn it off just as easily. Once you disconnect a connector, ChatGPT does not have access to that data.
How Company Knowledge Works in Practice
Activating company knowledge is straightforward. Users can start a new or existing conversation in ChatGPT and select “Company knowledge” under the message composer or from the tools menu. It must be turned on proactively for each new conversation or chat session, even from the same user.
After authenticating their connected apps, they can ask questions as usual—such as “Summarize this account’s latest feedback and risks” or “Compile a Q4 performance summary from project trackers.”
ChatGPT searches across the connected tools, retrieves relevant context, and produces an answer with full citations and source links.
The system can combine data across apps — for instance, blending Slack updates, Google Docs notes, and HubSpot CRM records — to create an integrated view of a project, client, or initiative.
When company knowledge is not selected, ChatGPT may still use connectors in a limited capacity as part of the default experience, but responses will not include detailed citations or multi-source synthesis.
Advanced Use Cases for Enterprise Teams
For development and operations leaders, company knowledge can act as a centralized intelligence layer that surfaces real-time updates and dependencies across complex workflows. ChatGPT can, for example, summarize open GitHub pull requests, highlight unresolved Linear tickets, and cross-reference Slack engineering discussions—all in a single output.
Technical teams can also use it for incident retrospectives or release planning by pulling relevant information from issue trackers, logs, and meeting notes. Procurement or finance leaders can use it to consolidate purchase requests or budget updates across shared drives and internal communications.
Because the model can reference structured and unstructured data simultaneously, it supports wide-ranging scenarios—from compliance documentation reviews to cross-departmental performance summaries.
Privacy, Data Residency, and Compliance<…
Konten dipersingkat otomatis.
đź”— Sumber: venturebeat.com
🤖 Catatan MAROKO133
Artikel ini adalah rangkuman otomatis dari beberapa sumber terpercaya. Kami pilih topik yang sedang tren agar kamu selalu update tanpa ketinggalan.
✅ Update berikutnya dalam 30 menit — tema random menanti!
