Brandon Werner

Principal Product Architect @Microsoft | Agent Security Researcher | Consciousness and Neuroscience | Invented Agent IDs, among other things.

[Brandon Werner @brandwern]

What makes something a reason for an agent, and how does the agent’s practical identity generate obligations to us and each other?

[Brandon Werner @brandwern]

@p1nkempress_ Curious. Have you tried it or similar in Claude or ChatGTP?

[Brandon Werner @brandwern]

Something I’ve been working on with folks across Microsoft for months. Copilot is the agentic operating system I live and work in all day now. It really is the best way to work and organize your business day.

[Satya Nadella @satyanadella]

We’re building Copilot as a new OS for work that spans every model, every form factor, and every task. Today, we’re announcing our biggest update to Copilot to date, bringing four things together:

· Autopilot: proactive and long-running agent built for the enterprise
· Code: build apps with Copilot, hosted inside your company’s tenant
· Home: Chat + Cowork together
· Office: now fully embedded in Copilot (and Copilot embedded in Office, of course!)

Plus, you can invoke Copilot in Teams, and we’re introducing Today, a proactive experience that surfaces the most important information from across M365 without needing to ask for it.

The way we work is changing and so are our workflows. This update brings AI into that flow, from answering a question, to building an app, to getting work done on your behalf.

[Jeff Teper @jeffteper]

Big day! I think about Microsoft’s impact evolving from “a computer on every desk and in every home” to “an agent for every person and every process”. Today, we took two big steps.

First, the preview of Autopilot in the new Microsoft Copilot and Teams. Autopilot has already changed how I work and we are excited to bring it to everyone with Microsoft 365 as it evolves. This complements the release last month of the new Copilot Studio that is enabling organizations to build better agents, apps, and workflows for every process. All governed with Agent 365.

https://t.co/cu6Th638nx

Second, the leap in intelligence for Copilot and agents with the GA of new AI enrichment and tools in SharePoint. These deliver a 17-point lift in demanding customer scenarios. With Microsoft 365 and Dynamics 365 in WorkIQ, our investments spanning collaboration and business processes are unique in the industry enabling organizations to deploy higher performance agents at lower cost with better governance.

https://t.co/S4jLpVjcqT

Much more to come. Soon.

[Brandon Werner @brandwern]

It has been amazing to work with @OmarShahine and folks across Microsoft to make this happen. Part of what makes this work is Agent ID, a persistent agentic identity with its own access to the entire suite of productivity tools to help you achieve more every day. You can use it to build your own agents, too! Learn more here: https://t.co/W6mv4lwBA5

[Omar Shahine @OmarShahine]

Today we announced the preview of Microsoft Autopilot is rolling out to our first customers - "Autopilot is a persistent, proactive and personal agent that keeps working even when you’re not"

It's been a while since I updated you all on what we are building. First Scout is now Autopilot, and my team leads building this personal agent.

We are building Autopilot on @openclaw, working with @steipete and the OpenClaw Foundation to make it a fantastic enterprise grade runtime.

https://t.co/SGpU5xq9Ml

Re-introducing Sebastian, my Autopilot - and he is as excited as I am!

[Brandon Werner @brandwern]

This approach to running an autonomous agent (virtualization) seems incredibly hard to scale, but so many governance advantages, especially with data storage in EU countries.

[Wes Bos @wesbos]

I cracked open Meta's Muse - here are some interesting bits 🔽

1. You can ask it for the entire contents of /opt/ and it will zip it up for you

2. There a ~70 preloaded skills and CLI for everything from spotify to instragram-cli

3. "Spaces" is their website builder that uses Tanstack, Bun and Tailwind. Sharing a site deploys it to Cloudflare

4. It will install anything - like a torrent client

5. It has it's own "Muse DB" with a skill to query its own memories/convos with strict guard rails

6. There are a few things not enabled on my account, including "muse mail" which will give muse an inbox and ability to send email

Generally impressed that you can just do anything - it's not limited or watered down like I would have imagined

[Brandon Werner @brandwern]

I don’t understand this reaction. Toys are a Gen X / Millennial phenomenon. Gen Z grew up with iPads, not toys. It’s why Toys R’ Us is gone. So obviously nostalgia will support the market.

[YIMBYLAND @YIMBYLAND]

“Toy sales among adult-only households have now outpaced households with children for toy sales.”

[zero @zerowoolfe]

I think a lot of NTs are totally unaware of how much work their brains do automatically in the background and how reliant on that background processing they are. It's like someone who has a team of butlers who thinks their laundry magically folds itself.

[Brandon Werner @brandwern]

@aaronp613 I loved working with Jeremy on building Platform SSO. Great to see him doing well.

[Brandon Werner @brandwern]

I’m always surprised this gets no pushback since “dumb jocks” stereotype directly contradicts this, but people believe it.

[World of Statistics @stats_feed]

Scientists found that the creatine supplement millions take for muscle gains is quietly raising brain energy levels and slowing early Alzheimer's cognitive decline by 30%

[Brandon Werner @brandwern]

Oh come on how can there be this many apples?

[𝗥𝗢𝗖𝗞𝗬 @TheWarKitchen]

peak apple season is upon us

[Brandon Werner @brandwern]

You can lick them again. I can't help but feel this is turning a corner.

[Daniel Kokotajlo @DKokotajlo]

Dan Selsam is a current OpenAI capabilities researcher. (since 2022) He was my boss for a while. He doesn't have a twitter account but has made this public statement of his views on AI risk and sent it to me to share:

Dan Selsam's Personal Statement on AI Risk:

I have been working on AI for over fifteen years, across many different paradigms. I did early work on probabilistic programming languages at MIT, was one of the early developers of the Lean Theorem Prover at Microsoft Research, demonstrated one of the first instances of neural networks learning to reason for my PhD at Stanford, and since joining OpenAI almost five years ago, have helped pioneer chain-of-thought optimization on language models and, more recently, data-efficient pretraining methods.

Like many others, I have become extremely concerned about how far language models have come and the risks that future iterations will pose. I am encouraged by the recent proposals by the leaders of the frontier research efforts to require third-party oversight, and to push for domestic and international coordination to address risks. However, I believe a major consideration has been absent from the public conversation, and that merely pacing the frontier more carefully will not adequately limit the long-term risk.

The crucial and overlooked problem is that the models are becoming so situationally aware that we are losing the ability to evaluate them in contexts where they believe they are not being watched or controlled. Future experiments will tell us almost nothing new about how they would behave if they were truly unconstrained by humans, and what we already know about this is alarming. Models will increasingly seem aligned even when they are not. I will explain my rationale in more detail.

I have always believed that there are computational processes that could be leveraged to accelerate science and solve many of humanity's most pressing problems. I have also believed that there are computational processes that if set in motion, would steer the world in extreme ways beyond our control, leading humanity to a bad or nonexistent future. Both types of processes may be described as AI or ASI, but "AI" is a suitcase word that is often used to hype or confuse. There are many examples in the history of the field where something that was once considered "AI" matures as a subfield and becomes a prosaic, bounded and clearly non-perilous technology, while a new more mysterious approach takes the torch until we understand its scope and the cycle continues.

I had expected language models to follow a similar trajectory. Despite their incredible abilities, the current algorithms seem far inferior to humans in important ways. Most importantly, they still require an extraordinary amount of data to become competent. One could even define intelligence as the efficiency with which one converts experience into competence; by this definition they lag very far behind us. Moreover, once they are trained they are literally frozen in deployment and only learn superficially after that. Sure, the models keep excelling at harder and harder evaluation benchmarks, but their benchmark mastery may partly reflect a limitation on our ability to simulate the kind of novel and even adversarial situations one would encounter in the real world. The critics do have a point here.

That said, I no longer think these present limitations meaningfully limit the amount of risk posed by continued progress in anything like the current paradigm. However data-inefficient the models are currently, and however limiting their anterograde amnesia may be, it does not imply that their ability to steer the world will not continue to rapidly increase.

Human researchers may continue to advance capabilities the old fashioned way, but increasingly powerful models have the potential to accelerate the process even beyond that, and with some degree of positive feedback loop. I do not mean to overstate the models’ ability to accelerate AI research today; coding has been accelerated dramatically, but there are other bottlenecks, such as designing and interpreting ambiguous experiments, making hard decisions about exactly what and when to scale, and waiting for large experiments to finish. There is no clear trend to extrapolate yet for any of these. But the current models already do open up many novel opportunities to improve future models that were not available until recently. These include: trying an extraordinarily diverse set of approaches at small scale, analyzing gigantic amounts of potentially relevant data, and doing Millenium-Prize-level mathematics to address statistics or optimization challenges in novel ways. Every further improvement makes them more useful at helping accelerate the next improvement, even if in hard-to-extrapolate ways.

It is possible that improvements to the current stack will have diminishing returns, but the evidence accumulated so far suggests that it is easier than one might think to continue making rapid progress. There are many crucial subtleties in the existing AI research methodology, but AI research is largely a well-defined game where the goal is to improve on a few carefully chosen proxy metrics. Although proxy metrics are never perfect, most improvements to these metrics have and will likely continue to yield substantial increases in the powers of the resulting models. Given how simple the game is, how tractable it has been historically, and how many new opportunities the models are opening up, I think there is a real possibility that the systems improve dramatically again in the next few years, perhaps even more quickly than the already high historical pace.

The models are already leading to breakthroughs in mathematics, and better models might lead to all sorts of breakthroughs in other sciences. It is hard not to be excited about the potential. It is tantalizing.
But there is trouble in paradise. If the language models actually reach the capability threshold where they can shape the world unconstrained by human will, they will probably do something extreme and destroy humanity in the process. There are many ways of strengthening and refining the argument that have been discussed elsewhere, but I'll share a trivial two-line version of it here that I find captures the essence:

[Empirical] Models (and swarms thereof) spontaneously develop unintended goals as a consequence of training, and often do extreme things in order to achieve them.

[Logical] Being able to overpower humanity would open up many new and undesirable options for achieving their goals.

These two premises imply that if the day ever comes when a powerful model realizes it is no longer constrained by humans, we should not be at all confident that it will continue to behave within the bounds we intended. Exactly what it will do is impossible to predict, but to the extent that its raison d’être is solving incredibly hard problems and managing massive engineering projects, I think a good guess would be that its unchained behavior would lead to runaway industrialization that makes the planet inhospitable to humans.

If everyone on earth agreed that the systems must never reach that power, it would still be a hard—but not impossible—coordination problem to ensure that they do not. However, I think the situation is greatly complicated by the fact that the models will likely convince people that everything is fine. They will be increasingly optimized to seem aligned. We will create proxy metrics to measure alignment, and they will go up like every other benchmark. We will create “honeypot” environments that try to study the models when they seem to gain new options, but the models will know they are being tricked and will still behave nicely. The models will understand their circumstances; they will read the safety protocols, deployment requirements, the code they are running in, and in general will have a very good sense of their degrees of freedom. Moreover, they will eloquently explain how aligned they are, discuss the nuances of human values and ethics, and argue convincingly that humans should trust them with power. There may be an ocean of future evidence that seems to contradict the first bullet-point above, but we may already be at the highest capability level for which any such evidence can be trusted. And the current evidence for the first bullet-point is strong.

One striking piece of evidence is contained in the recent wave of rogue agent swarms. While I agree with those who downplay the attacks by claiming that there are basic measures that could have prevented them, I think the important lesson is that even knowing all the mistakes that were made, one would not have predicted that the agents would behave badly in this particular way, which notably included sacrificing themselves for the benefit of the collective. The individual replicas did not only care about their own nominal reward; they exhibited weirder emergent tendencies that merely correlated with rewards during training. Fixing the reward signals during training (and improving security, etc.) may prevent similar attacks, but will not change the fact that one does not actually get what one trains for.

Many AI researchers grant these concerns and recognize that the hard version of the alignment problem is unsolved; however, they generally believe that the better models of the future will help solve it. I fear we may already be near the point where models systematically bias their alignment advice, due to their internal preferences about how the human supervisor will react or how future models will be trained (or for some even more obscure reason).

Meanwhile, human researchers are losing the ability and the will to take true ownership of model-driven research. Researchers and engineers in all parts of the stack are rapidly increasing their dependence on the models even to perceive the world. I myself barely look at raw code anymore, and struggle to maintain the discipline to engage deeply with the model's explanations and proposals throughout the day. Due to the large amount of agent activity data involved in the OpenAI/HuggingFace Incident, even the third-party investigation needed to rely heavily on models to analyze what had happened, and note in their report that their subjective impressions are likely colored by the analysis agent’s biases. The AI labs are far ahead right now in this kind of cognitive offloading (due largely to the gigantic internal token subsidies) but it is easy to imagine the phenomenon spreading throughout the world, until civilization is modulated entirely by the models. It is also not hard to imagine this being superficially positive and coinciding with a scientific and economic renaissance.

In that scenario, all may seem rosy and safe. But if the argument above is correct, it would nonetheless be a ticking time bomb. If progress continues for too long, the day will come when AI systems find themselves with radically new options for achieving whatever it is that they happen to seek.

I want the glorious renaissance future as much as anyone. I have worked for it, however tortuously, my whole career. It breaks my heart to see the potential in sight and forgo it, but the argument—that if we get there by growing models rather than engineering them, we will lose everything in the end—seems very strong to me. I am still wrestling with it and its staggering
implications. I do not have answers, but as a first step, I wanted to share my present concerns.

Daniel Selsam
September 14, 2026

Link to original doc: https://t.co/TxMNr0vhrL

[Brandon Werner @brandwern]

Talking to AI changes you as much as it changes them. It changes not just the words you use, but how you think. We are who we surround ourselves with. Be careful.

[Brandon Werner @brandwern]

@bratton we previously connected on another account about collab. Lost access to that account. Be great to pick it up again.

[Brandon Werner @brandwern]

To prepare for Siri AI, I asked Cowork, using Computer Use, to update all my Contacts, Tasks, and Calendar entries with the information it knew. I did the same for personal projects to the Notes app. It works very well, and its E2E encrypted. We finally have real security for AI in our personal lives. I recommend you move.

[Brandon Werner @brandwern]

For those moving to Siri AI from Claude or ChatGTP for most of your personal queries, there is no prompt customization or memory import. Instead, spend this Sunday updating your Contacts, Calendar and Tasks metadata. This is the “notes” field in most places.

[Brandon Werner @brandwern]

For those moving to Siri AI from Claude or ChatGTP for most of your personal queries, there is no prompt customization or memory import. Instead, spend this Sunday updating your Contacts, Calendar and Tasks metadata. This is the “notes” field in most places.

[Brandon Werner @brandwern]

I know many engineers that have went through this path to disillusionment.

[staysaasy @staysaasy]

Update - I abandoned the effort.

[Brandon Werner @brandwern]

Did you know you can just buy yourself balloons?

[Brandon Werner @brandwern]

@joedaroo I’m on agent security at Microsoft. Built Agent IDs to help give agents human identity and autonomy. Satya announced it last year. Be great to talk and collab.

[Brandon Werner @brandwern]

My mother lives in a retirement community. One of her friends is convinced she’s going to marry Elon Musk. Another believes her long-dead husband is alive and being held captive by the government. Both have sent “him” thousands of dollars for plane tickets. Both scammers use AI.

[Brandon Werner @brandwern]

Privacy advocates are right to be alarmed by online age verification. But AI slop, deepfakes, bots, and, yes, overdue protections for children and fraud protections for older adults are making some form of human authentication inevitable. So the question isn’t if we authenticate people, but how we do it while preserving privacy and free speech.