
Toby Padilla, Harald Kirschner and I will be live-streaming on The Context, on the MCP Developers Summit YouTube channel this coming Tuesday.

https://www.youtube.com/live/rtht8dbwe5U?feature=shared

The channel hosts the amazing content from their conference, as well as numerous videos and livestreams on all sorts of MCP and AI related topics.

I'm honoured to be invited to talk about my involvement in building the GitHub MCP Server, there have been so many legends in the field on their channel! I can't wait to share the ideas, problems and dreams we have.

The Context - Special Episode ft. GitHub & Microsoft



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> Join us for VS Code Live: Extending Agent Mode, a livestream showing how to unlock the full potential of agent mode using MCP servers and extension tools. We'll kick things off with a look at VS Code's vision in the MCP space, followed by demos from the VS Code team. Plus, we'll spotlight powerful MCP servers and extensions that you can start using in agent mode right away - featuring special guests you won't want to miss!

I had great fun chatting with Olivia Guzzardo McVicker (@OliviaGuzzardo) on the VS Code Livestream on YouTube about the [Remote GitHub MCP Server](https://github.blog/changelog/2025-06-12-remote-github-mcp-server-is-now-available-in-public-preview/) that launched during the episode! We chat about the server, I demo some functionality live and we also chat about using and extending agents in general.

I hope she'll have me back on again in future to see what we've achieved over the next few months.

VS Code Live: Extending Agent Mode


If you're like many people, the rapid rise of AI tools might bring a mix of excitement and, perhaps, some unease. It's natural to wonder what it all means for how we work. I've been thinking a lot about this, especially in my role with GitHub Copilot (although comments and opinions on this blog are my own), and I've noticed something that feels important to share.

Will AI make our existing tasks faster and easier, or will it replace our jobs entirely? I believe the answer is neither.

I've come to a different conclusion, which I think is more honest: AI probably won't make your job easier in the ways you expect, but it *can* help you do work that's genuinely better than what you might have produced before, and I do not believe we have arrived at an employment extinction event.

---

## The Quality Shift

When powerful AI tools become widely accessible, something interesting happens to the value of work.

Standard, good-enough output becomes abundant. Whether it's code, writing, or analysis, the baseline of acceptable is easier for everyone to reach. This also means that simply meeting that baseline is no longer a differentiator, worse than that it's almost indistinguishable from the noise floor of AI slop that people are already cursing the internet with. Tasks AI can do excellently autonomously, should be offloaded to AI, so what is left?

Truly excellent work—work that demonstrates deep understanding, thoughtful judgment, or unique insight—becomes necessary to stand out from the new noise floor.

This isn't just about working harder or faster. It's about the nature and quality of what we produce, and redefining what our best work is.

> Something I told 16 yo: In the near term, AI will increase variation in the returns for work. For example, mediocre programmers are now finding it hard even to get hired, but great programmers are making more than ever.

> Something else I told him: This is a trend that has been going since the stone age. Technology always increases variation in the returns for work. The bottom end is anchored at zero. Technology lets the top end move.

*Paul Graham*

---

## Moving Beyond "Write This For Me"

I've noticed a common pattern (and I've done this myself) when we first start using AI tools:

We provide minimal input, ask the AI to generate something complete, and then make a few tweaks.

It often works, to a degree. The output is usually... fine. But it rarely feels remarkable, and sometimes there's a subtle "off-ness" that's hard to pinpoint or fix with simple edits.

I've come to believe this isn't necessarily a limitation of the tools themselves, but more about how we're engaging with them.

---

## Finding A More Collaborative Approach

What's been working better for me is treating AI less like a vending machine and more like a thinking partner:

- Using it to explore various approaches to a problem, some of which I wouldn't have considered on my own.
- Asking it to critique my work (and then, importantly, critiquing its critique in return). With current models you have to push hard for critical feedback, they behave like sycophants by default, so ignore their flattery or at least remain suspicious of it.
- Using it to help articulate complex thoughts I'm struggling to put into clear words.
- Giving it rich context: not just *what* I'm trying to do, but *why* and what the broader goals are. This includes doing real research. Data, citations and subtle nuance are all things you should provide as context. 

It's important to note that these approaches often don't save time upfront. In fact, they can sometimes take longer than just diving into the work directly. The real difference I'm seeing is in the depth and quality of the final result.

---

## A Personal Example

Recently, I was wrestling with an annual performance self-reflection. My early drafts lacked a narrative that contextualised the relevance of my work to the company as a whole, undersold things and also lacked hard numbers, citations and other additions that would give clout to my words.

Instead of prompting AI to "write my self-reflection," I tried a more layered process:

I gathered all the relevant context—project outcomes, feedback from colleagues, company values, specific challenges I'd encountered—and then worked with an AI to help me structure and articulate this information coherently.

We went back and forth multiple times. I'd challenge its framing of a point; it would prompt me to consider an angle I'd missed. We explored different ways to structure the narrative.

The final document definitely took longer to create than if I'd just pushed through on my own. But it was clearer, more balanced, and, I think, more insightful than what I would have produced in isolation. I expect the next one will be much faster too.

---

## Some Things I've Learned About Working With AI

If you're looking to explore this kind of collaborative approach, here are a few things I've found helpful:

- **Be generous with context.** AI tools provide better assistance when they understand not just *what* you want, but *why* you want it and what success looks like.

- **Use it for thinking, not just producing.** Some of the most powerful uses involve exploration, brainstorming, critique, and refining ideas, rather than just generating finished products from scratch.

- **Expect to iterate.** As screenwriting guru Robert McKee wisely said: "Secure writers don't sell first drafts. They patiently rewrite until the script is as director-ready, as actor-ready as possible. Unfinished work invites tampering, while polished, mature work seals its integrity." This principle holds true for AI collaboration; the real value often emerges through a process of refinement.

- **Give yourself permission to experiment.** The most effective ways to use these tools are still being discovered. What works best for you might be different from what works for others, and new models are released almost daily.

---

## It's Not Just About Productivity

One of the most interesting realizations for me has been that the primary value here isn't solely about boosting productivity in the traditional sense.

A thoughtful VP at Cloudflare, William Allen, put it well: "The lesson isn't 'do more with less'—it's 'do more with more.'"

The opportunity isn't just to produce the same work faster. It's about raising the ceiling on the quality and impact of what we can achieve.

---

## A Suggestion

If you're navigating the world of AI tools, perhaps consider shifting your primary expectation from "this will make my current job easier" to "this might help me do work I'm even more proud of."

With that mindset, the learning curve can feel different. The occasional frustrations or clunky outputs become part of the investment in a new skill, rather than just failures of the tool.

And the collaboration—because that's what it truly is when it works best—can lead to outcomes that genuinely surprise you, in a good way.

Not necessarily faster, not always easier. But potentially, meaningfully better.

---

## A Note on How This Came Together

If you've made it this far, thanks for reading! I wanted to share a little about the process behind this piece, as it mirrors some of the iterative collaboration I've been discussing.

This article didn't spring out fully formed. It started as a stream-of-consciousness reflection on my experiences and observations. From there, it went through quite a journey:

*   I ran initial drafts and ideas through four different AI models, using them not just for generation, but for critical discussion, feedback on tone, and to explore different ways of articulating the core message.
*   There were many rounds of manual edits and rewrites based on my own reflections and the AI's input.
*   I consciously wove in quotes from people whose ideas resonated with the points I wanted to make.
*   Crucially, I also shared drafts with human friends and colleagues to get their invaluable perspectives and feedback.
* I consciously left in em dashes (I am not pretending I didn't get help from AI, why would I? This is exactly what I wanted to write and better use of punctuation is a gift)
*   Finally, I let it sit for a while, giving myself time to reflect and ensure it felt authentic to my voice and experience before feeling ready to share it.

It was a process of layering, refining, and sometimes stepping away. I genuinely hope you enjoyed reading it, and more importantly, that you found something to consider in your own interactions with AI.

For my part, I certainly enjoyed the process of producing it and learned a lot more about my own writing style and how to collaborate effectively with these new tools along the way.

Blog

The Context - Special Episode ft. GitHub & Microsoft

VS Code Live: Extending Agent Mode

AI Won't Make Your Job Easier—so what will it do?