Art credit @Madison Harney

Meg's POV: The risk of context graphs driving agents

I am re-posting this from a stand alone post because I believe this to be important and will take longer for this to become clear.

It is well known that timing is one of the most important elements of success (in life and in business). Anyone who has done their best work in a downmarket or had the right idea too early will understand this viscerally. As I reflect the conversations on context graphs (Foundation Capital, Dharmesh Shah) and resolution libraries (Usman Sheikh) I can't help but wonder how much of my hesitation is about timing vs. execution viability.

If I have to guess, I'd put the ratio today at 60/40.

60% of this idea of context capture is do-able and will happen. We will get better at ingesting insights and information and we will recognize the value of this context for both agent and human work optimization. We will find gaps in what we gather from digital exhaust, and we will devise new types of data capture to fill in the gaps. We will get better at this over time and the realized value will be real and relatively quick.

40% will be materially harder. There are two huge challenges we need to take seriously, one that Usman calls out and one that I think I am the only one to say out loud (but do correct me if I am wrong).

1) h/t Usman

❝

"Making judgment legible threatens the people whose status depends on opaque intuition."

Disruption of high status roles takes a looonnnggg time. Of course, eventually people who achieved status with legacy thinking will retire, but that approach is a decades (not years) process.

I noted this some time back and again early in our podcast series, because the material shift of high status work will be unsettling.

2) Meg POV: There is an additional nuance that isn't exactly captured in the above - humans are emotional not rational decision makers. We understand concepts and abstraction in our right hemisphere - the part of our brain that does not have language. Capturing or even understanding emotional decisions is very hard.

Is this intuition really just pattern recognition and thus eventually unlocked by an algorithm? I suspect so - but to do this you still need to have access to more data and I am lacking sufficient imagination to find where that data will be sourced from. In my own life, I source this data from observing contextual, cultural, emotional and non verbal cues. Listening for what is left unsaid and being aware of survivorship bias.

So then we have to wonder - is this 40% the "uniquely human" skill that won't be replaced by AI we all are hoping for, OR does the future have us activating business with dunning kruger-like over confidence based on the 60%?

It's in this last question that my real concern emerges. Of course, we could agree that a lot of business is operating on this 60% today anyway, but my counter point is that it is also operating on decades of pattern application and trial and error to create currently successful business outcomes.

So I guess my real question is are we going to see #AI move from mansplaining as a service to dunning-kruger as a service?

Reading and Pondering

  1. Act as if you love the future -  A great <10min podcast but also a great insight on how to drive action vs. hand wringing on the topic of climate. You might want to follow Ayana’s substack or check out this list of #badass #women doing the work on this important topic.

  2. 2026 Macro-Risks - a great report from Eurasia group on this topic - one that I haven’t been thinking enough about is the Water risk (not just humanitarian but also geo-political)

  3. Long term industrial planning - this is the core deficit and it’s both a symptom and an outcome of our current US partisan reality. This article talks about Rare Earth Minerals - an important topic in its own right, but I see a lot of parallels with our business strategy execution as well.  We are stepping into a moment where we have real opportunity but the execution paths require long term commitment and some real execution chops. This is the actual disruptive pattern we are noticing when we talk about transformation.

  4. Women’s health investments are creating wealth AND making the world better for everyone. Meg POV here and my personal venture journey here

  5. SF expands free childcare to 150% of median income. I love seeing this making progress.

  6. US Healthcare at 18% GDP, [inflation adjusted] out of pocket expenses have doubled, projected to get to almost 20% by 2032 - Meg POV: This is mostly about demographic shifts (aging Boomers) with a sub-plot of broader inflationary pressure but no matter how you measure this it’s a huge part of our economy.

  7. Invisible unpaid care work getting in the way of building an AI business case.  [please read this with the kind of tone you would read an Onion headline]

  8. AI Proficiency Report Jan 2026 - Calling out the knowing doing gap of business and the execution gap between what executives think is true and what is actually true in the org.

  9. Context graphs - the new new thing (and something we’ve been talking about on the podcast for awhile).  The key point of the glue being the tell fits nicely with the insight from Sangeet Paul Choudary about coordination costs. Another good take on this from Dharmesh Shah and my own $.02 add The concept of resolution libraries added by Usman Sheikh and how in many businesses these things are strategically opaque. Managing work by Enterprise Graph - provocative take from Arvind Jain

  10. We talk a lot about tariffs and not enough about subsidies - this was an eye opener to me - will be digging in on this a bit more. Source (data)

  11. AI Resource Race (Electricity, water and rare earth minerals)

Work and Jobs

  1. More Amazon layoffs (or I guess this is really just the start of another layoff season)

  2. AI agent oversight is a job - this job is growing and very important.

  3. AI layoffs are really just run of the mill corporate layoff spin

  4. Labor market is predictably sluggish, more job hugging, more part-time work with healthcare being the primary bright spot.

  5. 2026 jobs on the rise report - Interesting to see founders in top spot - revealing the reality that not many companies are hiring these days.

  6. Measuring productivity of work when the past assumptions are no longer valid. h/t Russell Fradin Meg POV: There are a few huge concepts to take away here that I’m ponding on the regular - 1) how are we accounting for costs and productivity when some of the work is done by software/technology - one might have guessed we tackled this before with prior waves of automation, but honestly this is different in many subtle and important ways. It's different in scale, execution path and materiality. I personally see this as closer to an economic shock than a traditional technology enablement and I believe that our existing measurement frameworks are severely lacking.

Enshittification and P(catastrophe)

  1. Influencer accents - I didn’t know this was a thing but after it was explained I couldn’t stop hearing it (you have been warned). Meg POV: We are all part of a massive social experiment and like most social experiments we are proving that humans are quite easily manipulated and wildly conforming.  As we train AI to get better at this, we need to know we are part of the problem and we are doing this willingly.

  2. 7 deadly sins all available by subscription -There are a few framing perspectives I’ve really enjoyed over the years. Kara Swisher’s comment about Silicon Valley tech scene as “assisted living for millennials” the quip about 2010s tech “building technology to automate your mother” and now we have reached peak enshittification and full WAll.E living taking all our vices and serving them up on demand.  The understanding that humans create the conditions of our own destruction is not new but the ability to do this so effectively seems to be worth taking note.

  3. Unbelievable Revenue per Employee in healthcare - prescription kickbacks (rebates)

Fun and Funny

Lifehacks - AI and Others

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** Typos and poor grammar either because I'm too lazy to edit properly, or to make you feel confident this was written by a flawed human and not AI generated.

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