James Ramey

Managing director at Middleland Capital - VTC Ventures: Early stage venture fund focused on healthcare, food & ag, deep/hard tech, defense. Recovering lawyer.

[James Ramey @jramey000]

Firmly believe this and try my hardest to practice it 👇

[Jack Altman @jaltma]

Keith's answer to "how do VCs actually add value?" mapped basically exactly to my experience at Lattice.

There might be a bit of value from platform teams, recruiting, etc. but the vast majority just comes from the quality of the person who's in it with you year after year.

[James Ramey @jramey000]

Bumping for reach.

[Nichole Wischoff @NWischoff]

Team of 4 and counting. Actively hiring for investors in SF who want to be on a Partner track. You will have exposure to every single part of firm building from day one, making investments, managing the portfolio and more. Reach out.

[James Ramey @jramey000]

@samtrevlicrow @NUCLRGOLF Same

[James Ramey @jramey000]

@BoringBiz_ Totally agree.

[James Ramey @jramey000]

@AlexFinn Great take: . This is the beginning of the wealth gap expansion. Those that can afford to spend $10,000 a month on Fable 5 will build incredible products that eat up more and more of the economy. Those that can’t afford Fable 5 will have an insane disadvantage

[James Ramey @jramey000]

Rebounding my own post...I tried to use @claudeai Fable to help me think through a few potential investments (all life science) and it reverted to Opus 4.8 🤦‍♂️

[James Ramey @jramey000]

I'll be sad when I stop being excited about new frontier model launches.

I almost never use @claudeai Opus due to token limits, yet I'm legitimately excited about Mythos, even though I'm highly likely to not use it 😂🤦‍♂️

[James Ramey @jramey000]

I'll be sad when I stop being excited about new frontier model launches.

I almost never use @claudeai Opus due to token limits, yet I'm legitimately excited about Mythos, even though I'm highly likely to not use it 😂🤦‍♂️

[James Ramey @jramey000]

Great post. Everyone operating in health tech should read this.

[Joshua Liu @joshuapliu]

It turned out big academic medical centers could NOT actually be “kingmakers” for startups - but Epic actually could, as we saw with Abridge. What does that tell us about adoption of Health Tech Innovation?

I remember when we first started @SeamlessMD, we made the wrong assumption that brand name academic medical centers could give you a halo effect. That if you could show famous hospital X was a customer, everyone else would follow.

So when we finally got a couple of big brands as adopters, I thought everything would be easy - but I was wrong. No one actually cared. No one bought our product just because some famous brand did. And every year another 100 startups make this mistake (and also some famous hospitals keep perpetuating this myth… but oh well).

What’s interesting is that it turned out there was a way to create a halo effect for Health Tech… but it came from the EHR, NOT health systems themselves.

Folks might remember that Abridge and Nuance/DAX were the only two AI scribe partners in Epic’s Workshop - which meant Epic was co-developing new Technologies together. Which enabled earlier access to new APIs and integrations with Epic.

This allowed Abridge and Nuance to completely dominate the US health system market for AI scribes for a couple of years. I know this mattered because I have many instances of CMIOs/CIOs telling me they only seriously considered Abridge and Nuance for this very reason. Even though other AI scribes had also integrated with Epic, this Epic Workshop designation created a perception that Abridge/Nuance had access to better integrations already or in the future. So of course this gave the impression that Abridge/Nuance were better in some way - why else would only those two be in the Workshop category?

I even remember a CMIO telling me that they had picked a different AI scribe vendor after a structured, multistakeholder evaluation… only to be overruled by the health system Board because Abridge had that halo effect.

This is a perfect example of the old adage “great distribution beats great product”. This is not to say Abridge and Nuance didn’t have the best products (maybe they did), but that doesn’t matter as much as having the best distribution - which the Epic Workshop status certainly helped provide.

However the lesson of this story isn’t that you need to convince Epic to create a new Workshop category to kingmake you - that’s an outlier event.

The bigger lesson is that nearly every great Health Tech innovation also needs great distribution and go-to-market to succeed. And that in the health system IT space, the perception of “who plugs in best to our core platforms like the EHR” often has far more influence on adoption than features, evidence or social proof.

Too often Health Tech innovators focus too much on the product and not enough on distribution - if this is you, consider this your wake up call.

[James Ramey @jramey000]

@credistick Good stuff as always.

One question - is it really common for multi-stage firms to lead multiple rounds in their own companies and mark their prior investments to the later price they set?

[Gokul Rajaram @gokulr]

AI Is in its 1997 era: Presume Radical Uncertaintiy

@benedictevans on @lennysan's Podcast, May 31, 2026

Benedict Evans, former a16z analyst-in-residence and now an independent tech researcher with a six-year track record of widely-read annual presentations, sat down with Lenny Rachitsky to argue that AI is exactly as big a deal as the internet or mobile - no more, no less - and that this comparison is the most useful frame for thinking about jobs, value capture, and what to do next. His core posture is "presume radical uncertainty," and his bottom line for everyone worried about being replaced is to stop hiding from the technology and start using it.

1. AI is as big as the internet or mobile, and only as big as the internet or mobile. Evans calls this his most controversial opinion. People in tech who think it's bigger - the industrial revolution, the singularity - are doing themselves no favors. People outside tech who think it's smaller are doing the same. Both are wrong in opposite directions, and arguing over whether it's 20% bigger or 100% bigger than the internet is a waste of breath.

2. We are in 1997, not 2007. Most things don't work yet. Most things people will eventually build haven't been built. Anyone telling you they know how this plays out is selling you their cluster of Mac Minis. The honest version of an 80-slide deck on AI is 80 slides saying "we don't know."

3. The job apocalypse is mostly fanfic. Every technology shift in 200 years has automated jobs and unlocked new ones we couldn't name in advance. The new job sounds dumb in retrospect: railway engineer in 1820, web designer in 1985. Even the AI labs themselves - the companies most positioned to fire everyone tomorrow - are adding headcount, not cutting it. Evans calls the people predicting blanket layoffs "morons."

4. The McKinsey test exposes the flaw in "X% of jobs will be automated." If Claude can produce a 75-slide deck, does that replace the consultant? No - because you weren't paying for the deck. You were paying for someone to walk through your company, talk to your customers, and figure out what the politics actually are. Same with the lawyer, the accountant, the engineer. The visible task is rarely the actual job.

5. Foundation model companies probably don't have lasting pricing power. There are no network effects between models. There's no radical differentiation users can feel. There are at least three serious competitors. That math has one answer: commodity. Evans expects models to end up looking more like AWS than like Windows - infrastructure you don't pick, layered under apps you do.

6. The value moves up the stack, again. Telecom revenue is a trillion dollars a year and the stocks have gone nowhere in 25 years, while the things built on top of mobile networks made trillion-dollar companies. The same pattern is likely for foundation models: huge revenue, thin margins, all the interesting wealth created by the people building on top.

7. When the product becomes commodity, distribution becomes the moat. Google and Meta are already spraying AI across every surface they own. OpenAI's "shipmas" sprint was an attempt to build a flywheel before that happened. Apple - whose 2024 vision of personal on-device AI was the most compelling demo of the year - is the last shoe to drop.

8. The anti-AI backlash is a fuzzy mess of real and unreal grievances. Some are true (electricity bills going up in specific places). Some are nonsense (data centers use 0.017% of US water). Some are an artist class watching the floor drop out on illustration commissions. Most discourse conflates them, which means none of them get addressed properly.

9. You can't predict which jobs are exposed. In 1997, the obvious safe job was taxi driver - what does the internet have to do with hailing a cab? Uber answered that. Today the supposedly safe jobs include personal trainer. Prop your phone on a rack, point the camera at yourself, ask AI to coach your form. Maybe that doesn't work. The point is you couldn't have predicted it.

10. Software engineers thought their job was the hardest to automate. It turned out to be the most transformed. Evans's read: engineers didn't realize that most of what they did was boring manual labor that could be automated. They thought it was creative work. The lesson generalizes - your job is probably not the thing you think it is.

11. The only useful response is to dive in. Going on Bluesky to shout about how evil AI is gives you a great feeling of moral superiority and accomplishes nothing. Walking into a law firm interview and saying "I think AI is bullshit and I'll never use it" is not the move. Submerge yourself in it. Come out the other side knowing what it can and cannot do. That's the only career insurance available.

12. AI corner - what Evans actually uses it for. Proofreading. Generating images for apartment redecorating ("here's a picture of this room, repaint it, add this rug, change the rug color"). Voice dictation that auto-transcribes to text. The general pattern: AI is good at the stuff computers used to be bad at, and bad at the stuff computers were good at. The boring precise retrieval tasks he most wants automated are still the things it does worst.

[James Ramey @jramey000]

@NUCLRGOLF Did it for the first time a couple days ago. Key advice: Just keep playing.

Second advice: keep the ball on the fairway as much as possible.

[James Ramey @jramey000]

@carsandgolf Nice!

51-46 for my first sub-100 game yesterday! Still riding that high 😂

[James Ramey @jramey000]

Had the full cycle this week. I was so bad with the driver I didn’t use it the first two holes yesterday, but I was doing bad with my fairway wood too, so I switched back to driver and played the best game of my life.

Heading back out this morning, I’m sure I’ll be spraying it at random with the driver 🤦‍♂️

[James Ramey @jramey000]

@BigSwingTempo Tried this today. Didn’t do it every hole, but tried to do it as much as possible. Broke 100 for the first time ever.

[James Ramey @jramey000]

@BigSwingTempo Can confirm.

[James Ramey @jramey000]

@mvernal @TownAI Looks really cool. What is the ETA on @Office integration?

[James Ramey @jramey000]

@chamath What is a safe rate to assume the price of inference drops to over the next 5 years vs today?

[James Ramey @jramey000]

It is always worth reading @wolfejosh’s LP letters. 💯

[Josh Wolfe @wolfejosh]

1/ Our Q1 ‘26 Lux LP letter is about two forces hiding in plain sight: ASYMMETRY + ENTROPY

they govern markets, militaries, machines + more

the central Q: What did you choose to build and protect—and did you understand what it cost? 🧵

[James Ramey @jramey000]

Big news for patients suffering from neurotrophic keratisis and other rare eye diseases & great work by the Tear Solutions team!

https://t.co/ICn5R6nwq5

[James Ramey @jramey000]

@PlayStalgiaX I choose Road Rash.

[James Ramey @jramey000]

@MericaCulture Boone could be a trilogy