Healthcare strategy pro | Exploring innovation & startups | Sharing insights on #HealthTech & #Entrepreneurship
Joined February 2021
- Tweets607
- Following208
- Followers31
- Likes148
1/
My dry cleaner tracks loyalty by scribbling a dollar amount on the back of a business card with a ballpoint pen. No app. No points dashboard.
No 8pm “we miss you” text from a CRM that thinks we’re friends.
And it still beats every coffee shop loyalty app i never use.
2
1
77
5/
Sometimes the “sophisticated” solution is just more expensive and worse.
The dry cleaner isn’t doing a psych study.
They’re doing the cheap, low-tech thing that actually works while the rest of us are busy building loyalty apps nobody opens.
1
10
Henry Fire retweeted
I’m not sure whether Dean Ball is confessing to a regulatory capture strategy or simply predicting this will happen (he now says the latter). Either way, the weaponization of regulatory uncertainty as a competitive tool should be completely unacceptable.
He argues there’s no need to ban Chinese open-source models — just direct agencies to issue soft-law warnings that create enough FUD so regulated enterprises back off. “It needn’t be that well justified.”
Wrong. Regulatory decisions should always be well justified and grounded in facts, logic, and evidence, not the deliberate exploitation of fear and uncertainty. Implementing a surreptitious policy through manufactured doubt — rather than strong and explicit justification — corrodes the rule of law and invites future abuse against anyone.
We are at a critical inflection point in AI policy. The leading closed labs, already a duopoly in terms of AI model revenue, want the government to eliminate their open source competition. They have laid their cards on the table. It is time for the rest of Silicon Valley — the vast majority that still values open competition — to do the same.
Some observations on Kimi:
1. It's a very good model! I don't think its performance can be explained away by distillation or anything like that. In agentic coding sessions, it seems pretty much on par with the best public models of Q1 2026. In my fairly limited use, it also seemed very token hungry. It's not obvious to me that this model is actually that cheap to run.
2. I am personally surprised the Chinese state continues to allow the open sourcing of models this good, given potential risks. To be clear, I *myself* might be fine with models presenting this level of marginal risk being open weight, but I am surprised that China is fine with it. I suspect the reason they are is 75% explained by strategic blindness/lack of AGI-pilledness (the CCP is very Yann Lecun-y in its views of AI). The other 25% or so is their lack of compute for customer inference (making China's open-weight strategy an unintended byproduct of US export controls) and the normal Chinese strategy of aggressive exports. For the companies, as opposed to the government, the decision to open source is partially ideological and partially because they are behind, and they know that very few people would pay for sub-frontier models from China.
3. Open-weight models are inherently decelerationist, and I'm continually surprised to see the so-called "accelerationists" so excited about open-weight models. I suspect the reason they are is that they know open-weight models are effectively ungovernable, and they simply like the overall cloak of ungovernability open-weight models create over the whole of AI. It's not a bad strategy; it reminds me of James Scott's recounting of the hill people in "the art of not being governed." Still, in the end, open-weight models deter further AI capex.
4. One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a "public good" which will ultimately be provided by the state as a kind of "digital public infrastructure." This future strikes me as a dystopian hellscape, but I've never met an open-weight models advocate who doesn't ultimately concede this is where things end. You'd be surprised how many 'accelerationists' lobbied me, while I was in government, to support an eleven or twelve-figure federally funded data center so that startups could train models at a subsidy and then give them away for free. There was no other way for AI to progress, they said. Perhaps this is the logical end state of things. Nonetheless, I find myself surprised to see supposed accelerationists excited about such an outcome. I think many of them just don't know what they're doing. Many accelerationists do not view the creation and serving of frontier models as a legitimate business.
5. I would guess that the Trump Administration will at some point realize that their best strategy here would be to create large amounts of regulatory risk around the use of open-weight Chinese models. You don't need to "ban open source" (one of the dumber motifs of AI policy discussion). You just need to direct every agency to issue soft law that creates FUD. "A Federal Reserve Advisory Bulletin found that there may be backdoors in Chinese AI models." It needn't be that well justified. You just create enough regulatory risk that every regulated enterprise backs off. You probably don't want to create so much regulatory risk that you scare off the hyperscalers from serving Chinese models; this will just drive startups to sketchier providers. There's a happy middle ground here. I'd assume they will do some version of this.
6. It's probably true that open-weight models of this capability make the world a bit more dangerous, but not so much more that you'll really notice. At some point the models will be capable enough that you will notice. "A nonliving, invisible, dangerous, and infinitely self-replicating agent escaped from a Chinese lab," you say? Color me shocked.
AI is proving real ROI in healthcare! CommonSpirit hit $100M+ value, Mount Sinai projecting $50M with 3:1 returns, and more systems scaling ambient AI + predictive tools. Opportunities are absolutely real. Great read from Becker’s: beckershospitalreview.com/he…
@BeckersHR @HealthITNews
#AIinHealthcare #HealthIT #DigitalHealth
187
Exactly right on incentives, @HealthcareAIGuy. AI just supercharges what the system rewards.
The third-party payer model + ACA MLR rules (80-85% of premiums on ‘medical’ costs) create a powerful loop: patients are price-insensitive, providers use AI coding tools for higher complexity/billing (as PwC showed), and insurers grow absolute profits by scaling premiums while routing more spend internally via owned PBMs, pharmacies, and providers.
‘Show me the incentive…’ Until we shift to value-based payment rewarding lower total costs and better outcomes, AI will mainly make the extraction machine more efficient.
PwC projects medical costs will rise 9% in 2027, the largest increase in 17 years, with US healthcare spending on track to exceed $9T annually by 2035.
One surprising contributor: AI-powered documentation and coding tools.
~70% of health plans surveyed ranked AI-enabled revenue optimization among their top cost inflators. Providers are using these tools to capture more billing complexity and higher reimbursement levels.
It is a useful reminder that AI does not change incentives. It amplifies them.
And today, healthcare incentives still reward doing more, coding more, and getting paid more.
Over the long term, AI should absolutely improve outcomes, reduce administrative burden, and make care more proactive. But in the short term, it could make the existing system more efficient at extracting revenue.
New payment models that reward lower costs and better outcomes could be the big unlock vs just better AI.
"Show me the incentive & I'll show you the outcome" - Charlie Munger
179
How do I change my twitter experience? Ideally I’d never see streamer content again. Similarly probably good on random cop videos. Business, sports, ai would be my preferred tailored algorithm.
The False Claims Act allows the government to collect up to $27,894 per false claim, plus treble damages on actual losses.
644,349 individual claim lines × $27,894 = $17.97 billion in maximum statutory penalties.
Plus treble damages on $59.6M in actual losses.
Courts apply proportionality — no one expects $18 billion. But even 1% of the statutory maximum is $180 million.
1
2
151
Sources:
- CMS Medicare Part B 2016–2023
- CMS Part D Prescribers 2016–2023
- OIG LEIE
- CMS Open Payments 2017–2022
- PACER (*USA v. Miranda*, 5:13-cr-01406 S.D. Tex.)
- TX Medical Board public license lookup.
All public. All verifiable.
@OIGatHHS @TheJusticeDept @TexasTribune @propublica @zerohedge
1
3
151
There's more.
Two Houston office buildings. 152 separate Medicaid billing entities. $148 million in Medicaid claims.
One provider billed attendant care at a volume implying 892 hours of personal care delivered per day.
By one person.
1
11
