VP Federal Tax @TaxFoundation. @StLawrenceU Alum. Arkansan. North Country NY Native. Aspiring aviation nerd. Married to @VictoriaBell. All views my own.
Farmington, Arkansas
Joined September 2011
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Exciting career news: I am stepping into the role of Vice President of Federal Tax Policy @TaxFoundation.
When I joined @TaxFoundation nearly 8 years ago, I saw it as one of the best places to educate taxpayers and make principled, pro-growth tax policy changes a reality. 🧵 1/6
Garrett Watson retweeted
DEBT LIMIT: @SenThomTillis tells me there’s no way he’s voting in lame duck to raise the debt ceiling. He expects there will be an attempt
Garrett Watson retweeted
This is so cool
“okay, so what? it’s just math.”
no, you don't get it.
here’s what OpenAI’s math breakthroughs could eventually help make possible in the real world:
Vlasov–Maxwell → stronger foundations for fusion research, whose ultimate prize is virtually limitless clean energy to power civilization
Calderón’s Problem → portable body scanners using electrical signals, making medical imaging cheaper and easier to access
Maximum-Cardinality Matching → faster searches for compatible kidney swaps across large donor pools, helping hospitals coordinate lifesaving transplants
Inverse Elasticity Problem → scans that map tissue stiffness, helping doctors locate suspicious growths inside the body
Mumford–Shah Conjecture → better tools for spotting tissue changes in brain scans, helping doctors identify signs of disease
Bose–Einstein Condensation → stronger foundations for quantum sensors that could help vehicles navigate without GPS
Simple Stochastic Games → better safety checks for self-driving cars and robots before dangerous mistakes reach the real world
Matrix Multiplication → cheaper AI and bigger scientific simulations using the same computers
Edit Distance → faster DNA comparisons, helping researchers study genetic changes linked to disease
The k-Server Problem → robots that waste less movement and energy, making warehouses more efficient and goods cheaper to move
a reminder that math is the foundation of science.
so accelerating mathematical discovery could compress centuries of scientific progress into years.
insane timeline to be alive for!
Garrett Watson retweeted
The math breakthroughs OpenAI released are (while indirect) a positive update for strong RSI.
Strong RSI is most likely to occur if AI can make conceptual breakthroughs in machine learning theory on par with, or even larger in impact than, the development of the transformer architecture. We haven't seen AI produce anything like that in machine learning so far.
But math and ML share many similarities as domains. (You could even call ML a branch of mathematics.) Both are highly verifiable purely in compute and rely on symbolic manipulation.
AI making true breakthroughs in math (category D in Abhishek's taxonomy below) should lift expectations that it can make conceptual breakthroughs in machine learning as well, which is bullish for RSI.
I remain convinced that we don't have evidence of anything even remotely close to a self-sustaining RSI loop today. But if the ability of AI to conduct ML research advances as quickly as it recently has in formal math, that may well change.
Some further thoughts on the 372 results released by OpenAI today, across 722 manuscripts.
If I were to classify theorems that mathematicians prove and publish according to their groundbreaking nature, I would (very roughly) divide them into four categories:
A) Non-breakthrough results.
This is the overwhelming majority of published mathematics. Such results can range from solid to excellent, and some represent genuine advances in a field. But they are not hugely surprising, and would not normally be described as “breakthroughs.”
B) Exceptional advances within an existing programme.
These are spectacular results, but where there was nonetheless an existing credible route to the theorem, and some expectation that sufficient work would get you there. Completing the programme may require a lot of ingenuity and deep work, but mathematicians would not be shocked that the theorem had finally been proved.
C) Surprising breakthroughs.
These are results that clear a major barrier and substantially change the state of a field. Before the proof there was no convincing roadmap to the full result. Yet, while mathematicians would find the theorem remarkable and surprising, they would not find it completely shocking: if you asked them beforehand if it was plausible such a theorem could be proved today, most would say yes.
Note: The very best mathematicians prove only a small number of results in categories B and C in a lifetime; many mathematicians never prove even one. Such results would normally belong in the very top journals, such as Annals, Inventiones, etc., and there are only a handful of them each year in any given area. Several results of this calibre by a single person would make a very strong case for a Fields Medal.
D) Shock breakthroughs.
These are results that, before their announcement, leading experts would have regarded as *extremely* unlikely to be proved with the current mathematical technology available. So the theorem itself would come as a shock. These are extraordinarily rare, and instant-Fields medal variety.
(There is one further category I have deliberately left out, because I suspect it is empty: a correct proof of a problem for which the overwhelming consensus of top experts, until the proof came, was that a proof was so far beyond existing mathematics that a claimed solution should, on prior grounds alone, be regarded as almost impossible. I would put the Riemann Hypothesis today in that category)
My current impression is that some of OpenAI's announcements today lie in A, but most fall into categories B or C. There is exactly one example in D (the Quasi-Riemann Hypothesis).
It is a very big day for mathematics.
Garrett Watson retweeted
Great observation by @fchollet . Frontier labs are almost doing no pretraining-- probably because pretraining is bottlenecked by data- the world is running out of data. So they focus on reinforcement learning, and there are many domains that may be insufficiently data abundant and too messy/unverifiable for RL.
What if the jagged frontier is mainly math + code (which you can push arbitrarily far with RLVR), and everything else starts to plateau because it is still bottlenecked by human generated data?
Model performance in non-verifiable areas has kept improving steadily, albeit much slower than for math and code. But is that steady improvement a side effect of a higher G (itself driven by RLVR), or only a function of the amount of new human data getting injected into training (which is still continually happening on a massive scale)?
A lot of things depend on the answer to this question
Garrett Watson retweeted
I think the gradually accelerating tempo of technological progress prepares people better for AGI than a lot of people imagine. The median American born in 1986 has lived through the fall of communism, the Internet, e-commerce, search, the ISS, 9/11, social media, the iPhone, the financial crisis, streaming, woke 1.0 and its backlash, COVID, BLM, and now early AI. The median Englishman on the eve of the Industrial Revolution saw virtually no social or technological development at all and so the events of the following decades were completely unprecedented
Garrett Watson retweeted
Americans can afford to purchase an immense quantity of goods and services from people in other countries, since Americans are rich.
However, they find it very expensive to hire each other to do anything since everyone is more or less rich.
This causes a certain confusion.
Garrett Watson retweeted
I'm shocked that eliminating barriers to entry and other regulations led to growth
Garrett Watson retweeted
I mean, the top 1% of Excel users use Excel in wildly different and much, much more intensive ways than the masses of regular office workers or people putzing around on personal spreadsheets. I'd expect that sort of thing to be true with AI as well.
Still much room to grow, tho.
This is my thesis: no one uses AI. I repeat, absolutely no one. We live in a bubble.
Even among my friends who pay for it, when I ask them to open ChatGPT and show me their queries, it’s the same handful of basic things.
Most don’t even know they can upload a photo and ask questions about it. Connecting Gmail so an agent can read and send emails blows their minds. An agent opening a browser and checking them into a flight? They’ve never even heard of it.
The massive challenge right now is adoption, and then getting people who already signed up to actually use what they’re paying for. Most have absolutely zero clue what’s possible.
Imagine the compute shortage when everyone starts using AI like the top 1% of users do today.
Garrett Watson retweeted
Has AI hit the labor market yet? @alexolegimas and my verdict after ~20 papers: not yet in aggregate. Unemployment and layoffs show almost nothing. But AI may already be cutting junior hiring in exposed white-collar jobs. Remote work may explain part of that decline.
A 🧵
Garrett Watson retweeted
Policymakers of all stripes love domestic investment, and expensing has enjoyed bipartisan support (even if Republicans got it into law). The tax code should not play a game of, "Invest in *this* not *that*." Proposals to narrow expensing eligibility makes little policy sense.
When Republicans passed their tax bill year, they knew Dems would hit them for cutting rich people’s taxes, but they never expected a provision titled “Full expensing for certain business property” to blow up in their faces.
story w/ @Robillard
huffpost.com/entry/data-cent…
Garrett Watson retweeted
.@TaxFoundation vice president of federal tax policy Garrett Watson (@GS_Watson) on whether a federal gas tax suspension would really tame rising fuel costs for consumers.
"It's just a very small source of relief compared to the bigger picture problem that we're facing with fuel prices. ... Some portion of that would be captured by producers of oil and gas."
Garrett Watson retweeted
Bottom line: +29k jobs (+50k 3mma) and 4.2% unemployment are hardly catastrophic. Rebound in prime-age e-pop is encouraging.
Fundamental issue in this labor market isn't that there aren't enough jobs. It's what they pay: Wage growth is continuing to slow.
U.S. employers added 29,000 jobs in September and the unemployment rate ticked up to 4.2 percent.
Data: bls.gov/news.release/empsit.…
Full coverage: nytimes.com/live/2026/10/02/…
Garrett Watson retweeted
.@TaxFoundation vice president of federal tax policy Garrett Watson (@GS_Watson) discusses the impact gas tax holiday and commuter deduction proposals would have on fuel prices and consumers.
LIVE: tinyurl.com/2p3y4rt6
How edgy. More defensible arguments can be made for abolishing the death penalty altogether or moving back to extant methods used in the US historically which had lower botch rates than lethal injection.
Garrett Watson retweeted
On some days, progress feels too slow. On others, @TheEconomist is citing Estonia as a glowing example of a good tax system.
"What is the right tax system for the 21st century?"
economist.com/insider/the-in…
Garrett Watson retweeted
Future population projections expect that fertility rates stop declining and roughly stabilize.
This new NBER paper asks what evidence we have to expect this. It finds there is none. "Instead, where and when fertility has fallen low, it has tended to keep falling."
nber.org/papers/w35824
Garrett Watson retweeted
FRI | @TaxFoundation vice president of federal tax policy Garrett Watson (@GS_Watson) discusses the impact gas tax holiday and commuter deduction proposals would have on fuel prices and consumers.
Watch live at 8:00 a.m. ET!
The AI buildout in one chart: Big Tech's profits have become chipmakers' profits.
Approximate free cash flow, from 2022 to today:
- Hyperscalers: $275B → $0
- Semiconductors: $50B → $400B
More charts in State of Markets II: a16z.news/p/state-of-markets…
Introducing our State of Markets pt 2, along with a companion podcast where we unpack the data and discuss what comes next.
Tech is the everything cycle.
Supply: putting the buildout in context, just passed railroads as % of GDP. The wisdom of Elon is real: the factory (or the datacenter!) is the product.
Demand: diffusion is so, so early. Median AI vendor spending in the top 1% of companies is 8x that of the top 10%. Only about 30% of S&P 500 companies report a quantified AI impact, which means there’s a substantial opportunity in connecting models to a company's data and workflows. Diffusion into companies is one of the main themes of the next 5 years.
We’re entering the agent work period. Only a few million users today, but applicable to billions of internet users with massive surplus created. META/GOOG monetize US users at $200+ per year today. Agent opportunity is much higher.
Mega-trends the next 5 years: Consumer agents, Robotics, Autonomy, AI x bio, Personal health, Diffusion into enterprise, New era of American Dynamism.
Much more in our SoM report here - a16z.news/p/state-of-markets…
@a16z @sarahdingwang @aleximm @santiago__rdz
Garrett Watson retweeted
A new paper from @kpomerleau & @AEI finds that Trump's tariffs have increased US firms' cost of capital and act as a significant tax on new US investment - one that offsets the OBBBA's investment tax cut. ☹️
aei.org/research-products/re…
Garrett Watson retweeted
Triple threat of new pieces from the @TaxFoundation federal team this week on:
🏘️ Multifamily housing expensing
⚖️ Investment deferral parity
✅ Top options to simplify the tax code
Links: