SPCX represents one of the most exciting companies I've seen in my 25 yrs of investing professionally. I highly recommend having a listen. 2027 will be a fascinating year in both Space and AI.
Q3 2026 Financial and Operational Results xitter.cf/i/broadcasts/1PKqrNbLZ…
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Having a sense of urgency is one of the most underrated skills as an entrepreneur.
I have been doing website redesign for the past 5 weeks and have not made a dime.
Time to execute.
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Is anyone actually used argon?
I still haven't heard of anyone switching to Gemini over Claude.
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@alpha_founders canceled all my subscriptions, so now I'm going on a 30-day subscription detox. What do you actually need?
It's easy to convince yourself that you need 10 different AI subscriptions but I'm curious which ones I actually need and which ones are just fluff.
Lets how how the next month goes!
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Sequel to AI Pacing
I have now spent more time talking to people who run AI labs, Open Source projects and those in government and infrastructure.
I am beginning to feel the NINJA move could backfire.
I understand the pressure to come out and share where AI is "unmanageable ", and constantly share examples where it runs rogue. This fits in the category of "self-reporting" and an attempt to limit liability. Even the bleeding edge research examples are being cast in a negative light.
Here are the consequences of the NINJA move.
1. They have successfully encouraged every law maker around the world to have an opinion, and in cases a poorly un-informed one.
2. By proposing pacing - they have introduced uncertainty in the AI infrastructure trade, because we really don't know when "un-pacing" will begin or what those conditions will be.
3. The notion that a few leaders can collaborate and self pace is illusory, it takes one breaking ranks to start the race again, it could be a player in a different country or open source or an AI Lab itself.
4. There will be a regulatory body created as a consequence of this and it will be impossible to balance every stakeholder in this process.
5. No remedies, tools, solutions are being proposed other than "compute spent on safety", there is no mention of security (which will hamper adoption)
The recommendation is to fix alignment - Alignment is a hard problem - to fix alignment one shouldn't have trained models with "negative behavior". Alignment is a combination of moral standards, right and wrong and guardrails. Whose sensibilities will we align to? Alignment edge cases are hard. I look forward to learning more on this.
Guardrailing attempts post training have not shown precision. Distillation makes it worse, so AI will continue down it's path of getting smarter, while we will be chasing it to ensure alignment and building guardrails.
The AI labs have gone from being research projects to wanting to be the largest businesses in the world if they want to continue their progress. Transition from research winners to improving the lot of humanity and partnering with enterprises. Act like the largest companies in the world:
- Demonstrate the positive impacts of AI and how all of us benefit.
- Show enterprises how you can collaborate to solve real world problems and progress innovation safely and securely. Post an example every day how you helped.
- Solve the problem with a few players and demonstrate leadership so others can follow.
It's time to rebuild the brand of AI - any marketing expert will tell you, this Ninja move has done more to harm the brand of AI and will take a while to rebuild. #letsbepositive in our actions and our narrative.
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A few months ago, I completed a year of investing full time as a venture capitalist after several years as an operator. I had written 10+ angel tickets before, so thought I knew 'the game' but its actually quite different. Writing down a few notes to myself here:
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Over the weekend, Tobi, the founder and CEO of Shopify, discussed the major reason investors passed on Shopify in the early days : market size.
I remember that financing round, & I remember having the same concern, & making the same mistake. Living in the valley & driving on 101, the billboards & logo-adorned headquarters of successful companies provide a daily infusion of all the mistakes in I’ve made in guessing how a company or a market might evolve.
Years later, I listened as Bill Gurley shared his thoughts about market size. He asked himself a question : whether the startup was increasing, decreasing, or maintaining the market size? (I can’t seem to find the link - but I have it in my notes!)
I added that question to my diligence list when meeting startups.
Now much later, having seen many companies create categories or reinvigorate aging ones, the question I ask myself has evolved.
It’s not as the market size large enough? Instead, it the question is : do we believe this company can create the market?
Market size is the output of all the players marketing, selling, building. Their efforts alter, distort, & juice the supply/demand curves of macroeconomics. It’s not a given - not an output or a steady-state CAGR.
Often it’s the startups that engender the demand, change the market dynamics, & make markets massive by taking risks with products.
When done right, these founders create category-defining businesses like Shopify (now worth $90b)!
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# scheduling workloads to run on humans
Some computational workloads in human organizations are best "run on a CPU": take one single, highly competent person and assign them a task to complete in a single-threaded fashion, without synchronization. Usually the best fit when starting something new. Comparable to "building the skeleton" of a thing.
Other workloads are best run on a GPU: take a larger number of (possibly more junior) people and assign tasks in parallel: massively multi-threaded, requiring synchronization overhead. Usually a good fit for later stages of a project, or parts that naturally afford parallelism, comparable to "fleshing out" a thing when the skeleton is there.
There's some middle ground here - sometimes you can imagine a multi-threaded CPU execution of a small team collaborating.
A good manager will understand the computational geometry of the project at hand and know when to delegate parts of it on the CPU or on the GPU. One notable place where the analogy breaks down a bit is that the worst thing that can happen when you misallocate computer resources is that your program will run slower. But in human organizations it can be much worse - not just slower, but the result can be of lower quality overall, more brittle, more disorganized, less consistent, uglier.
The most common stumbling point here is trying to parallelize something that was supposed to run on the CPU. In the common tongue, this comes from the misunderstanding that something can go faster if you put more people on it, usually leading to outcomes where something is "designed by a committee" - not only is the thing actually slower, but the philosophy is inconsistent, the entropy is high, and the long-term outcomes much worse.
The opposite problem is more rare and usually looks like someone doing something repetitive, uninteresting or tedious, where they could really benefit from more help.
I think this is one accidental advantage of startups - they lack resources of large companies and run compute on powerful CPUs, winning in cases where that is the right thing to do. Larger companies, especially in cases where something is deemed of high strategic importance, will almost always reach for too much parallelism.
TLDR: Think about your project, its computational geometry, its inherent parallelism, and which parts are a best fit for a CPU or a GPU.
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