QuadSigma (4σ)⚡⟠♠️

Rebuilding new X acct & looking to reconnect w my followers. Tech, Econ 📈 $BTC, $ETH, $AVAX OG. Poker ♦Travel. Econ=Mises. End the Fed 🦔🥩
Vires ad margines.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

Published some ACP material here for improving AVAX tokenomic-architecture alignment:
https://t.co/tSYnnYt5xf

Please take a look and contribute any productive thoughts / challenges to the discussion.
@AvaLabs

As I've mentioned, I have a lot more not-yet-published material (~30 draft pages currently, only a fraction of which overlaps with what is published here). It will form a basis for several ACPs along related lines. I've been working hard to refine those proposals to minimum viable for publication, but each time I think I'm close, it just doesn't feel done. Hopefully soon.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@JustnThePhotog When do we sue?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

The model Labs uses now is structured like Avalanche is a private company and the chain as its asset. They sell the tech directly to L1 institutions, via means external to the token, accruing immense benefit to them directly, while leaving investors (who footed the bill for the growth and development and marketing of the network) footing the bill. Investment occurred when the model was for a fixed 2000 AVAX fee per validator, etc. The terms changed underneath us, without any corresponding consideration of value accrual and investor impact. They just started syphoning the growth directly. A "Labs to Instutions" BD pipeline that allowed them to negotiate the terms extraneously to any linkages to the token. Origianl tokenomics linked gwoth to the token. Then we were rugged, with no recourse. AVAX had plunged ever since. And if they were transparent about what they are siphoning off by this dirext relationshsipnwith the institutional L1 (payments, consult frees, etc.), that would be one thing. But they are not.

There needs to be a legal demand made to disclose Labs' revenue streams. We've made this call politely and informally, as a community, but it is always ignored. Tone deaf. When does it become not polite or informal anymore? When does it get teeth?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

At what point does legal class action recourse become an option. $AVAX was offered and presented as an investment in the primary means of value accrual of the Avalanche network. However, that is being actively bypassed, with almost 100% of value accruing to insiders via direct revenue to AvaLabs via direct engagement/cash fees with L1s. At what point is there a fiduciary or negligent action standard that has has been crossed in the sale of the token and subsequent management of it, to the detriment of investors who had a reasonable expectation of accrual linkage and equity in network activity vs, direct lab revenues from these business deals that bypass investors. At what point should these de-linked L1s be considered spin-offs that mandate value distributed back to the investors in the main chain. At what point is this considered a bait and switch or ponzi? Eminem and the entire organization has been negligently silent and unfocused on this. At what point is a network with 1000s % growth, but with an asset down 97% considered fraud for the implied or implicit claims made about its token and it's linkages to value of the network. At what point does at least a basic, common sense level of fiduciary or basic ethical standard get applied. When is legal recourse our only option to have the networks success shared by its investors instead of Ava Labs hoarding it all via direct accrual to them and not the token?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

The model Labs uses now is structured like Avalanche is a private company and the chain as its asset. They sell the tech directly to L1 institutions, via means external to the token, accruing immense benefit to them directly, while leaving investors (who footed the bill for the growth and development and marketing of the network) footing the bill. Investment occurred when the model was for a fixed 2000 AVAX fee per validator, etc. The terms changed underneath us, without any corresponding consideration of value accrual and investor impact. They just started syphoning the growth directly. A "Labs to Instutions" BD pipeline that allowed them to negotiate the terms extraneously to any linkages to the token. Origianl tokenomics linked gwoth to the token. Then we were rugged, with no recourse. AVAX had plunged ever since. And if they were transparent about what they are siphoning off by this dirext relationshsipnwith the institutional L1 (payments, consult frees, etc.), that would be one thing. But they are not.

There needs to be a legal demand made to disclose Labs' revenue streams. We've made this call politely and informally, as a community, but it is always ignored. Tone deaf. When does it become not polite or informal anymore? When does it get teeth?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

At what point does legal class action recourse become an option. $AVAX was offered and presented as an investment in the primary means of value accrual of the Avalanche network. However, that is being actively bypassed, with almost 100% of value accruing to insiders via direct revenue to AvaLabs via direct engagement/cash fees with L1s. At what point is there a fiduciary or negligent action standard that has has been crossed in the sale of the token and subsequent management of it, to the detriment of investors who had a reasonable expectation of accrual linkage and equity in network activity vs, direct lab revenues from these business deals that bypass investors. At what point should these de-linked L1s be considered spin-offs that mandate value distributed back to the investors in the main chain. At what point is this considered a bait and switch or ponzi? Eminem and the entire organization has been negligently silent and unfocused on this. At what point is a network with 1000s % growth, but with an asset down 97% considered fraud for the implied or implicit claims made about its token and it's linkages to value of the network. At what point does at least a basic, common sense level of fiduciary or basic ethical standard get applied. When is legal recourse our only option to have the networks success shared by its investors instead of Ava Labs hoarding it all via direct accrual to them and not the token?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@el33th4xor @timcryptocon

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@el33th4xor @timcryptocon Awesome. $AVAX must be going through the roof! ...Oh, wait.

Everyone who believed this was possible and invested early to help make this happen has been raked over the coals. When do stories like this translate to the people who put up the capital being treated with decency?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

I take a strange sense of pride in being able to live and work indefinitely out of a "personal item size" backpack while traveling. I've got it down to an art form. I know what shape to form the vacuum seal roll bags, etc.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@wsop, table #550 (White) desperately needs some WD-40 or to be taken behind the barn and decommissioned. Maintenance was notified 3 days ago, but it's been tilting every table nearby since.
@kevmath, can you pull some strings?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

Feeling those @WSOP kick-off vibes! On my way soon to do battle. This will be my 18th consecutive year at WSOP, which is hard to believe!

I spent time building a poker tournament training app. A byproduct of this effort has been a lot of practice and deep diving while doing a lot of dev and app tuning, modelling (tourney stage dynamics and inflection timing, player/dynamic-type exploitative and GTO), training, testing, scenario creation, cross-checking AI, heuristics building, etc. "Accidentally" put in a lot of practice this way.

Feeling prepared and confident. Less final 2 and final 3 table "almosts" and more final tables and bracelet(s). Lfg!!!

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

The problem here is that bad engineers who don't understand the principles of engineering or the *properties* of what they are engineering (and only understand building features) will have no idea of how to use AI efficiently to produce working systems, so they'll throw compute at the problem. They end up spending 10% of their token budget on coding features haphazardly, then 90% chasing ghosts, bloating context, and doing full-scan refactors incessantly, and STILL not requesting or asking about the right things needed.

I intentionally keep my agentic dev budget-constrained to force actual and efficient engineering and it's paying off... JIT injection, LLM-efficent prompt context constructs, routing to best-fit models, structured design... My platform does not just throw everything to a frontier model, and it forces actual engineering considerations upfront in-process. The workflow matters. The engineering mindset matters.

[Ricardo @Ric_RTP]

Microsoft just banned its own engineers from using AI.

The tool was literally costing MORE than the humans it was supposed to replace.

They lied to you about AI adoption and now the whole narrative is blowing up:

Microsoft gave thousands of engineers access to Claude Code six months ago and encouraged them to use it.

Engineers loved it and adoption exploded. But then the invoices arrived.

Token-based pricing means every query, every code review, every debugging session costs money. At scale across 100,000 engineers, the numbers became so large that Microsoft issued an internal order to cancel nearly all Claude Code licenses by end of June and force everyone onto their own cheaper tool instead.

The company that invested $5 billion in Anthropic just told its own people to stop using Anthropic's product because it costs too much.

Uber's story is even worse...

Their CTO Praveen Neppalli Naga told The Information that the budget he planned for the full year was "blown away already" by April.

Uber had rolled out Claude Code in December 2025. By March, 84% of their 5,000 engineers were using it with 70% of all committed code coming from AI systems.

Heavy users were burning $500 to $2,000 per month each. Naga himself spent $1,200 in a single two-hour demo session.

The company had even built internal leaderboards ranking engineers by how much AI they used. They literally gamified the spending and then ran out of money.

Now look at what Nvidia's own VP of applied deep learning Bryan Catanzaro said to Axios last month. Direct quote:

"For my team, the cost of compute is far beyond the costs of the employees."

This is a VP at the company that SELLS the chips saying that using AI is more expensive than paying humans.

Think about what this means for the entire AI narrative.

Every CEO on every earnings call for the past two years has said the same thing:

AI will make us more efficient, reduce headcount, and cut costs.

The stock market rewarded every company that said it.

Fired workers, stock goes up. Announced AI adoption, stock goes up.

But the actual companies deploying AI at scale are discovering the math doesn't work. The MORE employees use AI, the HIGHER the bill.

Goldman Sachs forecasts a 24x increase in token consumption by 2030 as companies adopt AI agents. Gartner just published a report showing that even though individual token prices will drop 90% by 2030, total enterprise AI costs will go UP because agents consume exponentially more tokens per task than basic tools.

Meta built an internal dashboard called "Claudeonomics" to track which employees use the most AI. Amazon started pushing engineers to "tokenmaxx," their internal term for consuming as many AI tokens as possible.

Both companies are spending hundreds of billions on AI infrastructure this year alone.

And Microsoft, the company that bet its entire future on AI, just told 100,000 engineers to stop using the tool they liked best because the per-token bills got out of control.

The companies building AI are telling investors it saves money. The companies using AI are finding out it costs more than the humans it was supposed to replace. And even the company that makes the chips just admitted it through its own VP.

This is the gap nobody on Wall Street is pricing in.

$725 billion in AI infrastructure spending this year across Big Tech. And the first companies to actually deploy these tools at scale are already pulling back because the economics don't work.

What do you think?

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@RebaseMan Esp. while OG AVAX native, BenQi, is winding down the ecosystem (sub-)markets and native yields are pretty dead.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@chooserich Kick its ass

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

Outside of some basic due diligence, my thesis on $TWLO was fairly straightforward:

It's user base was about to explode in parallel with agenetic development, vibe coding, and personal agents... smaller scale players who would have never heard of Twilio prior, but now need a simple bridge between agents and their humans. This is playing out and was evident in their most recent earnings.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@JustnThePhotog @lunchwithbashir

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

Agree with this take. Formal models /verifications / proofs (and software intentionally produced with structures to take advantage of it) will see accelerated adoption and eventually dominate.

While creative artistry/craftsmanship will always have an apex role at the frontier, we are exiting the artisan-dominated phase of software production as an industry. Formal model specs and rigor of provable precision will dominate the mass production aspects of software from here on out.

[vitalik.eth @VitalikButerin]

Many people have claimed that with AI-assisted bug finding, secure code (and hence trustless anything) will be impossible.

I have a much more optimistic take, and AI-assisted formal verification is a major part of the reason why:

https://t.co/0ceMBZ6uqj

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@RebaseMan This is what happens when Marketing designs the product instead of Engineers. Maybe the suits decided to "replace" the engineers with Copilot subscriptions for the biz school grads.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

Suggested tweak to this. Keep the top prizes per tourney, but ...
1. Tie some bonus pay to the ones who rate outside of the top 3, but consistently get high marks... both per tourney and over the course of the series: top 5%, 10%, 25%, even top 50% (gives a bottom 30% dealerer something to strive for).

2. Bottom 10% dealers (20% early in series, 5% late) identified and given training and chance to improve. Persistent bottom 5% performance + rating below threshold + without improvement after re-training = prob not a good fit for this position.

3. "Top 3 only" may encourage "hamming it up" by some dealers (try to add flair/personality vs. dealing)... Maybe have a separate category "overall" vs. "excellent dealer skill" (speed, accuracy, table/situation control, knowledge). Make sure to reward the plain, all-business good dealers, not just the hams.

4. Instead of top 3 per tourney, make it the top 1%. Split mostly equally. This better accounts for incentivizing dealers in large fields. 25K-person events should have proportionate award distro to a 500-person event.

5. Bonus: one-time dealer skills competition for prizes. Public can watch. Top rated dealers eligible. Fastest pot count, fastest accurate deal, shuffle, fastest 3-way chop with side pot.

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@WSOP Great improvement. But a little credit, maybe? 🤔
https://t.co/qawAdSMhWn

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@WSOP Great improvement. But a little credit, maybe? 🤔
https://t.co/qawAdSMhWn

[QuadSigma (4σ)⚡⟠♠️ @QuadSigmaX]

@rumnchess @WSOP 6. Basic dealer skills - allow players to rate dealers at their table via WSOP+ app. Allow low scoring dealers the opportunity to improve via access to daily instructor-led training/practice sessions. (At some point, some would need to be let go if they don't show improvement).