Pablo Salonio Ph.D.

International Relations - Naval Systems - Former Captain Tall Ship LIBERTAD - Technology, AI & Blockchain enthusiast

[Pablo Salonio Ph.D. @pablosalonio]

@JOBhakdi Only if he does with supervising attitude. A passenger , as such, delegates responsibility to the driver . Not so?

[Pablo Salonio Ph.D. @pablosalonio]

@karlmehta Agreed. A controlled model is the imperative part. Eventually, each company will adjust to its best possible solution

[Pablo Salonio Ph.D. @pablosalonio]

@MsMelChen Agreed with Dr. Balakrishnan

[Pablo Salonio Ph.D. @pablosalonio]

@sijlalhussain @Nicochan33 Capability divide, that always existed will be reinforced by AI that is capability driven

[Pablo Salonio Ph.D. @pablosalonio]

@Yoshua_Bengio @m_angelmendez @elconfidencial Agreed, probably it’s not a question of time but a new attitude to incorporate from now on. As humans , AI and its challenges will continue to evolve. Thank you for your interesting posts.

[Pablo Salonio Ph.D. @pablosalonio]

Summarize https://t.co/UqRwIA94Oa

[Pablo Salonio Ph.D. @pablosalonio]

@GerardoMunck Thank you for sharing the post Gerardo.

[Pablo Salonio Ph.D. @pablosalonio]

@rohanpaul_ai Define conciousness

[Pablo Salonio Ph.D. @pablosalonio]

@vitrupo Consciousness is too broad a concept to discuss without first defining our terms. As always, Bengio’s perspective provides a valuable starting point.

[Pablo Salonio Ph.D. @pablosalonio]

@HyperTechInvest All in with SpaceX

[Pablo Salonio Ph.D. @pablosalonio]

@IntuitMachine Clarifying, simply put and useful.Thank you for posting Carlos.

[Pablo Salonio Ph.D. @pablosalonio]

@IntuitMachine Spot on. Beyond this $GOOG example… DeepSeek and others relate to a clear ongoing strategy

[Pablo Salonio Ph.D. @pablosalonio]

Recommend to all interested in AI geopolitics

[🇦🇺Craig Tindale @ctindale]

https://t.co/vWLN07swYq

[Pablo Salonio Ph.D. @pablosalonio]

@antgrasso @McKinsey Yes. And conceptually would consider in leadership “ orchestrators and governance of hybrid intelligence”. Thank you for posting.

[Pablo Salonio Ph.D. @pablosalonio]

@Fuel_YourGrowth Yes, very useful. If I may add communication quality, as the measure of how effectively, clearly, and empathetically communication flows to ensure alignment, productivity, psychological safety, and a strong sense of team cohesion. Thanks for sharing.

[Pablo Salonio Ph.D. @pablosalonio]

@Nouriel @ProSyn Thank you for posting.

[Pablo Salonio Ph.D. @pablosalonio]

@FredaDuan You have a point

[Freda Duan @FredaDuan]

Some deep thinking about the frontier-model business model. All of this is grounded in numbers leaked by The Information, NYT, etc.

🔵The Core: It’s a Compute-Burn Machine

At its heart, the model is brutally simple: almost all costs come from compute – inference, and especially training. Training follows something like a scaling law. Let’s assume costs rise ~5x every year; and ROI on training costs is 2x.

That creates a weird dynamic:

Year 1 training cost: 1
Year 2 revenue from that model: 2
But Year 2 training cost for the next model: 5
Net: +2 - 5 = -3

Run it forward and it gets worse:

Year 3 revenue: +10
Year 3 training cost: -25
Net: -15

Frontier models, as currently run, are negative-cash-flow snowballs. Every generation burns more cash than the one before.

For this to ever flip to positive cash flow, only two things can logically change:

A. Revenue grows much faster than 2x, or

B. Training cost growth slows from 5x a year to something like <2x

Anthropic’s CEO Dario Amodei has broken down scenario B (“training costs stop growing exponentially”) into two possible realities:
https://t.co/eJKJE7ShUK

1/ Physical/economic limits: You simply can’t train a model 5x bigger — not enough chips, not enough power, or the cost approaches world GDP.

2/ Diminishing returns: You could train a bigger model, but the scaling curve flattens. Spending another 10x stops being worth it.

What OpenAI and Anthropic’s Numbers Reveal:

Both companies’ leaked financial projections basically validate this framework.

OpenAI: OpenAI’s plan effectively assumes total compute capacity stops growing after 2028.
Translation: margins improve because training costs flatten. This is scenario B.
https://t.co/jLHILxTNkE

Anthropic:
1/ They assume the ROI per model increases each year. Spend 1, get back say 5 instead of 2.
2/ Their compute spend growth is also much more muted. From FY25 to FY28: OpenAI compute cost growth >> Anthropic's

Using the framework above, they’re counting on both A revenue ramp and B slower cost growth.
https://t.co/bFHX1VSFzw

🔵 $NFLX Is the Closest Analogy

In tech, capital-intensive models are rare, though not unprecedented. $NFLX is a good analogy: for years it had deeply negative cash flow that worsened annually. They had to pour money into content upfront, and those assets depreciated over four years. In many ways it resembles data-center and model-training economics.

Peak cash burn in 2019: -3B
2020 cash flow: +2B

Why the sudden swing positive? COVID shut down production. Content spend stopped growing. Cash flow instantly flipped.

🔵The Endgame: Margins Arrive When Cost Growth Slows

$NFLX didn’t stop investing in content entirely – it just stopped *growing* that investment aggressively once it reached ~300M global subscribers. At that scale, stickiness is high, and they only need to maintain their position, not expand content spend 10x a year.

I don’t think OpenAI or Anthropic will ever stop training entirely. But they won’t need to grow training spend by multiples forever. At some point:

ROI per model goes up, or scaling limits kick in, or both.

And the moment annual training spend stops growing 5x a year, profit margins show up almost immediately.

That’s the strange thing about LLM economics:
It’s a burn machine…until suddenly it isn’t.

Sources:
https://t.co/jLHILxTNkE
https://t.co/fJ4wwxS5uk
https://t.co/qSONdkPtGy
https://t.co/bFHX1VSFzw

++
Full article:
https://t.co/k0FdqNe6KY

[Rohan Paul @rohanpaul_ai]

Robotic Companies in the United States

With global headquarters that are in the US.