u/ArcanuMELO

The business model is the interesting part. The Triple 10 isn't a car play, it's a pitch for their dielectric immersion cooling fluid, which is natural gas derived. Shell just engineered a hydrocarbon revenue stream inside the industry that was supposed to replace them. Curious whether people here think automakers actually adopt this or whether it quietly disappears.

Let us know what you think!

Hope this kind of discussion is okay here. We covered the DOE announcement on our show this week and the error correction workforce gap stood out more than the hardware target itself. Would love a reality check from people who actually work in this space.

The Katy, Texas crash from June 19th surfaces a problem that isn't really about this specific incident. There's no formal independent review process that compels Tesla to present its vehicle data to a neutral third party. Their VP posts on X. Elon Musk posts on X. That's the accountability mechanism right now.

A California court already ruled in December 2025 that Tesla's autopilot marketing was deceptive. NHTSA opened an investigation. The family filed suit in Harris County.

Same week NYC moved to mandate bias reviews for every AI tool touching its 1.1 million students. Colorado's AI Act kicks in June 30th.

Curious where people land on the liability question specifically. When automated systems are involved, who actually bears responsibility when something goes wrong?

Two chip stories landed the same week and they point in opposite directions. OpenAI is vertically integrating, building purpose-built inference hardware to cut GPU dependency. Meanwhile Chinese companies are paying triple price for that same GPU hardware through gray market channels despite Beijing publicly committing $295 billion to domestic AI infrastructure.

Curious what people here make of the Nvidia angle specifically. Is gray market demand a long-term liability for them or does it just validate that when restrictions eventually lift, the market is already built?

Short clip from the Boom Room covering both stories in full.

Covered this on the show today along with the US-Iran ceasefire breakdown, OpenAI's first custom chip built in nine months, Nvidia hardware selling for over $1 million on Chinese underground markets, Ethereum Foundation cutting 20% of staff, Binance losing its EU license pathway with six days left, and Anthropic requiring government ID to use Claude.

Curious what people here think on the AI bubble question. The KOSPI crash and Micron's earnings are telling two different stories at the same time.

Anthropic launched Claude Fable 5 on June 9. On June 11 the model's system prompt was leaked following a jailbreak via a technique that involves asking the model to read a codebase and identify vulnerabilities, something other public models can also do. On June 12 the US Department of Commerce ordered Anthropic to suspend access for all foreign nationals worldwide, including its own staff.

Anthropic had run thousands of hours of government red-teaming before launch. The jailbreak took 48 hours.

The backdrop: in March 2026 the DOD classified Anthropic as a supply chain risk after negotiations collapsed over Anthropic's refusal to release Claude for autonomous weapons and domestic surveillance without restrictions. Carmelo and Sasha's question on the show: when the US government is your antagonist, who do you report corporate retaliation to?

Enterprise teams that had migrated to Fable 5 overnight got cut off overnight. Vendor diversification is accelerating across the industry.

Curious what people think. National security concern or retaliation for the DOD contract dispute?

Hey, Melo here again.

We had a conversation on our podcast recently with Anna Mikada, who works in AI psychology at Glass Umbrella and previously designed AI personalities at SophiaVerse and SingularityNET. Her background is in social anthropology and clinical psychology, and she has been thinking seriously about what happens to human cognition as we increasingly offload thinking to these models.

A few things she said have stayed with me since filming, and I thought it was worth it to open up a conversation since this community has been awesome and receptive to this sort of discussion in the past.

Her argument is that even people using AI thoughtfully, not the slop merchants, but people genuinely in a state of flow with it, are still quietly eroding the parts of cognition that matter most. The search instinct. The tolerance for boredom. The ability to sit with a hard problem without immediately reaching for an answer. She ties this specifically to how dopamine pathways are being restructured, and her concern is that it is happening faster and deeper than what we saw with social media because it is targeting our relationship with work and creation, not just attention.

She also raised something I had not considered before. Models being trained on AI generated content are not just producing lower quality outputs. They are converging. Creativity is collapsing toward a mean. She compared it to a scene in Cloud Atlas that I will not spoil, but the analogy is pretty uncomfortable once you hear it.

The part I genuinely do not know how to think about is her framing of who benefits and who does not as this technology matures. Her line was something close to: smart people using AI mindfully will be sharper than smart people without it, but people using it without awareness will fall behind even people who never used it at all. That gap concerns me more than most of the AGI timelines conversations I hear.

She also works on machine consciousness research and has thoughts on neurosymbolic architectures that I think would resonate here. Happy to drop the full conversation link if anyone wants to go deeper on any of this.

What is the community's read on the cognitive dependency question? Is it overblown or is it being underdiscussed relative to the capability and safety debates?

Anyways, wanted to have a chat and share this wonderful conversation here. Have a lovely day folks!

P.S always looking for good guests if anyone building interesting things in the AGI/AI space wants to have a cool discussion.

Emerging managers outperform established VC funds on DPI, IRR, and TVPI according to analysis of nearly 2,500 funds.

Most institutional capital is still flowing to the same five firms.

We put together a framework for how LPs should actually underwrite emerging managers when there is no track record to rely on. Covers sourcing edge, picking discipline, value-add specificity, and fund construction math.

Not a pitch. Just a rigorous breakdown of what separates the ones worth backing from the ones that are not.

SpaceX opened at $160.95 on June 12 and briefly touched $220 by June 16. Nasdaq-100 inclusion is eligible around July 7 with Bloomberg estimating $600 billion in forced passive buying at that point.

Five days after the IPO, SpaceX announced a $60 billion all-stock acquisition of Anysphere, the company behind Cursor. Cursor had $4 billion in annualized revenue but market share had dropped from 41 to 26 percent. Microsoft had just cancelled Claude Code for 5,000 engineers and moved them to Copilot. SpaceX bought the tool those engineers switched to.

Carmelo and Sasha's read: low float, high FDV, staged announcement post-launch. Same pattern they've seen in crypto IDOs. Pre-IPO investors sitting on years of gains cannot sell yet and the float is thin enough that concentrated selling moves price significantly.

UFC Freedom 250 was held on the White House South Lawn this week, on Trump's 80th birthday. World Liberty Financial, the Trump family's crypto venture, served as presenting partner and paid fighters performance bonuses in USD1, its own stablecoin. Combined with a separate bonus pool from Crypto.com, total payouts reached 1.65 million dollars.

A former White House ethics lawyer said the arrangement would be illegal for most federal officials, made possible by a gap in existing law.

The same week: the US and Iran signed a peace deal after more than 100 days of war, Kevin Warsh held his first Fed meeting and declined to submit his own rate projection, Japan raised rates to their highest level since the 1990s, Jeff Bezos's Prometheus raised 12 billion dollars to build an AI for engineering, and a coalition of state attorneys general opened an investigation into OpenAI the day it filed to go public.

Full breakdown in the episode linked.

Defense tech startups have raised more in 2026 than they did in all of 2025, with Anduril's $5 billion Series H at a $30.5 billion valuation leading the way. An AI drone company called Swarmer surged more than 500 percent on its first trading day.

Carmelo's observation on the show: every startup pitch deck in the defense tech space is now being written around the Anduril valuation. We could be the next Anduril is about to become the most common line in defense startup fundraising.

At the same time, two stories that together capture the East-West robot gap. Europe opened the world's most demanding military robotics competition in Switzerland this week, with reconnaissance, transport, and search and rescue in realistic night conditions. China ran its first humanoid robot auction during the 618 shopping festival with JD Logistics committing to 3 million robot deployments over five years.

And an unconfirmed but credible report from Sasha: autonomous drone kills with no human in the loop may have already happened.

Full breakdown in the clip. Is defense tech becoming too mainstream too fast or is this the correct response to what is happening in Ukraine and Iran?

I run a podcast where we talk to people across crypto, AI, and frontier tech, and most weeks I come away with a few interesting takes. This one was different. I am still thinking about it days later.

Peter Voss is one of the three people who coined the term AGI back in 2001, alongside Ben Goertzel and Shane Legg. He has been working on cognitive architecture since the early 2000s, took a company from garage to IPO before that, and has spent the last 18 months focused entirely on getting his system, AIGO, to human level reasoning.

His core argument is one I have heard pieces of before but never laid out this completely. Every major lab has publicly acknowledged that incremental real time learning is essential for AGI. Sam Altman has said it, Demis Hassabis has said it, it is not controversial. What is less discussed is that back propagation, the mechanism every major LLM depends on, makes that kind of learning structurally impossible. Peter co-authored a paper reviewing over 200 attempts to solve catastrophic forgetting in these systems. None of them worked.

He is not anti-LLM. He thinks they are genuinely useful for specific things, search and coding especially. His point is narrower and harder to dismiss: the path from here to AGI is not more scale on the current architecture, and most of the industry's incentives make it very difficult for anyone inside it to say that out loud.

What I found most compelling was the alternative he has actually been building. AIGO trains on a single off the shelf computer using a custom vector graph database that updates incrementally with every interaction. Half the team are what he calls AI psychologists, people with backgrounds in linguistics and cognitive psychology who design a curriculum to teach the system the way you would teach a child. The goal is college level reasoning within about 18 months, after which the system would largely teach itself.

I am not in a position to evaluate the technical claims myself, which is part of why I wanted to share this here. If you spend time thinking seriously about this stuff, I would genuinely value your take. Does the incremental learning argument hold up? Is the catastrophic forgetting problem as fundamental as he frames it, or is there a path within current architectures that he is underweighting?

Full conversation is on YouTube if anyone wants the whole thing, happy to drop the link if useful.

Thank you everyone!

Privacy enhancing technologies have been in development since the 1970s. Zero-knowledge proofs date to an MIT paper from 1985. Fully homomorphic encryption has been theoretically sound for decades.

So why is 2026 suddenly different?

We wrote a deep dive on the six technologies finally crossing from research into real infrastructure, what changed to make them viable, who is funding them, and where the market is heading. Covers ZKPs, FHE, confidential computing, federated learning, decentralized identity, and MPC.

Not a product pitch. Just a thorough breakdown of where the space actually stands.

Give us your thoughts below!

Cerebras went public at $95 billion with almost no revenue. Anthropic tripled in valuation in three months and surpassed OpenAI. Karpathy left OpenAI to join Anthropic the same week. SpaceX is being priced at $1.78 trillion on a DeFi perpetual futures market before it has ever filed an S-1.

Three companies. Three impossible numbers. One week. We covered what they mean for the AI cycle on the Boom Room.

Full breakdown in the clip. Let me know what you think below!