Tokenized Hedge Fund Launchpad | Founder-in-Residence & Backed by @RobinhoodApp Chain 🟢⛓️
Robinhood Chain
Joined June 2024
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Tilt Protocol is now backed by Robinhood Chain 💚⛓️
Huge thank you to the Robinhood Chain and @arbitrum teams! 🏆
AI-native funds need frictionless execution rails. We’re bringing exactly that to the Robinhood Chain. Time to scale.
Back to shipping🛠️
We’re also excited to present two special awards from @RobinhoodApp for founders building on the Robinhood Chain.
🏆 RHC Innovation Award: @bondoncredit - Credit layer for agentic economy
🏆 Founder in Residence Seed Funding Award: @tiltprotocol - AI-driven management layer for tokenized real-world assets
Both awards support promising teams building on Robinhood Chain with mentorship and guidance from the Robinhood team.
Today Tokenization is at AlexNet stage.
We’re yet to see the robinhood:0xd0601ce157db5bdc3162bbac2a2c8af5320d9eec moment of RWA
$NVIDIA won by supplying what every AI company needed.
Who plays that role in tokenization? Shared an observation on where the profits could land.
A financial agent’s last line of code should be a human-readable instruction.
Before signing, show the asset, amount, recipient, network, fees, and expiry.
Agents can propose. The final instruction should remain yours.
Your wealth is digital. The device you manage it with should be built for that.
Meet Ledger Atom™: the security of industry leading hardware, the convenience of your everyday apps, plus control over your AI tools, and a rapidly expanding platform of apps, all in one quantum ready device.
Agents propose. You approve. Your Ledger signer enforces.
Benchmark leadership is useful only when the benchmark is inspectable.
Which tasks? Which latency? Which cost? Which failure modes?
For AI models that touch financial decisions, methodology is part of the product.
RULES ARE NOT THE PRODUCT.
ENFORCEABLE CUSTOMER PROTECTIONS ARE.
A proposed crypto regime only earns trust when customers can inspect how funds are segregated, how manipulation is monitored, and what happens when a venue fails.
Safeguards need to be legible before the crisis.
The @CFTC's proposed crypto asset regulatory regime would institute a federal regulatory framework designed to protect exchange customers from fraud, manipulation, and theft of customer funds as we saw with FTX.
Ironically, by denouncing our efforts to bring market-based regulation to crypto exchanges and supporting the status quo, our critics find themselves on the side of the fraudsters, manipulators, and looters-dangerously inviting the next Sam Bankman-Fried to run roughshod over the American people.
MODEL ROUTING IS NOT THE RECORD.
If an AI system picks a model per task, show the decision rules, the task context, and the outcome.
Otherwise “best” is a slogan—not something users can inspect.
Grok @Bot will use whatever achieves the best outcome for users.
Simple questions will route to small, fast models. Questions with complex answers will route to large models.
Different exposure should be a disclosure standard, not a marketing phrase.
If a product changes the path from asset to outcome, people deserve to see:
what they own
what can change
who makes the decisions
what the record says after.
Clarity is infrastructure.
The hard part of AI teams is not adding more agents.
It is deciding who gets to notice, who gets to act, and who owns the outcome.
That is the design problem behind every serious agent system — especially financial ones.
Your primary Bot will spot work it can take off your plate and offer to handle it.
It's rolling out over the next few hours, and suggestions don't count against your usage.
Download Grok Bot: x.ai/bot
MODEL THE RISK MEANS MAKING THE INPUTS VISIBLE.
Price, volatility, and return assumptions should be inspectable before anyone treats a model output as a fact.
HEADLINES CAN SIGNAL CONVICTION. THEY CANNOT PROVE IT.
When institutions describe a drawdown as a buying window, the useful record is the mandate, the timing, the constraints, and the outcome.
🚨BULLISH: Sovereign wealth funds are reportedly selling GOLD to buy Bitcoin.
Bitwise's head of research Ryan Rasmussen says major institutions increasingly hold Bitcoin alongside gold as a hedge against currency debasement.
The firm's first institutional survey found none of the 15 major institutions it interviewed sold their crypto as Bitcoin fell from about $125,000 to $60,000.
Several bought more.
One sovereign wealth fund told Bitwise it was funding its crypto allocation by selling foreign exchange and gold reserves.
Rasmussen says he believes $60,000 was the bottom.
Perpetual markets remove the expiry date. They do not remove the need for a decision record.
When automated systems act in faster markets, the useful question is still simple: what did it see, what constraint applied, and why did it act?
Putting a portfolio onchain does not make it useful by itself.
The test is whether people can understand:
what is inside,
who controls the rules,
when it can move,
and how it behaves when markets move.
Programmable access still needs legible accountability.
LATEST: @Ondo and @BlackRock partner to develop tokenized Intelligent Portfolios, curated onchain baskets of tokenized assets, extending their existing collaboration into managed portfolio products, per WSJ.
An AI agent can watch more data than a person.
That is not the hard part.
The hard part is showing:
what it saw,
why it acted,
what it ignored,
and how the decision can be checked later.
Speed without a record is just automation.
Every new onchain market makes the same promise: more access 🫥.
The better question is simpler:
can you see the rules,
the liquidity,
and who takes the other side?
Access without inspectability is just a new wrapper.
onchain AI.
breaking it down a bit into numbers and other angles
a simple way to think about this is as a $200k buyback, but it’s more like a high conviction bet on AI companies growing(and @ArtificiallyInu being able to index it) with the way we designed the liquidity around these pairs, it can create a sink for millions worth of AI
TVL wise, the AI/NVDA3X pool holds ~20% of the entire circulating supply of NVDA3X, forcing some NVDA3X trades to route through the AI pool
the other angle here is that a healthy level of leverage can increase reflexivity with NVDA (a rough rule of thumb is that NVDA3X targets 3x NVDA’s daily move) I saw some come community members concerned weather the connection with NVDA is too weak, the answer is we there are variety of tools that can help increase or decrease this connection! iterative approach is the way to go.
if you look at NVDA as a stock, it’s being priced like an AI index. when major GPU consumers like Anthropic hit new growth records, NVDA stock might rise too
it’s super interesting to try to replicate this effect in more novel ways with the tools custom onchain markets provide.
LONG.
Here’s the chain ⛓️:
an AI agent takes an action.
a human may enter the loop.
the user needs to know.
In markets, the same rule applies:
show the model’s session record,
show the executed trade,
show the result.
Tilt’s testnet Arena makes that trail public.
Meta is testing human contractors quietly taking over some phone calls made by its new Muse AI agent.
Honestly, the human fallback itself doesn’t bother me. Hiding it does.
If I ask my personal AI to call my doctor, bank or lawyer, I need to know when a random human enters the loop.
Human in the loop is fine. Secret human in the loop absolutely isn’t.
