BIO price

BioProtocol reshapes DeSci: biotech + AI + onchain funding 125M $BIO staked • Aubrai • Seed $6.9M led by Maelstrom • V2 on Solana/Base @BioProtocol

My holding in Cryptos. $om $melania $usual $meme $ena $bio $act #ai16z $ethfi $ath $manta $cow $bom $world $foki $warmwhole $blast $arb $aixbt $myro $tnsor $eigent $altlyer $wlfi $griffain $jasmy $sudeng $ksm $the $chainbase $move $voxels

The wind is a bit chilly, but the news is hot. Tonight, let's discuss two major events that bring research and privacy into the "usable zone": @zama_fhe's FHE acceleration breakthrough and @BioProtocol's full-stack transformation of funding and IP in biological research onto the blockchain. Let's start with a small appetizer: FHE is a hardcore black technology that allows calculations without decrypting data, with a key bottleneck called bootstrapping (the step of "denoising" the ciphertext). Previously, it was painfully slow and would bottleneck as soon as the volume increased. Now, it's like kicking the door open: • bootstrapping: 53 ms in 2021 → 0.9 ms today, a 56x speedup • 64-bit encrypted addition: 2000 ms in 2022 → 8.7 ms today, a 230x speedup • 64-bit encrypted multiplication: 13000 ms in 2022 → 32 ms today, a 400x speedup What does this mean? The TPS for confidential transactions has increased from 0.1 in 2022 to 230 today, surpassing Ethereum and several L2s. The roadmap is clearly laid out: soon reaching 1000 TPS, moving towards dedicated FHE ASICs by 2029, aiming for 100k TPS. For privacy to be useful, it must first be fast, and this line is now at full speed. Now, let's shift the focus to the front lines of research. Most people think that science only happens in locked laboratories, but in reality, data, samples, life logs, and clinical readings are the bricks and steel that determine whether something can be replicated or implemented. @BioProtocol has turned this pile of "order in chaos" real assets into a collaborative research operating system on the blockchain: • Integration of funding and IP on-chain, BioDAO brings researchers, funders, and patients to the same table. • IP can be split, tokenized, and circulated; experiments and validations have unified, auditable outputs. • AI-assisted research funnels, community-driven trials, and decentralized governance allow research to flow seamlessly from project initiation to validation. • $BIO: staking → BioXP, Launchpad access, IP-NFT profit sharing; participation is not just talk, but real revenue sharing. • And there's a slogan I really like: "Some logs add time. Some logs add truth"—standardizing the recording of variables affecting aging such as diet, sleep, exercise, and social interactions, linking them to clinical readings, giving each experiment a reusable "truth watermark"; these are not just show logs, but reusable assets. Putting the two lines together, it makes sense: • Collection: Individual life logs, experimental data, and clinical indicators standardized according to protocols, leaving traces on-chain. • Calculation: Using FHE to run AI and statistical analysis in encrypted states, hospitals, research institutes, and BioDAO can collaborate without exposing underlying data. • Settlement and rights confirmation: Results are solidified as IP-NFTs, with revenue sharing, incentives, staking, and governance all transparent throughout the chain, auditable, and protecting privacy. • Payment and compliance: Confidential transfers at 230 TPS are already available, moving towards 1000/100000 TPS, with research funding and milestone disbursements no longer delayed. Imagine a few real-world scenarios: • Multi-center clinical trials: Each hospital shares metrics in ciphertext, collaborating on analysis without disclosing original cases. • Longevity research: Running encrypted state models coupling lifestyle with epigenetics across institutions, validating progress in community trials. • BioDAO governance: Privacy voting + publicly verifiable results, with funding flows, sample flows, and knowledge flows operating in sync. • Researcher profiles and credit: Contributions of data/models/replicated experiments can be recorded and profit-shared, incentivizing not just Day 1, but throughout the entire lifecycle. Two main lines: one turns "science money" into a programmable financial layer, while the other refines "data privacy" to deployable performance levels. Science needs to be faster and broader; the door must be opened, and the moat must be maintained. Not to boast, the numbers are already here: 0.9 ms, 8.7 ms, 32 ms, 230 TPS; standardized logs, on-chain IP, BioXP, and decentralized trials. The next step is to see who can turn this closed loop from experiment to production into an industry norm. Who will you bet on, or should we all just hop on this converging express train together?