Ashis Tiwari

1. Phala Network – A privacy-preserving compute layer that uses confidential computing to run off-chain workloads securely. It enables developers to run sensitive AI tasks (like private model inference or encrypted data processing) without exposing the data or logic on chain.
2. Bittensor – A peer-to-peer machine learning network where participants contribute model updates and earn tokens. The network rewards useful AI agents and fosters an open marketplace for AI intelligence.
3. Render Network – A decentralized GPU compute market where contributors share idle GPU power for rendering, simulations, AI training, and inference workloads. It aims to reduce reliance on centralized cloud compute.
4. NodeGoAI – A decentralized compute marketplace that lets users rent out unused CPU/GPU power for AI and high-performance workloads. It bridges hardware owners and developers needing scalable compute.
5. Oort – A decentralized data and compute platform designed to support AI workflows. It focuses on decentralized data storage, processing, and tooling to feed decentralized AI applications.
6. ShareAI – A project that turns idle GPUs into an accessible API backend for developers needing AI compute. It’s positioned as decentralized compute with easy AI integration.
7. The Graph – While not a physical layer, The Graph is critical infrastructure that indexes blockchain data in real time. AI systems and DePIN apps use it to query structured data efficiently.
8. AIOZ Network – A decentralized bandwidth, storage, and compute network that can support AI and media workloads. Participants earn tokens by contributing resources that power distributed content delivery and compute tasks.
9. Grass Network – A decentralized data collection and web data provisioning layer that can supply datasets useful for training and analytics in AI applications.
10. Shieldeum – A decentralized security and compute framework that layers AI-driven threat detection on distributed infrastructure.
11. Ozak AI – A newer entrant combining predictive AI tools with decentralized compute and automation for large-scale data analysis.
Why This Space Matters
Decentralized compute and AI are complementary. AI workloads (training, inference, data labeling) are resource-intensive. Traditional cloud providers are expensive and centralized. DePIN efforts try to distribute that burden across many contributors while creating economic incentives.
Privacy and secure compute are big angles. Projects like Phala focus on confidential execution, letting developers run sensitive AI tasks without exposing data. That matters for regulated industries, healthcare, identity systems, and business logic that cannot be public.
Open markets for AI resources could democratize access. Instead of one company owning most AI models and infrastructure, decentralized networks could allow anyone to host models, run compute, or contribute data.
Data access and indexing (like what The Graph does) is crucial for AI to pull insights from blockchains and decentralized apps. Without structured access, training and analytics become harder.
Things to Keep in Mind
Most of these projects are early stage. Technology, adoption, and economics are still evolving.
There is overlap with traditional cloud and web3 compute marketplaces (some projects blend both).
Evaluate mission, community, adoption, and technical traction before forming an investment view.

Crypto never really sleeps. Prices move at 3 a.m., developers ship upgrades on weekends, and someone somewhere is always launching the next idea. That nonstop rhythm is part of what makes the space exhausting and fascinating at the same time.
Bitcoin started as a peer to peer experiment in digital cash. What followed was an explosion of parallel efforts trying to answer a bigger question: what else can be rebuilt if value and ownership can live natively on the internet? Over time, that curiosity turned into exchanges, lending markets, NFTs, gaming economies, DAOs, and entirely new funding models.
Ethereum pushed the idea further by turning blockchains into programmable platforms. Instead of only sending coins, people could deploy logic. Code became the referee. Agreements could execute automatically. That simple shift unlocked a laboratory where finance, art, governance, and identity could all be tested in public.
What makes the ecosystem unusual is that it evolves in full view. Successes are transparent. Failures are too. When a design breaks, everyone studies the wreckage, forks the code, and tries again. Iteration happens at internet speed.
Another interesting twist is how incentives are baked in. Users are often owners. Developers can become stakeholders. Communities can fund themselves. Traditional industries usually separate customers, builders, and investors. Crypto tends to blend them, which changes how loyalty and growth work.
Then there is the global aspect. Someone in a small town can access the same protocols as a hedge fund in a financial capital. Barriers still exist, but the baseline is far more open than legacy systems. A smartphone and connectivity are often enough to participate.
Critics point to volatility, scams, and speculation, and they are not wrong. Those elements are real. But they coexist with serious research into cryptography, distributed systems, and economic design. The noise can hide the signal, yet the signal keeps getting stronger.
The next chapter may feel less like a gold rush and more like infrastructure. Better custody. Friendlier wallets. Compliance layers. Integration with banks and payment providers. Less drama, more reliability. Not as flashy, but probably more durable.
What keeps people engaged is the sense that the rules are still being written. Very few industries allow newcomers to influence architecture at such a foundational level. If you care about how money, ownership, or coordination should function online, this is one of the rare places where experiments can become reality.
That is why, even after brutal downturns, builders return to their keyboards. The opportunity to redesign pieces of the financial system is hard to ignore, and progress rarely waits for perfect market conditions.

Prices have pulled back, sentiment is fragile, and every bounce feels suspicious. Yet the pattern the industry has shown again and again is that contractions tend to clear excess while the underlying technology keeps moving forward.
We have seen it through multiple resets in Bitcoin and Ethereum. Leverage unwinds, fast-money narratives fade, and attention shifts from hype to execution. When the dust settles, the networks that kept building during the storm usually end up defining the next expansion.
A quieter market changes incentives. Funding becomes stricter. Communities focus more on utility than promises. Developers spend less time reacting to price and more time improving throughput, privacy, interoperability, and usability. That groundwork rarely makes headlines in the moment, but it is often what supports the next wave of adoption.
One area that continues to gain relevance is confidential computing and privacy-preserving infrastructure. As more real businesses explore blockchain rails, they quickly run into a simple problem: they want transparency where it helps, but they also need protection for sensitive logic and data. Public chains alone do not always offer that balance.
This is where projects like Phala Network come into the conversation. The goal is to enable computation that can be verified while still keeping inputs or processes private. Instead of exposing everything on-chain, trusted execution environments and cryptographic techniques allow applications to prove correctness without revealing secrets.
If decentralized finance grows up, institutions participate, and enterprises tokenize assets, demand for this type of infrastructure could expand significantly. Trading strategies, identity data, medical information, AI workloads, and business contracts are all areas where privacy is not optional. Networks that make confidential execution practical may end up sitting underneath many higher-level products.
During a downturn, expectations cool, which can actually help such platforms. Rather than chasing speculative inflows, they can refine tooling, attract developers, and build integrations. When capital returns, mature ecosystems tend to capture it faster than ideas that are still on the whiteboard.
Of course, potential is not destiny. Delivery, security, partnerships, and real usage will decide outcomes. Competition in privacy and compute layers is intense, and the bar keeps rising. But the direction of travel for the industry suggests that verifiable yet private computation is becoming a requirement, not a luxury.
Market cycles test conviction. They force participants to separate price action from progress. While charts move sideways or down, codebases expand, communities experiment, and new capabilities come online.
When optimism eventually returns, it often arrives to find a landscape that is far more advanced than it remembers. The projects that invested in fundamentals during the hard months are usually the ones people rediscover first.

Prices across digital assets have cooled off again, and the mood feels heavy. Red candles, liquidation headlines, and social feeds full of fear can make it seem like the story is over. History suggests the opposite. Periods like this have often been the reset that sets up the next phase of growth.
In earlier cycles, sharp pullbacks hit Bitcoin, Ethereum, and the broader market after waves of leverage and hype built up too fast. Weak projects faded, speculation washed out, and builders kept working. What survived tended to emerge stronger, with better infrastructure, clearer regulation, and more serious adoption.
What makes downturns uncomfortable is also what makes them useful. Easy money disappears. Narratives get stress-tested. Teams that relied only on marketing struggle, while those with real products, users, and revenue continue to ship. Capital becomes selective, which usually improves quality across the ecosystem.
Adoption has not stopped just because prices fell. Payment companies continue to explore stablecoin rails. Financial institutions experiment with tokenization. Developers keep improving scalability, security, and user experience. None of that work vanishes during a correction. In many cases it accelerates, because noise is lower and expectations are more realistic.
For long-term participants, drawdowns can offer asymmetric opportunities. Accumulating fundamentally strong networks when sentiment is poor has historically produced better outcomes than chasing vertical rallies. That does not remove risk, but it changes the entry profile.
It is also worth remembering that crypto is still a young asset class. Volatility is part of the design. Open, global markets trading 24/7 react faster than traditional systems. Excess builds quickly, and it unwinds quickly. Anyone involved needs position sizing, patience, and a time horizon that can survive dramatic swings.
Looking forward, several themes continue to attract attention: real-world asset tokenization, more mature DeFi structures, clearer regulatory frameworks in major economies, and deeper integration with existing financial plumbing. If these develop as many expect, the foundation under the market becomes broader than pure speculation.
None of this guarantees an immediate rebound. Recoveries take time, and confidence rebuilds gradually. But innovation has a habit of compounding quietly while headlines focus on price.
The question in moments like this is not whether volatility exists. It always has. The real question is which projects will still be relevant when the next wave of users arrives. Those are usually the ones worth studying while the market is distracted by fear.