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Distributed computing platform Ankr to launch its Mainnet

distributed computing platform

The US-based distributed computing platform, Ankr Network’s Mainnet is set to launch on July 10th, according to an official blog post. Once live, the new mainnet will allow enterprises to provide their idle cloud resources to Ankr’s platform. In 2018, the company launched an Initial Coin Offering (ICO) and raised funds amounting to a whopping $14.5 million, according to InWara’s Market Intelligence Platform.

During its heyday, launching an ICO was the most convenient way for a startup to raise funds. It was easier to execute, less time-consuming, less expensive and the sale of ICOs were a non-equity deal.

But the beginning little improving of 2018 was also the start of the ICO market crash as investors and regulators grew weary of ICOs. More than a year since then, there have been very few improvements to ICOs. Both the number of ICO projects being launched and the funds raised by ICOs have crashed considerably.

Funds Raised by ICOs

distributed computing platform

Source: InWara Market Intelligence platform

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The funds raised by ICO projects have depreciated by over 91% YOY during H1 2019 to $1.32 billion. This is still a significant amount to be raised via crowdfunding. But note that, as many as 403 ICO projects were launched during this period. 

Notably, while funds raised by ICOs tapered, IEOs and STOs have witnessed significant growth over the past year with IEOs outperforming all other token offerings.

Ankr Network’s vision for its future

Along with the mainnet release, Ankr also introduces Ankr chain and its native token to the public. Its native token (Ankr) will be a critical part of the Ankr platform and will likely serve the purpose of purchasing services, incentivizing computer resources.

Ankr believes its platform can be leveraged for numerous real-world use cases which could be critical in driving adoption of its platform. For example, Ankr believes its platform can be used in computation-heavy applications specifically where computation offsets communications, running Monte-Carlo simulations, time-sensitive signal processing offloading.

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