Digital identity systems have evolved and continue to evolve. They are core to our interactions with the digital world and have made great strides in both security and convenience. However, the privacy and data-use consent of identity-holders remain problematic.
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What’s Inside
History of digital identity
Web 3.0 and a new digital identity
User & organization benefits
DID obstacles to adoption
Market and product outlook
History of digital identity
We have seen Digital Identity evolve from the silo identity model to the federated identity model. The current systems reside at a very low trust level, with over 93% of users distrusting social media platform’s digital custodianship. We believe the next stage of evolution will be Decentralized Identity
Web 3.0 and a new digital identity
Web 3.0, the internet’s next evolution aims for a decentralized interconnected and intelligent web. It aims for decentralized, peer-to-peer networks for secure, trustless transactions— without intermediaries. Unlike today’s static web that does not adapt to the needs of its users, Web 3.0 will be dynamic and interactive, leveraging AI and blockchain to personalize, adapt, and democratize the internet. As user identity is crucial in Web3, DID will be foundational. We explain the ecosystem and functionality of the DID network. Download Complete Research
User and organizational benefits
User benefits include credential forgery prevention, password-free authentication, spam prevention and many others. While Organizations will benefit from operational cost reduction and security cost reduction, enhanced user experience thereby improving the brand. Organizations must however use a phased approach to implement, which is explained.
DID obstacles to adoption
We identify four key obstacles that present themselves and what organizations can do to overcome them.
Market and product outlook
The global decentralized identity market was valued at $285 million in 2022 and is expected to grow at a CAGR of 88.7% over the next 5 years. We evaluate top players and products in the market and how they have helped the technology evolve. Download Complete Research
By acquiring 100+M users in first 3 months of launch, ChatGPT has brought the field of Generative AI (GenAI) into mainstream awareness. The adoption of ChatGPT and similar applications have positioned Generative AI (as well as “Deep Learning”) as the newest disruptive tech after cloud computing.
What's inside
An overview of Generative AI, the market for it and the reasons for the hype
A qualitative assessment of its time to mainstream across various industries
Enterprise use cases and its limitations
What is Generative AI?
Generative AI, a field of Artificial Intelligence, refers to computational models that are trained on massive amounts of input data (300bn words in the case of ChatGPT). They can synthesize data, draw inferences and create new outputs in the form of text, images, video, audio, new data and even code.
Two architectures have made GenAI immensely valuable
Generative Adversarial Networks (GAN), that became popular in 2014are used for generating images & videos.
Transformer Models, proposed by Google in 2017, are used for generating text.
ChatGPT, for instance, uses the transformer model along with Human Feedback Reinforcement training to generate high quality outputs. Training a model requires intensive computational power (supercomputers were used to train GPT3) and significant investments (OpenAI being a key example). But once a model is trained, it can be optimized for a larger user base. Download Complete Research
The market for Generative AI
The market is expected to grow from $8B in 2022 to $109B in 2030 at a CAGR of 34.6%. Key facts as follows
Software segment accounts for 60%, service segment is the fastest growing.
Media & entertainment is the biggest user of Gen AI accounting for 18% of revenue, BFSI is the fastest growing at 36% CAGR.
North America is the biggest market with 40% share and APAC is the fastest growing region.
Why GenAI is here to stay?
Need for content synthesis We generate ~2.5 quintillion bytes of data every day on the internet. This not only makes searching for information tough but also makes inferring tougher, for regular users. GenAI tools can search, synthesize and compose an answer.
Democratization of content creation We are moving from a search and retrieval economy to an infer and compose economy. People used to prompt algorithms to search and retrieve information but now they can prompt algorithms to infer and compose information.
Instant economy Digital natives prefer tools that enable instant creation of content e.g. Tik Tok. ChatGPT can generate a word in 350ms after processing database of 300B words.
Access to massive computational power The ability to instantly process and compose information using cloud computing.
Evolution of deep learning neural networks Large Language Models have become openly available. These models help organize much of the internet’s information and develop patterns to mimic human decision-making.
An early assessment of time to market & key GenAI companies.
Enterprise use cases of Generative AI
GenAI will fundamentally change several functions in enterprises leading to improved productivity and performance of employees. A Few areas of business where it will have the biggest impact are as follows:
Content creation Gen AI will lead to more automation in content creation. It will not only reduce the cost of content creation but also increase the quality & variety of content created. Generative AI based DIY Apps are expected to emerge for marketing and design functions.
Content personalisation Marketing touchpoints like newsletter, websites, videos, metaverse etc. will get hyper-personalized. This will improve brand engagement and conversion ratio of the sales funnel.
Drug discovery Drug Discovery is a time-consuming process that can extend to 5-12 years. Gen AI can help identify potential drug candidates and test their effectiveness using computer simulations, thus saving time in the process. It has already led to tremendous real-world value, when the first mRNA covid vaccines were developed by programming mRNA molecules to express the specific antigen response. By 2025, more than 30% of new drugs and materials could be systematically discovered using GenAI techniques, up from zero today.
Software development IT products and services could see the biggest impact. Below are some scenarios that may unfold.
Reduced time on testing and coding Gen AI has the capability to create, test and debug the code in real time. In typical product development cycles, coding and testing takes 30-40% of the time. Gen AI will cut this significantly, thus reducing time to market.
Improve programmer performance Code can now be generated with a simple prompt command. This will allow even less-tech savvy programmers to generate a better code. Gen AI can also translate the code from one programming language to another. However human intervention will still be needed to customize the code for specific vertical / client use.
Automate recurring tasks Manhours will be freed from repetitive tasks, as automation is easier with GenAI. Tasks like report generation, log analysis etc will fall in the domain of automation.
More secure and reliable IT infrastructure Gen AI can track performance and security of IT infrastructure in real time. It can pre-empt any failures, by generating early warning signals and hence improve reliability of operations.
Foundational for identity verification and rights access, government-issued IDs face breaches and inefficiency within centralized systems. Enter Decentralized Identity solutions, redistributing verification control to individuals. But is it applicable for equally for all government services?
What's inside
Decentralized vs Centralized ID for governments
Identity credential issuing process today
Suitability of government functions to Decentralized Identity
Long term outcomes
Decentralized vs Centralized ID for governments
Decentralization of government-issued IDs is a complex issue with potential benefits and drawbacks. Whether government issued Ids should be decentralized or not depends on various factors that we delineate. Any decision on decentralization should be carefully considered and implemented with caution to ensure that they do not breach security or undermine the government’s programs. Download Complete Research
Identity credential issuing process today
Govt Identity Credential Issuing Process
We detail out the current Issuing process typically followed by governments today and identify several challenges and issues that face it today. This includes Ids like the Passport, Licenses, Voter cards and Social security cards. All of them are critical and everyone can do with an easier and more fool proof process for the same.
Suitability of government functions to decentralization
We evaluate the different government functions such as Education, healthcare, elections, security, taxation, etc and analyze which of these would be most suitable to be decentralized. We map them on a matrix of Complexity & Coordination and the Need for scrutiny to give us an easy framework for assessment. Download Complete Research
Long term outcomes
We end by considering the long term onjectives and intended outcomes of such an exercise.We feel that Decentralized Identity solutions can rebuild trust in public institutions by empowering residents with data control.
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