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

1. History of digital identity
2. Web 3.0 and a new digital identity
3. User & organization benefits
4. DID obstacles to adoption
5. 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

Credits
Author@lab45: Sujay Shivram, Abhigyan Malik

Generative AI is forming a new economic ecosystem, reshaping the behaviour of key players in the IT industry, generating opportunities for super-scalers, and unveiling numerous niches for startups. The outlines of this new IT landscape are emerging, prompting a closer examination.

What's inside

1. Introduction: The rise & impact of generative AI
2. The technology & business stack of GenAI
3. Business niches for GenAI startups
4. Future trends in GenAI
5. Appendix 1: Reality & expectations of GenAI
6. Appendix 2: Startups across the GenAI tech stack

Introduction: The rise & impact of generative AI

Generative AI has caused significant disruption, expanding its offerings and services well beyond traditional AI domains. This has led to an explosion of potential use cases for customers who aren't AI experts. Unlike before, customers no longer require a team of AI experts, curated data, or precisely measurable outcomes to adopt AI tool and gain immediate benefits. The interaction with GenAI is so seamless and intuitive that the onboarding for new customers is frictionless, eliminating barriers to adoption and facilitating rapid technology spread. The high variability in potential inputs and priming of generative models allows for a diverse range of applications impacting nearly every imaginable aspect of people activities. This is a foundation of a new era of Artificial Intelligence.

In this primer we leveraged our knowledge of 50+ GenAI-related and VC-backed startups to reconstruct the technological stack of the forming GenAI space.

The technology & business stack of GenAI

Large tech companies are leveraging their existing technological and capital advantages to create the framework for the GenAI market landscape, which we are going to explore in this section.

While offering of the LLMs on the current scale and heavy focus on unstructured data are somewhat new, the other elements of the tech stack closely mirror those needed for any large computational modeling. Established companies in the field of traditional AI are at an advantage, as they can expand and repurpose preexisting software, infrastructure, and services. Download Complete Research

Business niches for GenAI startups

While large players are occupying a sizable portion of the GenAI tech stack, there remains more than enough room for GenAI startups to flourish. The landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly.

Bottom-right (AIOps): Here, startups may offer tools for easier adoption of LLMs, facilitating the initial process of customizing and implementing these models.

Ascending (Integration): Moving upwards represents the process of integrating LLMs into various applications and business operations. Startups could offer integration services, templates, or frameworks to streamline this, or build an entire end to end app for a selected market niche.

Moving left (Service platforms): As we move leftwards, the focus shifts from core LLM functionality to auxiliary services. This could range from platforms offering specialized training data, to marketplaces for LLM apps, to optimization tools. These firms may automate the need for certain experts.

This taxonomy can serve as a foundational overview for anyone looking to understand the current state of the LLM ecosystem. It’s also worth noting that the landscape of AI and ML is continuously evolving, with new startups, technologies, and methodologies emerging regularly. Let’s inspect each block in greater detail:

Future trends in GenAI

The future of the GenAI landscape is going to be defined by several processes:

1. Consolidation of major players
2. Rise in open-source adoption
3. Surge in service platforms
4. Expansion of skill marketplaces
5. Segment-specific applications
6. Regulatory oversight and standardization

Appendix 1: Reality & expectations of GenAI

While enhancing the users with great capabilities, the LLM-based service is neither a freebie, nor a cornucopia. Each implementation of LLMs carries its own advantages and downsides. In this section of the Appendix, we discuss what can and cannot be realistically expected from a GenAI model in each of the most popular use cases.

We start with primary properties of a pre-trained LLM model, underlying its strong sides and functionalities as well as build-in flaws. And we move to the current ways of augment LLM model to work around the flaws. Download Complete Research

Appendix 2: Startups across the GenAI tech stack

The table of 78 startups we have based our analysis on is presented in this section.
The states of startups are set to the August of 2023.

Credits
Author@lab45: Rinat Sergeev

Manufacturing is becoming more smarter, efficient, precise, and sustainable by adopting IIoT, AI, Robots, Blockchain, and 5G for operations optimization. Manufacturing business trends are enabling flexible & transparent supply chains, customer-centric & agile production, ecosystem collaborations, and new business models.

What's inside

1. Key Takeaways
2. Key drivers shaping trends in manufacturing
3. Business Trends driving innovation
4. Technology Trends aiding the business

Key takeaways

The convergence of advanced technologies with labour, supply chain, and demand challenges is driving full automation. Nearly 84% of manufacturers have adopted or are considering smart manufacturing. Manufacturers are exploring tech-enabled ecosystem partnerships, reshoring, and factory-in-a-box model to address supply chain instability. AI, IIoT, Big Data & Analytics, Robotics, 5G & Edge Computing are enabling data collection, pattern identification, and prediction for process optimization and efficiency improvement. AI in manufacturing is expected to reach $115 billion in 2032 globally. Blockchain is ensuring supply chain and ecosystem security. Driven by regulations and environmental commitments, manufacturers are adopting technologies to reduce emissions. On-demand production, mass customization, and subscription-based products are enhancing customer experience. Download Complete Research

Key drivers shaping trends in Manufacturing

• Ensuring supply chain resilience and navigating market demand uncertainty are manufacturers’ main requirements amid natural disasters and geopolitical tensions. AI and Data Analytics are key to overcoming disruptions and predicting demand.
• Manufacturers are prioritizing automation to deal with skilled labour shortage, manage rising costs and enhance efficiency while maintaining quality.
• Sustainability is another priority for manufacturers, focusing on responsible materials sourcing, environment-friendly production, and safe waste disposal. Customer preference is changing, and they are seeking sustainable products and processes.

Business trends driving innovation

• Dark factories fully orchestrate processes using automated machines while operators remotely monitor activities, enhancing performance and resource efficiency.
• The product-as-a-service business model opens new revenue streams for manufacturers while lowering the upfront costs for customers.
• Global data exchanges & platforms allow data sharing between ecosystem partners while protecting proprietary data.
• Green manufacturing is getting propelled using digital technologies and adopting sustainability policies.
• Reshoring and micro-factories have gathered pace due to wage increases, supply chain disruptions, and the advent of digital technologies.
• Prescriptive maintenance identifies patterns, performs root-cause analysis, and recommends the best maintenance approach, lowering running costs and downtime.
• Customized product manufacturing is being enabled by additive manufacturing and flexible manufacturing approaches.
• Advanced environment-friendly materials are gaining traction resulting in enhanced performance, high wear resistance, and high thermal stability of products.
• These trends impact various stakeholders, including manufacturers, suppliers, workers, and customers.

Technology trends aiding the business

• AI is transforming manufacturing through real-time machine operation optimization, problem prediction, demand forecasting, cost modelling, product quality inspection, robot navigation, etc.
• IIoT is key for prescriptive maintenance, real-time production and machine monitoring. 5G provides high bandwidth for reliable connectivity between IIoT devices, offloading computation-heavy tasks to edge infrastructure.
• Robots & Cobots enable production line process handling, intelligent material handling, and warehouse automation.
• Digital Twin enables the convergence of information technology (IT), operational technology (OT), & engineering technology (ET) in manufacturing.
• Blockchain allows for tracking materials & goods through the supply chain, inventory management, & warranty management.
• Secure and dedicated private networks, Blockchain, and AI are increasingly used to safeguard critical manufacturing operations from cyberattacks.

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Credits
Lead Authors@lab45: Parag Arora
Contributing Authors@lab45: Hussain S Nayak