Generative AI startups: Landscape & trends

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Primers

Primers: Primers are quick short form business reports that educate leaders on key emerging technologies.

Generative AI startups: Landscape & trends

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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.

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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

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Sustainable technology: An urgent need

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15:13 Minutes The average duration of a captivating reports.

Primers

Primers: Primers are quick short form business reports that educate leaders on key emerging technologies.

Sustainable technology: An urgent need

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Technology both improves and harms environmental sustainability. It’s is a double edged sword. While emerging technologies such as Al, loT, AR/ VR and others are being used to help achieve sustainability goals, these technologies when put into mainstream adoption will leave a hefty environmental footprint.

What's inside

  1. The urgent need for sustainable technology
  2. A framework for sustainable technology in datacenters
  3. Implementing our sustainable technology framework
  4. Sustainable technology maturity model
  5. Bringing it all together

The urgent need for sustainable technology

If we continue on our current trajectory, datacenters will leave a huge footprint on our planet. Data centers contribution to Co2 Emissions will go up 12X by 2030 and their consumption of water will go up 17X in the same time. Needless to say, they will be huge contributors to E-waste in landfills.

A framework for sustainable technology in data centers

We have developed a framework based on the IT lifecycle consisting of a stack of building blocks. We believe this framework will help facilitate a comprehensive and structured implementation of a Sustainability strategy within the data center.It is quite intuitive and easy to identify potential actions that can be taken and what their impact would be. Download Complete Research

Implementing our sustainable technology framework

As organizations move with sustainable strategies, we highlight key insights that will help drive these strategies. We also identify metrics that can be used to measure progress.

The maturity model

We have painstakingly built a 5-level maturity model that can be used as a guideline to assess where the organization stands. We also give insights on what would be required to move to the next level.

Bringing it all together

We finally put this all together to see how organizations should approach their Sustainability journey.

Credits
Author@lab45: Hussain S Nayak, Sujay Shivram, Chandan Jha
Author@Wipro: Susan Kenniston

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Reimagining business processes through decentralized identity

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Reimagining business processes through decentralized identity

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Decentralized Identity systems solve for inefficiencies and security breaches, making them extremely useful for enterprises. We explore in detail important industry use cases where these solutions can be used and means to implement them.

What's inside

  1. Transforming KYC using DID
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  3. Enabling electronic health records using DID

Transforming KYC using DID

Kyc Process Flow

The KYC Process in Banks and Financial institutions is mandated by the government and can be quite painful both for the bank and the customers. We examine how DID can help not only simplify the process but also ensure high trust and make the process fraud proof, by eliminating intermediaries and returning to trusted direct relationships. Download Complete Research

Perishable supply chain using DID

In case of Food supply the application of IoT can provide real-time data and insights. The current IoT supply chain and the food Supply chain face innumerable challenges.  Using DID and verifiable credentials in food/perishable supply chain can provide a tamper-proof and auditable record of a product’s journey, from its origin to its destination. Solution will have lasting impact not only for controlling quality and expense for organization but will also have impact on public nutrition, health, and sustainability. We explore how!

Enable electronic health records using DID

While managing Electronic Health Records, healthcare organizations face two main challenges: Privacy & Security and Interoperability due to multiple systems in play. By providing patients with greater control over their health information, Decentralized Identity solutions can enhance trust and confidence in the healthcare system, leading to better health outcomes. We explore the details with an example. Download Complete Research

Credits
Author@lab45: Sujay Shivram, Abhigyan Malik

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