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

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
2. Perishable supply chain using DID
3. Enabling electronic health records using DID

Transforming KYC using DID

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

The banking sector is experiencing significant changes primarily driven by the growing integration of technology into consumers’ daily lives, evolving customer expectations, increasing interest in digital money and the volatility of cryptocurrencies. The potential annual value of AI and analytics for the global banking industry is expected to be as high as $1 trillion.

What's inside

1. Key takeaways
2. Key drivers shaping trends in banking
3. Business trends driving innovation
4. Technology trends aiding the business

Key takeaways

• Changing IT spend- IT spending in banks is shifting from Capex to more.
• Opex, with significant shifts to Cloud.
• Future of banks- Neobanks can play a crucial role in addressing and responding to all the key drivers.
• Rise of fintechs-The emergence of fintechs is shaping business trends and expanding the range of choices available to customers. Studies indicate that from now until 2028, the growth rate of fintech companies is expected to be three times that of the banking industry as a whole.
• Impact of emerging technologies- New technologies like GenAI, Blockchain, IoT are likely to cause banks to change the way they work and will influence the majority of the business.
• Cybersecurity resilience- Cybersecurity is evolving from being solely a technological concern to becoming an important consideration for new business strategies.
• Sustainability increases in priority-While Sustainable finance enables banks in financing sustainable projects for other businesses, banks must also prioritize making their own operations sustainable. Download Complete Research

Key drivers shaping trends in banking

• Customers want personalized, convenient, and seamless banking experiences. Over 60% of banking executives report rising customer experience expectations, with 45% struggling to keep up.
• Fintechs are revolutionizing banking with mobile apps, online lending, and personalized experiences using AI. By 2030, they will be constituting 25% of all banking valuations.
• To address environmental risks and foster responsible economy, banks are focusing on sustainability. Banks representing 41% of the global banking assets have joined Net-Zero Banking Alliance.

Business trends driving innovation

• A lot of the trends are reflecting the move to extended value chains or ecosystems thinking.
• Technology platforms support a lot of this, and banks and financial service providers are seeing the benefits of these.
• BaaS allows third parties to connect with a bank’s API infrastructure to build and integrate products.
• With automation, banks can now reduce their lending processing time from weeks to a couple of days.
• Neobanks can operate on a low-cost model, which can be instrumental in improving the accessibility of banking services.
• With Open Banking, banks can now open online accounts in just three minutes, 100 times faster than before.
• These banking trends impact various stakeholders, including customers, regulators and government bodies, employees, technology providers and fintechs.

Technology trends aiding the business

• Banks can leverage AI to gain deep insights into their customers and the financial ecosystem, identifying new fraud patterns and money laundering strategies using synthetic data.
• Banks are migrating their analytics platforms to the cloud for complex banking analytics. Data marts are being used to store and perform analytics on sensitive bank data.
• Recent developments in cybersecurity in banks, like Zero Trust Architectures and Adversarial ML, help train ML models to be more resilient to attacks and increase cloud adoption.
• Blockchain technology in banking promotes secure digital transactions, cost reduction, decentralization, and anonymous financial activities while ensuring accountability.
• IoT implementation helps to monitor each customer touchpoint in realtime when they bank, enabling banks to offer more relevant services and identify fraudulent activities faster.

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Credits
Lead Authors@lab45: Deepika Maurya, Chandan Jha
Contributing Authors@lab45: Sujay Shivram, Hussain S Nayak