Reusable rockets & satellite miniaturization collaboratively are democratizing access to space. Fuelling innovation across multiple domains and fostering a future where space is accessible to enterprises, individuals, and nations alike.

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What’s inside

1. Space race
2. The economies of reusable rockets
3. Space X Falcon 9 Launch
4. Miniaturization and cubesats
5. Transforming agriculture with CubeSat
6. Further innovations brought in by CubeSats.
7. Business models in the next generation of space services

Space race

The section delves into the historical roots of the space race between the United States and the Soviet Union, highlighting its profound impact on technological advancements. It explores how the competition between these superpowers catalyzed innovations that have become essential in various industries, including telecommunications, healthcare, and more. The section also discusses how the end of the space race didn’t mean the end of innovation but rather heralded a new era of technological advancements that continue to shape our world today.

The economies of reusable rockets

This section focuses on the economic revolution brought about by the introduction of reusable rockets. It discusses how companies like SpaceX have drastically reduced the cost of space exploration by developing rockets that can be landed and reused. This has made space more accessible to not just governments but also private entities and educational institutions, thereby democratizing space exploration. Download Complete Research

The Aerospace Security Project (ASP) in Sept ‘22 conducted an analysis of the cost of payload launches throughout history. The cost of heavy launches in LEO have been reduced by almost 95% from $65,000 to $1,500.

SpaceX falcon 9 launch

The section provides an in-depth look at SpaceX’s Falcon 9 rocket, one of the most iconic reusable rockets in the industry. It discusses its design, capabilities, and the groundbreaking technology that allows it to be reused, making space travel more economical and sustainable. The Falcon 9 serves as a case study for the potential future of cost-effective space exploration.

Miniaturization and CubeSats

This section explores the trend of miniaturization in satellite technology, focusing on the development of CubeSats. These small, modular satellites have opened up new avenues for space exploration and commercial applications. Their smaller size and modular design make them more cost-effective and accessible, allowing for a wider range of applications and participants in space technology. Download Complete Research

Transforming agriculture with CubeSat

The section discusses how CubeSats are revolutionizing agriculture by providing high-resolution imagery of farmland. This technology enables more efficient and effective resource allocation, transforming traditional farming methods. CubeSats are making precision farming more accessible, allowing for optimized use of water, fertilizers, and workforce, thereby making farming more sustainable and cost-effective.

Further innovations brought in by CubeSats

This section delves into the additional innovations that CubeSats have enabled, beyond their initial applications. It discusses how these small satellites serve as testbeds for new technologies and scientific research, from studying Earth’s atmosphere to testing new materials and communication technologies. CubeSats are paving the way for future advancements in space technology. Download Complete Research

Business models in the next generation of space services

The section explores the emerging business models in the space industry, particularly focusing on the ‘As-a-Service’ models. It discusses how these models are making space technology more affordable and accessible, from Data-as-a-Service to Mission-as-a-Service. These new business models have the potential to further democratize space technology, making it accessible to a broader range of participants.

Credits
Authors@lab45: Abhigyan Malik
Key contributors: Ariel Zajdband, Planet

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

1. Decentralized vs Centralized ID for governments
2. Identity credential issuing process today
3. Suitability of government functions to Decentralized Identity
4. 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

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.

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