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Anthropic’s Claude 2 – A Leap Forward In Contextual Understanding

Anthropic’s recently updated flagship AI offering, Claude 2, comes with a promise to redefine the landscape of large language models, boasting a dramatic increase in context window size to a staggering 100k tokens. While the AI industry has been abuzz with excitement over this development, it’s important to delve deeper and understand its implications, both positive and potentially not so positive.

The Game Changer: 100k Context Window

Claude 2’s most striking feature, and its principal differentiator, is undoubtedly its 100k context window. This is an order of magnitude improvement over prior state-of-the-art models, allowing it to retain and consider much larger chunks of text before it ‘forgets’. The implications of this enhancement are profound and far-reaching (though there is already talk of infinite context windows).

In practical terms, this means Claude 2 can process and generate coherent responses to much longer inputs, such as academic papers or lengthy reports. As an example, Claude 2 was able to generate a succinct and accurate summary and review of my masters thesis on Sustainable Venture Capital:

Overall, this thesis makes a valuable contribution to the emerging field of sustainable venture capital. It explores the intersection between venture capital and sustainability through a mix of literature review, survey research, and database analysis. The central research question – “What actions should venture capital firms take to ensure sustainability of their investments and themselves?” – is timely and pertinent.

The literature review provides helpful context by summarising key insights from previous academic research on topics like business angel decision-making, portfolio diversification, power law distributions, and corporate venture innovation. The author makes apt connections between these areas and sustainable venture capital. The review of research on the United Nations Sustainable Development Goals (SDGs) is particularly insightful.

The survey research component is a strength of the thesis. The author designed a well-constructed questionnaire and gathered responses from over 100 potential investors. Statistical analysis of the data reveals interesting findings regarding investing preferences across dimensions like gender, generation, and risk appetite. The top motivations and SDG priorities identified provide practical insights into investor sentiment.

The database analysis on companies, funding, and exits further contextualises the current state of sustainable venture capital. While the data accessibility issues faced are a limitation, the author was still able to extract useful quantitative insights. The assessment of sustainable venture capital firm performance is quite preliminary but suggests an avenue for future research.

The key recommendations around structuring decisions, diversification, metrics, corporate innovation practices, SDG utilisation, and legal structuring demonstrate practical implications from the research. The author does well to consider both investor and venture firm perspectives. The conclusions effectively synthesise the main findings and highlight opportunities going forward.

In summary, this thesis executes an ambitious mixed-methods research design with care and rigour. It makes several important empirical and practical contributions that advance understanding of sustainable venture capital. The limitations in data access and scope provide opportunities to build on this work through expanded sampling, statistical testing, and tracking company performance over time. Overall, a promising early effort to examine this increasingly critical domain.

Claude 2’s review of my Sustainable Venture Capital masters thesis.

This huge context window is a feature that will prove invaluable to researchers, academics, and professionals who frequently work with complex and lengthy documents.

However, it’s not just about managing larger texts; it also fundamentally improves the coherence and relevance of the AI’s output. The ability to maintain context over longer interactions can lead to more cogent and insightful conversations, and permit deeper exploration of complex topics. This is a significant step forward in the quest for more human-like interaction with AI.

Safety and ‘Constitutional AI’: A Double-Edged Sword

While the expansion of the context window is undeniably impressive, Claude 2’s focus on safety and its ‘Constitutional AI’ approach may be less universally lauded.

Anthropic has made clear efforts to address the existential threat posed by unfettered AI, taking a safety-conscious approach designed to prevent misuse and harmful outputs. This is commendable, to an extent. The AI industry needs guardrails to ensure responsible use and to protect against inadvertent creation of harmful content.

However, Claude 2’s opinionated approach, which Anthropic describes as ‘Constitutional AI’, may not resonate with all users. By setting firm boundaries on what the AI can and cannot do, there’s a risk of stifling creativity and limiting the AI’s potential uses. While it’s undoubtedly important to ensure safety, it’s equally critical to strike a balance and not overly restrict the AI’s capabilities.


In summary, Claude 2 represents a substantial advance in the field of generative AI, primarily due to its 100k context window. This feature opens up new possibilities for complex, in-depth interactions with AI that were previously unattainable. However, its safety-focused approach and ‘Constitutional AI’ may not be to everyone’s taste. While safety should never be compromised, it’s important to ensure that this does not come at the expense of the AI’s potential.

Anthropic’s Claude 2 is a fascinating glimpse into the future of AI, demonstrating the potential to dramatically improve the utility and capability of language models. However, striking the right balance between safety, utility, and versatility will continue to be a key challenge in the development of AI.