An engineering approach to Venture Capital (We are hiring!)
Building a computer aided venture investing machine (CAVI)
The venture capital industry has always been about turning imagination into reality. As we step into the future, we see an opportunity to build upon this tradition by integrating the power of emerging technologies into everything we do. Today, we'd like to share a high level vision of Inflection’s future: a deep tech venture firm that pairs human intuition and judgement with cutting-edge AI and machine learning technologies to revolutionise how we identify and support innovators through networks, capital and insights.
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Vision: A computer aided venture investing machine (CAVI)
At its core, a VC firm is a decision-making and insight machine, an amalgamation of mental models, frameworks and human intuition. We believe this machine can be enhanced and optimised. We envision a world where VC operations - currently largely manual - transform into an elegant synergy of human and machine, powered by data-centric processes and automation. We called this concept CAVI - computer aided venture investing. This fusion will enable us to leverage our most valuable asset – human attention – more efficiently to focus on non-fungible activities such as relationship building, ecosystem building or creative thinking in dynamic environments with incomplete information.
Here’s what we’re going after: Imagine the first time you held an iPhone or the first time you were recommended exactly that song you wanted to listen to but didn’t know it yet. That is the kind of step function improvement CAVI can bring about in venture capital.
Creating a well-designed decision making system helps to mitigate the influence of personal biases and emotional factors, allowing for more objective and consistent outcomes.
Paraphrasing Ray Dalio
Up until now, it was really hard to be very data-driven and tech-first in venture (as public interviews with some of the larger funds will tell you). The data is inconclusive, mostly it’s people and ideas, without little quantitative support to indicate correlation with returns. Research and contributions have been made by other funds - large and small - going from standardised personality tests of founders to predicting performance by proxy in website visits. We want to build on these efforts and take it to the next level. The exponential advancements in AI, machine learning, NLP coupled with the increasingly ubiquitous access to data have opened up unparalleled opportunities to reimagine the VC landscape.
Those are some of the core drivers paving the way for such shift:
Text processing and manipulation is leaps and bounds ahead of where it was just six months ago: The introduction of GPT-4, the latest language model from OpenAI has made it dramatically easier to analyse and extract insights from text data.
More data is available than ever before: The digital era has created an explosion of data. This ever-growing wealth of information can be harnessed to provide a much deeper and broader understanding of research models, startups and markets.
Emerging venture funds are redefining the playing field: The traditional VC model has its strengths, but it also has limitations. As the startup ecosystem continues to grow and change, our industry must also adapt to remain effective.
In conclusion, the time is ripe for a more data- and automation driven approach to venture capital. By combining the power of advanced technology with the irreplaceable human element, we can create a venture engine that is scalable, efficient and effective in today's fast-paced, data-rich world. If you’re more interested in the broader topic in general we highly recommend followingand the community.
A starting point - decomposing the VC value chain
Before we dive into how the venture capital firm of the future could look like more concretely, let’s explore its underlying principles and analyse how the core of its value chain might be affected by the ongoing compute revolution.
This is one out of many ways to define a venture capital firm’s key functions. In  we defined where the function sits on the spectrum of automation potential where 0 = non fungible human competency and 10 = fully automated. We used a rule of thumb framework taking into account (1) reliability & availability of high quality data allowing for extrapolations into the future (2) required creativity and intuition in highly dynamic environments with incomplete information, (3) required human coordination with external human stakeholders to fulfil the function.
Thematic Research 
Where to look at; which hay stack to explore.
Inputs for this function could be
viability: research breakthroughs, technology maturity, technological limitations like scale, dependencies
catalysts: convergence, amplification through other emerging technologies, regulatory risk and incentives, demand drivers, geo political and macro economic forces
impact: degree of novelty, superiority vs. incumbent solutions, societal and economic implications
market: large problem set yielding multi billion dollar market opportunities within 5-10 years from investing
popularity: controversial sentiment (we have to be contrarian & right), low investment volumes, low web traffic and media coverage
While some of those inputs can be pulled from research institutions, open source research platforms and the like, qualitative insights might need to be non fungible like expert interviews, hence the 7/10.
Sourcing & Pre-Screening 
What to spend time on; which needles in the haystack might be worth analysing.
This function is tricky. If the funnel is too narrow the firm might lose out on potential outliers. If it is too broad it drowns in noise and struggles to focus.
Inputs to this function include:
network analysis: connections between people; connections between people and organisations; connections between people and ideas / problem sets
Outbound sourcing will be informed by thematic research as we invest with low velocity and high conviction. Our outbound sourcing filters will be set accordingly what can be strongly technology assisted. On the other hand, a variety of other factors will play a rule, such as (1) thematic content creation and events, (2) establishment of a scouting and mentorship network for soon-to-be founders etc. which have more of a human touch. Hence the 8/10 score.
Recommended further reading: More needles, bigger haystacks: What we mean when we talk about data-driven VC.
Investment Decisions 
What to invest into; which needles to pick from the haystack.
This function lies at the very core of our business. Over almost a decade we developed frameworks, score cards and various heuristics to take maximally informed decisions optimised for upside potential in the early life cycle of a company.
Inputs to this functions include (over-simplifying here; each of those bullets can be universe in itself):
Team: personal fit; complementary to existing portfolio founders; shared vision & purpose; deep co-working history; alien skills; obsession with problem space; history of outstanding achievements; leadership that attracts elite talent; resilience and grit
Product: fundamentally revolutionary approach to solving the problem (0→1); enabling something previously impossible; strong first- or early mover advantage; unique technical moat (network effects; brand)
PMF: 10x improvement for customers (faster, better, cheaper); high retention; high product velocity; validated demand in target market
Market: Large ($10BN+) current market and / or growing rapidly; blue ocean or unique approach to gain market share; ability to capture value demonstrated; business logic allows for strong network (ownership) effects and defensible valuation; clear monetisation strategy
Terms: pre-seed or seed valuation; ownership target of X; 100x upside potential; complementary potential syndicate; 24+ months of runway post financing; clean set up (no previous debt, side deals or skewed incentives
Note: The more early stage (read pre product or MVP stage) a fund operates the more weight needs to be put on research (see above), market and team. Diligence on most of these items can only be automated to a limited degree, hence a 4/10 score for us as a first-check fund. This might look vastly different for later stage funds harnessing much more data (number go up!).
Deal Execution 
Pitching our firm to get an allocation; Term negotiations; Due Diligence; Syndicate construction.
This function is one of the more non-fungible ones we can image as it is profoundly related to human relationship and trust building. For the more formal parts including the review of data rooms and legal documents or finding ideal syndicate partners technology might be helpful. Hence, 4/10.
Founder Support & Platform 
Strategic Sparring; Board Roles; Founder Coaching; Go to market; Pricing Strategies; Pivots; Key hires; Business Development; Shared Resources; Best Practices.
This function is critical to support our founder ecosystem. Historically, it has been the most non-fungible activity a venture firm’s partners have been involved in (besides fundraising). We are of the belief that this is about to change. Most of the problem spaces mentioned above can be approached through frameworks. The underlying problem spaces are unique in some ways as every company, team and market conditions are different - but at the same time structural patterns remain similar.
network analytics to identify key hires
Custom trained LLMs to relay specialised knowledge
Shared software- and data infrastructure
E.g. every company finding product-market-fit will define modular experiments to test hypothesis and measure success. How this can be done best is specialised knowledge which can be sourced from the public domain and extended through expert-input. Aggregated insights could be packaged in a custom data set and made accessible through LLMs for example.
Artificial agents helping innovators to navigate mission critical challenges will seem like a natural expansion of what venture investors do today. Through data-network-effects (knowledge is a network) such services will improve over time and can provide a competitive edge for an entire ecosystem.
Fund Operations 
Portfolio Modelling; Investor Reporting; Investor Relations; HR; Legal; Finance; Accounting
These functions are very broad and currently necessitate a lot of human engagement and coordination with external stakeholders.
Inputs to this function include:
Strategy: portfolio modelling and construction; sensitivity analysis
Tracking: Portfolio performance tracking; automated quarterly updates; Queries for LP requests
Audits: Preparation of audit reports; facilitation of audit memos
Legal: screening & summarising long winded legal documents
Finance: Cash flow planning; expense analysis
Fundraising: LP discovery
Given the vast breadth of operations and tooling involved the software and data stack for such functions is typically is fragmented. Hence, the optimisation potential is very high but complex - 9/10.
Putting it all together we can draw an analogy to Hans Moravec’s framework comparing computer performance with water slowly flooding the landscape. We wonder what this landscape will look like in a decade from now… in the meantime we are building Arks!
Computers are universal machines, their potential extends uniformly over a boundless expanse of tasks. Human potentials, on the other hand, are strong in areas long important for survival, but weak in things far removed. Imagine a “landscape of human competence,” having lowlands with labels like “arithmetic” and “rote memorization,” foothills like “theorem proving” and “chess playing,” and high mountain peaks labeled “locomotion,” “hand-eye coordination” and “social interaction.” Advancing computer performance is like water slowly flooding the landscape. A half century ago it began to drown the lowlands, driving out human calculators and record clerks, but leaving most of us dry. Now the flood has reached the foothills, and our outposts there are contemplating retreat. We feel safe on our peaks, but, at the present rate, those too will be submerged within another half century. I propose that we build Arks as that day nears, and adopt a seafaring life!
Hans Moravec, “When Will Computer Hardware Match the Human Brain?” Journal of Evolution and Technology (1998), vol. 1.
We are hiring a senior engineer!
Onwards & upwards
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