An Investor and a Practitioner’s Perspective on GenAI

We recently hosted a conversation with over a hundred CXOs from our network as part of our ongoing efforts to bridge the gap between the promise and reality of GenAI. IT leaders across all industries are already thinking about what their top of mind priorities will look like, including how they plan to educate and experiment, manage their blue sky planning, update data governance policies, and so much more.

Key Takeaways

  • A recent survey in McKinsey suggested that the technological advances we’re seeing in AI today are ten years too soon for a variety of different use cases. This includes rote tasks such as math or coding, but also cognitive activities that are further afield, like dialogue, video generation, and a higher order of understanding and interpreting natural language.
  • AI won’t necessarily replace humans, but humans utilizing AI may be substantially more productive than humans who aren’t. And because of the human aspect of all of this – the markets that AI will be replacing have to be considered differently. People tend to look at markets and say: “What is the TAM for accounting software?” But in the case of AI, they’ll have to say “What is the TAM for accountants?”
  • We’re seeing a bimodal distribution of funding. Some startups with just a couple million dollars can use gen AI to get to an MVP really quickly because they’re using open source tools or publicly available datasets. On the flipside, if you created your own model you have to raise a lot more, like a $20-$40M seed raise before you can even start selling anything. So the bifurcation is really between companies who are building models vs. those just taking things to market directly.
  • The same way that mobile devices in the past led to the consumerization of IT, generative AI is leading us in the direction of the consumerization of search. Unstructured content and documents can now be used to make customers and employees better informed and more intelligent.
  • CIOs will need to project where their specific IP is going to offer advantages and how that will play out across their industry. This is the first step in defining a game-changing LLM strategy. The second piece of that will of course be cleaning and prepping data for those private LLMs.
  • The abstraction of the stack results in selling to different personas – Back in 2014, the original AI stack was quickly abstracted away. You needed to be an AI expert and know algorithms and also hardware. But then, CUDA came along and you could write algorithms on top of that. And with PyTorch and TensorFlow there came another layer of abstraction. Then finally, with MLOps and SageMaker, eventually you could go directly to apps to write things. So you went from selling to the data scientist, to analysts and business users. And from a core engineering perspective it was interesting to see how different tools developed for different personas.
  • There has been an evolution from AI- Centrism to Data-Centrism – Many companies started out as AI-centric companies, but after applying models in practice, quickly realized that those models weren’t fitting their use cases properly (leading to many companies becoming more data-centric). This was a difficult pivot for them, but that transition did need to happen. Data-centrism is more about how to fit the right models to the data. What kind of model needs to be used? How is the data being collected?

Key CXO Considerations for CXOs:

Data governance

  • Where is your IP going? Where can it be used?
  • Make sure to review SaaS T&S
  • How are you accelerating data quality?


  • What are your employee use cases and platforms? What tools are being used in what ways?
  • How are you validating results? Make sure everyone is helping expose what problems and opportunities are worth solving for
  • How are you addressing safety and regulatory?


  • To your board and leadership: What is your clear and articulate AI strategy that’s updated on a quarterly basis?
  • To your customers and partners: What are you experimenting with as a company today? What’s your roadmap for tomorrow?
  • To Employees: Where are there learning opportunities they can take advantage of?

Stay tuned for upcoming conversations as we all navigate this exciting and changing landscape together.

Accelerating GenAI Adoption Through a Data Operations Cloud

Revefi Announcement

I first met Sanjay Agrawal and Shashank Gupta in the summer of 2021. They had built storied careers at companies like Amazon, Google, Meta, Microsoft and Yahoo, solving tough data analytics and infrastructure problems. They were part of the founding team at ThoughtSpot, one of the industry’s pioneering business analytics companies. They had decided to join forces to pursue their passion around data and embark on a new journey as CEO and CTO of Revefi, a next generation data operations cloud company. We led their $10.5 million seed round and partnered with them as they built the team and product.

Today, they have announced the Revefi Data Operations Cloud, which serves as a zero-touch co-pilot to data teams to monitor data quality, spend, usage and performance. By enabling data teams to get the right data into cloud data warehouses reliably, promptly and affordably, their organizations are able to use the data to make critical business decisions. They have already found product-market fit with a no POC/self trial model, and are demonstrating measurable ROI. Within weeks, one customer saw six figure savings, by using Revefi to uncover insights that allowed it to save nearly 30% on its total data warehouse spend while at the same time increasing its usage of the cloud data platform by 35%.

Three reasons I am excited about the next phase of the Revefi journey:

  1. Data quality is a mission critical problem that has only become more real in the age of AI: According to Gartner, an enterprise incurs an yearly loss of $13-15M on an average due to data quality issues. According to Forrester, less than 10% of respondents believed data met quality standards. According to HBR, it costs ten times as much to complete a unit of work when the data are flawed in any way as it does when they are perfect.
  2. Elevating data ops teams with a zero touch model will unleash their power.
    Data Ops teams spend a significant amount (in one case it reached a high >40%) of time on investigating and root causing SLA violations, and other data quality issues. With the exponential increase in the amount of data in the age of AI, and an ever-increasing number and variety of data sources, this pain is getting worse. On the flip side, if a business user sees a business-critical report where data does looks unexpected, the process of figuring out whether it’s even a problem, and if yes where (whether it’s in data, or semantics), will it resolve on its own (data is delayed) or needs intervention (unexpected nulls), results in more tickets against and thus more load on the Data Ops teams.
  3. Building a company, vs just a product, is a superpower of serial entrepreneurs who understand the value of institutionalizing culture and surrounding themselves with excellence.

We look forward to partnering with Sanjay, Shashank and the Revefi team as they accelerate the adoption of GenAI.

Welcoming our First Dedicated Select/Spring Fund Partner Sri Pangulur

Sri Pangulur Announcement Image

I’m excited to welcome Sri Pangulur, a proven enterprise infrastructure, developer tools,  and SaaS investor and operating leader, to the team as our newest and eighth partner. Sri will be our first dedicated partner for Select/Spring Funds. He will be partnering with entrepreneurs focusing on enterprise infrastructure, AI, developer tools, and SaaS. These companies will be primarily at the Series B stage and we will invest in them out of our Select/Spring funds, which represent approximately $800 million of capital. To date, we have invested in 23 companies from our Select Funds across all layers of the technology stack, and announced our new $375M Select III/Spring Fund in May of this year.

120 IPOs, 550 Investments, 225 M+As, 20 U.S. Funds, $3B Under Management, Founded 1969

Prior to Mayfield, Sri led the enterprise software investing practice at Tribe Capital and was an active angel investor before that. Some entrepreneurs he has partnered with include the teams from, LinearB, Docker, JupiterOne, Orum, Instabase, MindsDB, Abnormal Security, Hasura, HYCU, Nylas, Airbyte, Hightouch, and Oort.

During his operating career, Sri held sales and business development leadership roles across multiple enterprise infrastructure and SaaS companies. Prior to his operating tenure, Sri was a member of the investment banking division of Barclays Capital.

The team at Mayfield is collaborative and focused, where every new member makes a significant impact toward our firm’s success. Sri embodies the Mayfield Way, a set of of operating principles that guide our firm, and is a perfect addition to our team for the following reasons:

  • The founders he has worked with highlight his empathy for their journey, which is a key pillar of our People-First philosophy;
  • His unique combination of recent operating and investing experience enables him to bring a both-sides-of-the-table mindset when guiding entrepreneurs;
  • He complements our inception stage focus with expertise for companies a little further along. Key areas he has contributed to include rounding out executive teams for the next stage of growth, upleveling founder narratives for successful future financings, and leveraging his recent operating experience to build scalable go-to-market strategies, high-performance organization structures and business models;
  • He is not a fan of the recent *steroid era of investing* and brings a craftsperson approach which is a signature of our firm.

Sri outlines some reasons for his excitement in joining our team including:

  • Having known us personally and by reputation for a long time as a focused and high-performing team with a track record of success;
  • Our People-First culture and willingness to follow our own North Star;
  • The greenfield opportunity to further build our Select/Spring investment practice.

Sri joins us during a momentous year at Mayfield, one during which we raised $955 million in May across our oversubscribed Mayfield XVII and Mayfield Select III/Spring Funds in record time, and also announced our $250 million AI Start Seed Fund in July. 

We look forward to partnering with bold entrepreneurs on their inception to iconic journeys.

A People-First View of the AI Economy | TechCrunch

Mayfield Announces $250 Million AI Start Seed Fund and Adds New Partner

Mayfield, a top-tier early-stage venture capital firm, today announced the $250 million AI Start, the first seed fund in its history, to partner with AI-first founders starting at Day Zero. The Firm also added Vijay Reddy, an AI-focused investor with a decade of successful seed stage experience, as a dedicated AI Start Seed Fund partner.

Mayfield Launches New $250 Million AI-Specific Seed Stage Fund

Longtime venture capital firm Mayfield Fund is launching a new investment vehicle specifically to back artificial intelligence startups.

Announcing The $250 Million Mayfield AI Start Seed Fund

Today we are announcing the $250 million Mayfield AI Start, our first seed fund, out of which we will invest in AI-first founders starting at Day Zero.

2023 Mid-year Review

Sharing highlights from the past 6 months including $955 million raised across our two latest funds, founder journeys & insights, our Managing Partner’s 15th appearance on the Midas List and the Mayfield Way – it’s been a wild ride in Silicon Valley (to say the least), but we’re eternal optimists in the power of entrepreneurs to build a bright future.

MindsDB: Introducing the World’s First Cloud to Serve AI Intelligence Logic

Having worked in tech as an entrepreneur and investor for over 25 years, I remember the exuberance of the Web era of the mid to late 90s, which yielded enduring enterprise software companies. The hallmark attributes of many giants were that they had a painkiller value proposition, combined revolutionary approaches that integrated into existing architectures, and enabled enterprises to leverage their current developer resources. Today, we are squarely in the AI-first era, with enterprises rushing to adopt the benefits of AI. With the announcement of our lead investment in MindsDB’s $25 million financing round, I am proud to welcome Jorge, Adam and the MindsDB team to the Mayfield family. I believe that as the industry’s first cloud to serve AI intelligence logic, they will play a role similar to the legendary web application server BEA Systems/Oracle and supercharge enterprise adoption of AI. 

To understand the role of an AI intelligence logic cloud, we need to go back in history to look at the idea of a three tier architecture, a well-established software concept that organizes applications into three logical and physical computing tiers: the presentation tier; the application logic tier; and the data tier, where the data associated with the application is stored and managed. During the web era, the presentation tier was dominated by web servers on which websites were built; the middle tier by application logic servers which housed the logic and transaction capability; and the data tier by databases. In today’s AI-first world, there’s a lot of activity on the presentation tier with consumer interfaces powered by multiple AI frameworks; data repositories have grown beyond SQL to include many NoSQL and data lake leaders; but the middle tier of a unified cloud to serve AI intelligence logic is still nascent.

MindsDB, which grew out of an open source project started over five years ago, is growing into the must-have middle tier cloud to serve AI intelligence logic, driven by three attributes:

  • A framework- and data-agnostic stance (currently supports 10+ front end AI frameworks and 100+ data sources);
  • The technical breakthrough of adding AI tables to existing databases which enables users to identify patterns, predict trends, and train models;
  • A data-centric approach that eliminates the need for ETL and minimizes data exposure risks.

As a result, existing developers can grow into an army of AI engineers who quickly deliver production-ready applications, thereby enabling enterprises to generate revenue, but also control costs by leveraging in-house talent. 

Building on the momentum of its open-source project, MindsDB has achieved several key milestones:

  • Adding more than a hundred platform integrations, including with big tech players like OpenAI, Hugging Face, Snowflake, MongoDB, Databricks.
  • Being recognized as one of Forbes’ Top AI 50 Companies and named as a Cool Vendor in Gartner’s 2022 Data-Centric AI and multiple Hype Cycle reports.
  • Transforming into a mature open-source project with more than 500 code contributors, 16k+ stars from the community and more than one hundred thousand installations.
  • Launching a Cloud Enterprise version, proven by tens of thousands of developers.

We believe that MindsDB is well on the evolution path from open source phenomenon to a must have cloud that serves AI intelligence logic to enterprises. This will firmly establish it as a key player in the AI-first landscape, similar to how BEA powered an entire class of web apps. We are excited to partner with the MindsDB team and investors and look forward to their journey from inception to an iconic company.


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