Investment Strategy Archives

Nvidia’s Investment Strategy is Fueling Tomorrow’s AI Winners

April 14, 2024

In recent years, Nvidia has transitioned from a pioneer in graphic accelerator chips to a key architect shaping the future of AI. Though it looks like an overnight success story, like most uber-successful companies, this one has been decades in the making. Like other big tech companies, Nvidia has adopted a multifaceted approach of investing, partnering, and hiring great talent, thereby fostering innovation within the early-stage AI and big tech ecosystems. By leveraging its resources and expertise to invest in and partner with early-stage AI companies, Nvidia is fueling innovation and strategically positioning itself as a linchpin in the AI revolution. And at Madrona, we’re excited to partner with a company actively investing in creating tomorrow’s AI success stories.

Since its inception in the early ’90s, Nvidia has been revolutionizing the way developers utilize graphic chips for computing. But Nvidia catapulted itself to the forefront of technological innovation with its 2006 introduction of CUDA, a groundbreaking GPU architecture allowing direct programming in C to accelerate mathematical processing and simplify its use in parallel computing. From then on, Nvidia has been at the forefront of technology inflection points, including gaming, crypto, autonomous driving, and, most notably, AI, solidifying its position as THE leader in the compute and networking space.

Throughout that work, Nvidia has become a crucial ally for early-stage AI startups, providing not just the computational firepower needed to explore new frontiers in AI but also strategic investments that can turbocharge their startup journeys. As Nvidia continues to lead the charge in compute and networking, its latest role as an investor is reshaping the AI ecosystem. This proactive approach has accelerated the growth and innovation of these startups and ensured that cutting-edge AI technologies reach consumers and businesses faster.

Nvidia’s Investment Strategy

Nvidia’s investment philosophy is clear: support companies that leverage its technologies, thus cementing its leadership in the AI market where its chips are nearly ubiquitous. The success of this strategy is evident in Nvidia’s staggering growth. In the past year, Nvidia’s annual revenue grew 265%– from $6B in FY2023 to $22B in FY2024. It should be no surprise that Nvidia has the third largest market cap in the world — exceeding $2.3T in March — just behind Microsoft and Apple. Just five years ago, the company was at about $109B.

Nvidia’s aggressive investment strategy, highlighted by its backing of more than 100 companies over the last five years, according to Pitchbook, underscores its commitment to fostering innovation within the AI sector. Nvidia nearly tripled its activity from 2022 to 2023 and has already invested in eight companies in 2024, including two Madrona portfolio companies — Unstructured and Cohesity. Others include Perplexity, Cohere, Coreweave, Mistral, Together AI, and Imbue.

Madrona’s Partnership with Nvidia

Nvidia’s strategic approach signifies a profound understanding of the transformative potential of AI and a commitment to shaping its future. Recognizing Nvidia’s impact on nascent startups, Madrona has partnered with Nvidia on three other investments in addition to Unstructured and Cohesity — Runway, Deepgram, and Terray Therapeutics. Madrona also partnered with Nvidia as part of the Nvidia Inception program and the Nvidia VC Alliance Program, which provide our portfolio companies access to collaboration opportunities with Nvidia.

Terray Therapeutics

Jacob Berlin, CEO and co-founder of Terray Therapeutics, a small molecule drug discovery company, felt “pretty good” about its AI model Coati early last year when it was first trained. It was functional, but it could be better, he said. However, after Nvidia announced its investment in November, Terray retrained the model entirely, armed with boosted computing resources and Nvidia’s engineering prowess. Jacob told Forbes his company saw much better performance, “and we couldn’t have gotten there without the collaboration with Nvidia and their support.”

Cohesity

Cohesity, a leader in AI-powered data security and management, partnered with Nvidia to unlock the power of generative AI and data using Nvidia NIM microservices and by integrating Nvidia AI Enterprise into its Cohesity Gaia platform. After this announcement, which included announcing Nvidia as a new investor, Cohesity CEO Sanjay Poonen shared that the partnership was a testament to the promise Nvidia sees in Cohesity as a leader and innovator. “Through this collaboration, technology and business executives will have a unique opportunity to leverage the power of generative AI, turning information into knowledge while keeping their data both compliant and safe,” he said.

Unstructured

Brian Raymond, CEO and Founder of Unstructured, the leader in ingestion and preprocessing for LLMs, was excited to partner with Nvidia for its recent $40M Series B because he recognized the acceleration the partnership would provide for his startup. Unstructured utilizes a variety of models to extract, classify, and embed unstructured data, and working with Nvidia will accelerate the work the engineering teams are doing to optimize its ML pipelines for enterprise speed and scale. Brian shared that Unstructured is also “excited to deepen our support for their Nemo platform, a powerful constellation of tooling that Nvidia is assembling to assist developers in the GenAI ecosystem.”

Buy vs. Build

Amidst the ongoing wave of AI platform evolution, Nvidia’s strategy is part of a noticeable: As each big tech company vies to be the leader in AI, they have become increasingly creative when it comes to their buy vs. build approaches. No longer are big tech companies simply buying startups for the tech they have. Instead, they’re investing in the ecosystem. Microsoft’s investment and partnership with OpenAI — and its recent partnership with Inflection AI. Google and Amazon’s investments in Anthropic. Nvidia and Salesforce’s investment in the early-stage AI ecosystem. And this new multifaceted approach of investing, partnering, and hiring great talent fosters innovation within the early-stage AI and big tech ecosystems. But it’s worth acknowledging that this pivot is likely partly due to the intricate regulatory landscape that has presented its challenges for big tech firms accustomed to more traditional acquisition strategies.

Nvidia Shapes AI Winners

By aligning with nascent AI companies, Nvidia is fueling technological breakthroughs and gaining invaluable insights that fuel its advancements at the hardware and software layers. This symbiotic relationship between Nvidia and the AI startups it supports fosters a culture of innovation, driving progress and pushing the boundaries of what’s possible in AI development.

At Madrona, we are excited to see how the AI landscape will continue evolving with Nvidia’s help fueling tomorrow’s AI winners. We are equally excited to continue partnering with Nvidia on future AI investments. We believe Nvidia is laying the foundation for a future where AI powers change across industries and consumers, further driving innovation at a rapid pace.

How Madrona Supports Founders From Day One for the Long Run

March 28, 2024April 12, 2024

Watch the full conversations on our YouTube channel: Sunny Gupta | Aaron Easterly

When founders are early in their entrepreneurial journey, they often face tough choices about who to raise capital from and what to expect from those investors. If you are fortunate to have compelling founder-market fit, you will likely have several investor options. Investors will try to convince you they are your best long-term partner. Often, this is the first time a founder has started a company and raised capital. It can be hard to distinguish between investor persuasiveness and a genuine ability and commitment to being in your corner from day one for the long run. At Madrona, we believe the best founders need and want great partners for the long, unpredictable, and challenging path to successful company building.

Every year, Madrona’s investors, called Limited Partners, attend our annual meeting. We discuss company and fund progress over the past year and forecast what we expect will happen in the future. This year, we focused on the long-term commitments that we believe venture investing requires to build trust-based relationships and lasting companies.

When investing in founders, we invest in people, their vision/ideas, and the company they formed to deliver a better solution to a customer problem. The founders are also making an investment in our partnership. They trust us to help shape the strategy, open doors to customers, employees, and business partners, and attract the future capital needed to maximize value. “Day One for the Long Run” is a phrase we have used for years to describe our way of working with founders from the earliest days of the journey. No matter our entry point, our long-term commitment is the same: We roll up our sleeves from Day One to help entrepreneurs in an authentic way create a valuable business.

At our annual meeting in Seattle in March, we hosted three fireside chats with founders and CEOs we worked with from the earliest days for more than a decade. Smartsheet CEO Mark Mader, Rover CEO Aaron Easterly, and former Apptio CEO Sunny Gupta talked about their journeys of company building and the lessons learned.

These founders and their companies had moments of despair and uncertainty, but they chose to invest in personal growth and build teams that could overcome the inevitable challenges. Their stories are truly incredible, and we are thrilled to have been there from the beginning to participate in the evolution and support along the way.

From these conversations, there were three consistent themes: A company’s mission should guide its decisions; startup journeys are rollercoasters; and always look around the corner to ensure you are headed to the right destination.

Your Mission Guides Decisions – Big and Small

Building a company is full of twists and turns and endless possibilities to expand or add to product lines or business units. But how do you know which opportunities to pursue and which to let pass by?

Aaron Easterly talked about how Rover uses its mission to make it easier for people to experience the love of a pet to guide their decisions. With their global audience, they could expand in many directions, but with every opportunity or idea they come back to — “Is this an impediment to pet ownership? Would someone not get a pet if this thing didn’t exist?” This focus helped keep their North Star clear.

Similarly, Apptio’s mission to help CIOs show the value they created for their companies (vs. being perceived as a cost center) led directly to building the Technology Business Management category. Apptio knew CIOs would embrace this new approach to transparency and communication. Seeing the opportunity to work closely with their customer group to build this mission broadly, Apptio created the Technology Business Management Council, which attracted CIOs from global brands and Fortune 500 companies to shape industry standards, share best practices, and learn about emerging technologies. This category creation and relationships with the world’s most powerful CIOs led Apptio to a successful IPO and, ultimately, an acquisition by IBM. (Watch the full conversation with Sunny Gupta here.)

Startup Life is a Rollercoaster

Every company that makes it in the long run has some kind of near-death experience. When this happens, you need trusted partners to help you consider the options and make the best decisions possible.

Smartsheet’s near-death experience came in 2008 — amid the financial crisis. It was clear that Smartsheet’s product needed both a front and back-end overhaul. The founders believed strongly in their mission but had to rethink their approach. The big problem was the company was running out of cash, and a rebuild would take significantly more than a couple of months. Madrona also believed in the mission and the people at Smartsheet and chose to reinvest to support the product rebuild. By early 2011, Smartsheet was on a path to success. The company went public in 2018.

“I’m really appreciative and grateful to Madrona for being patient with us. There were a lot of uncomfortable meetings where we weren’t delivering for you, and you stuck with us, as did the LPs indirectly. It really changed a lot of our lives and the lives of our customers.”

— Mark Mader

Rover’s near-death experience was one that impacted many companies — the global pandemic in 2020. A company that had built its business on people going on vacation or going to work while their pets were at home was suddenly in a negative revenue situation. A circumstance that no investor or company leader could have ever anticipated. The team had to make tough expense management decisions and do so quickly. But, Aaron Easterly and his leadership team could also anticipate and prepare for the future. As the world navigated through the worst of the pandemic, they believed there would be a lot more pet parents out there who, having adopted pets in the pandemic, would need help with their new family members. While Aaron isn’t convinced his employees at the time believed him, it quickly became apparent that was the case, and the tailwinds of business from the boom in pet parents came. Rover was purchased by Blackstone for $2.3 billion earlier this year.

“It has been a privilege to have people as long-term oriented as our investor base. When we agreed to sell, something like 30% of our cap table belonged to people who had been in the company ten years or more. That’s not a privilege a lot of people have.” — Aaron Easterly

Look Around the Corner to get to the Best Destination

Focusing on the future – and making well-considered decisions with a long-term view was a consistent theme across all three conversations.

Apptio, uniquely positioned for having gone public and subsequently been acquired twice at increasing company valuations, received numerous offers for purchase throughout the years. Building a category is not easy and not fast. One of these offers came on the eve of going public in 2016 and was significantly above its IPO price. However, the company, with the support of Madrona, believed that there was greater value to unlock as a public company. While life as a public company wasn’t always easy, greater value creation proved to be the case with the purchase by Vista at nearly $2 billion in 2019 and the subsequent sale to IBM in 2023 for $4.6 billion.

“A lot of the lessons I’ve learned have come from my partnership with Matt and the rest of the Madrona team. From day one, I felt that long-term commitment to me as a founder. From an entrepreneur’s perspective, I cannot underscore the importance of that enough. Every day, you’re going to feel lonely. But a committed partner is kind of like having your parent’s unconditional love. And I have genuinely felt that with Madrona.” — Sunny Gupta

Smartsheet remains a public company today, and their performance is judged every trading day and quarter. They have adapted by embracing scalable accountability while maintaining a long-term commitment to their customers and employees. This led them to adopt new technologies such as cloud-native architectures and GenAI that deliver enterprise-grade work collaboration to customers of all sizes. They have also grown their footprint through thoughtful acquisitions and productively evolved by being open to changes and contributions from new team members. Founders should focus on building a solid foundational culture that can adapt and grow as a company scales.

Ask A Founder What They Experienced

When first-time founders ask us to describe our approach to partnering for the long term, we are honored to be able to share stories like the ones above. We also have stories of amazing companies with talented founders whose outcomes weren’t so positive. The lessons from those experiences and how we worked to help those companies have the best outcome possible are equally important. That is why we always encourage entrepreneurs to talk to founders we have worked with in the past and ask them about their relationship with Madrona. Often, the best indicator of what will happen in the future is knowing the authentic experiences of working with a team in the past. We are thankful for the partnerships with outstanding entrepreneurs and long-term investors who help Madrona make durable commitments to companies for the full journey.

The Most Important AI Model Is The Business Model

February 22, 2024April 12, 2024

With the explosion of GenAI and foundation models, there is a growing debate about what the most important AI model will be in the months and years ahead. OpenAI’s GPT-4 is a clear market leader today, but its lack of openness, higher costs, and other customization restrictions make it an especially hard model for others to use at scale in production. Countless models with varying degrees of “openness” are vying to supplant GPT-4, including models from Meta, Amazon, Google, Microsoft, Anthropic, and Mistral. But, these debates are missing the key point. The most important model required to remain an enduring winner in AI is a compelling business model.

The proliferation of AI models has inspired a new generation of companies to leverage them and related technologies to solve customer problems. That customer focus is crucial, but it doesn’t work to just repackage other people’s technology if you’re not providing clear, differentiated value and building a sustainable moat! Many GenAI companies are experiencing rapidly changing fortunes and the need to evolve business models, as we’ve seen with Jasper, Stability AI, and Adept.

The world of applied AI is evolving so quickly that identifying the winning business model will require curiosity, iteration, and agility. Investors who have the company-building experience to recognize past patterns and thoughtfully apply them to this innovation cycle can help in that discovery journey. Below is a framework for thinking about business models, specifically the pricing and packaging approaches used by technology companies in recent eras. Based on previous disruptive technology cycles, including SaaS, mobile, and cloud native, it’s time to consider how these four approaches might apply to AI companies throughout the intelligent application stack.

Creating vs. Capturing Value

A compelling business model enables the creation and capture of value by offering solutions that address specific customer needs. It allows a startup to differentiate itself from competitors — and long-standing incumbents — by providing unique and differentiated offerings. Salesforce.com used this to its advantage with subscription pricing versus Siebel, which used a legacy license/maintenance pricing model. Business models not only facilitate profitable revenue generation but also support scalability and agility in an ever-changing technology sector, ultimately contributing to a company’s growth and success.

Successful business models are developed by exploring several key questions:

Do you have a clear understanding of your target customer and the problem they are trying to solve
Can you articulate the value created by your product in terms of the benefits it delivers to the customer
Are you able to deliver a product/solution to your customer in both a cost-competitive and a differentiated way
What pricing and packaging combination allows you to capture your fair share of the value created

Strategic pricing and packaging enable a startup to capture a portion of the value a customer receives. By thinking deeply about pricing and packaging, founders can test early whether their product/solution is beneficial and preferable in the customer’s eyes. Said more simply, pricing is a measure of strategic value. However, startups must align their cost of delivering value in a risk-adjusted manner – which is more complicated in the GenAI world due to the dependencies on scarce infrastructure capacity and the fixed commitments required by cloud providers. Startups are up against incumbents that already have data and customers — but we believe savvy founders will navigate the complexities of AI business models to become long-term winners.

The Most Important AI Model is The Business Model — *Customer acquisition cost (CAC), gross margin percentage (GM%), and customer Lifetime value (LTV) are the most important metrics for assessing model success over time.*

Business Models and the AI Stack

Subscription pricing

Subscription pricing appears to be the most compelling approach for startups building applications and copilot at the top of the AI Stack.

Subscription pricing models have dominated the software application world over the past two decades. These SaaS offerings are easily understood from a buyer and seller perspective. The customer often pays per user or “seat” — most sellers prefer offering a modest discount for an annual subscription commitment and upfront payment. Often, the customer gets to try the software through a free trial, a proof of concept, or a less-featured “forever free” version of the product. And, in an increasingly collaborative world, there are sometimes pricing distinctions between the seat prices of a user who creates content and those who contribute to a workflow or business process.

While subscription pricing is easy to understand and track, when it comes to intelligent applications, there are often higher underlying costs for the data, models, and compute required to train and run them. So, many software companies are experimenting with hybrid pricing models of a base subscription and then some consumption-based payment tied to model/token usage.

Consumption Pricing

Consumption models have grown in popularity for different technology services, including cloud infrastructure, data warehousing, and observability, and they are becoming more prevalent at the application layer. Consumption model pricing is tied to the underlying units of usage (e.g., tokens, compute, storage), and for GenAI, the underlying quality of AI models that power that software. These consumption units are generally tied to components of the solution that are variable in cost, so cost, value, and revenue are reasonably mapped. The alignment between the marginal cost of delivering a service (including their contracts with CSPs) and the price/value per unit charged for that service is often why technology providers prefer consumption pricing.

Consumption models have the disadvantage of not providing visibility into how much customers will be billed for the services they consume. There is often tension for customers between signing an annual, minimum-consumption contract to get discounts and visibility and enjoying the flexibility of a pure consumption model. An upfront commitment (often with cash upfront) mitigates some key flexibility benefits of the consumption model, but it guarantees access and preferred pricing.

Embedded Pricing

AI companies and their partners are exploring different forms of usage and distribution in the AI stack. Customers can access a model through an API or use a virtual private cloud (VPC) to access models and applications in a distributed manner. They can often access popular models through model aggregators like HuggingFace or AWS Bedrock for further customization and fine-tuning. Depending on where an AI company sits in the stack, it may choose to sell its product through another technology/distribution partner.

These approaches align with an “embedded” pricing model where the end user is not directly paying the model builder or enabling technology company for the value being delivered. In addition, some AI model aggregators run “models as a service” offerings and charge an end-user customer while paying a portion of those revenues to the model creator. Other AI model aggregators allow customers to select models and then run those models on the cloud service of their choice – in which case the cloud provider is sharing revenue back to the aggregator. There are other variations of these embedded models, and they are an alternative mechanism for players in the AI stack to access or monetize customers. We think the embedded approach will be one of the most innovative and dynamic areas of pricing, packaging, and distribution in the applied AI model era.

Intelligent application companies can also get embedded deals where an established software company has the primary relationship with the end user customer and embeds the AI-powered capabilities into their applications. Those capabilities can be embedded as an API into another app, such as the case with a transcription, voice-enabling, or translation service. In other cases, a full application, such as Microsoft’s Github or 365 Copilot, is combined with other software to enhance that existing app. The market for both application and model layer embedded opportunities is very early, and we expect it to evolve in the months ahead.

Solution Services and Pricing

Whether at the model, middleware, or application layer, AI services are often stand-alone and accessible to the end user customer (or the developer who is using them to build an app). As AI is applied to more specific functions or verticals, and new workflows require some “change management,” we see the rise of AI solution offerings. Model makers such as Anthropic and Cohere are working with clients to customize their domain-specific models in a secure (either on-premise or VPC) manner. Middleware companies are helping their customer select the best models, integrate them with other capabilities (such as a vector database for RAG), and then help with deployment. Gen-native application companies (Typeface and Runway in marketing, Harvey or Lexion in legal) often need to help their customer with onboarding, application personalization, and ongoing adoption/change management. We anticipate more elements of the whole solution being priced and packaged as service offerings, bundled with core subscription or consumption pricing, and sometimes combined with solution provider partner capabilities to help end user customers realize value.

Navigating the Road Ahead

As AI models proliferate, companies across the AI stack will need to think deeply about their business models in general and their pricing and packaging strategies in particular to ensure long-term success. There is currently a tension in AI business models between achieving near-term scale and delivering strong unit economics over time. This tension will become more acute as incumbents bundle AI with existing software and customers demand a clear ROI for the incremental dollars they are paying for AI to improve business processes or team productivity.

These are not new challenges. What is different is that very well-capitalized companies – the cloud providers – are working on two vectors of growth – building AI infrastructure AND backing emerging AI model players with billions of investments. The cloud providers are also trying to move “up market” to the middleware and application layer, as evidenced by Amazon Bedrock and Q, along with Microsoft copilots, GPT-4, and other domain-specific models. Model companies are creating new and improved models, and that proliferation is leading to value-capture uncertainty at the model layer. The role of Gen-native and Gen-enhanced applications appears to be favoring the incumbent software companies because they have the customers, the contextual data, and the workflows to build relevant AI improvements. However, a massive opportunity exists for a first-principled focus on customer needs, and we believe several AI-native companies will be able to navigate the complexities of business models to successfully provide customers with strategic value. These innovators will become long-term winners in the age of AI.