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Echo Health Ventures: Pioneering Systemic Healthcare Transformation Through Strategic Venture Capital

by Jia Lissa
written by Jia Lissa

Echo Health Ventures, a prominent venture capital firm at the vanguard of healthcare innovation, is forging a unique path to drive systemic transformation within the American healthcare landscape. Founded in 2016, the firm distinguishes itself through its mission-driven approach, emphasizing hands-on, purpose-driven strategic investing. At its core, Echo Health Ventures believes that meaningful progress in healthcare necessitates robust partnerships between established enterprises and burgeoning innovators. This conviction has led to the creation of the Echo Innovation Alliance, a collaborative network that unites influential health plans—Cambia Health Solutions, Blue Cross NC, USAble Corporation, and BlueCross BlueShield of Tennessee. Together, these entities collectively serve over 8 million members, forming a powerful consortium dedicated to accelerating healthcare innovation on a national scale and fostering tangible, positive impact.

The Genesis and Mission of Echo Health Ventures

The inception of Echo Health Ventures in 2016 was rooted in a profound understanding of the complex challenges facing the U.S. healthcare system. The founders recognized that traditional venture capital models, while effective in many sectors, often fell short in addressing the intricate, multi-faceted nature of healthcare. The firm’s mission, therefore, was crafted to be more than just financial; it was designed to be a catalyst for deep-seated, systemic change. This transformative vision is pursued through a dual mandate: delivering both substantial financial returns and significant strategic value. The firm actively cultivates partnerships with forward-thinking health plans, leveraging their insights to identify and address critical needs within the healthcare ecosystem. This unique structure empowers Echo Health Ventures to provide unparalleled resources and direct support to its portfolio companies, while simultaneously facilitating crucial connections that drive tangible progress. The underlying philosophy is that strategic investors, when collaborating closely with key industry stakeholders, are uniquely positioned to instigate enduring and impactful improvements in healthcare delivery and accessibility.

A Differentiated Approach in the Venture Capital Landscape

What truly sets Echo Health Ventures apart in the competitive venture capital arena is its innovative corporate investment platform. The firm’s leadership firmly believes that capital alone is an insufficient driver of transformation; it must be coupled with strategic guidance, market intelligence, and deep industry understanding. This belief is operationalized through the Echo Innovation Alliance, which acts as a vital bridge between the investment firm and the on-the-ground realities of healthcare delivery. By closely collaborating with its Alliance members, Echo Health Ventures gains invaluable insights into the evolving needs and challenges faced by health plans and their members. These insights then inform investment strategies, identifying opportunities where innovative solutions can yield the greatest impact.

The firm’s investment thesis is not confined by geographical boundaries or specific stages of company development. Instead, Echo Health Ventures is "stage-agnostic," meaning it actively seeks out companies demonstrating a profound commitment to catalyzing healthcare transformation, regardless of their maturity. Whether a startup is in its nascent stages or a more established enterprise looking to scale, Echo Health Ventures is interested in bold, energetic, and original companies that are determined to tackle complex healthcare challenges with innovative solutions. This broad approach allows the firm to identify disruptive technologies and business models across the entire spectrum of healthcare.

Geographic Reach and Regional Dynamics

While Echo Health Ventures operates with a national scope, its strategic alignment with the Echo Innovation Alliance provides a nuanced understanding of regional healthcare dynamics. The health plans within the Alliance—Cambia Health Solutions (covering Oregon, Washington, Idaho, and Utah), Blue Cross NC (North Carolina), USAble Corporation (Arkansas), and BlueCross BlueShield of Tennessee (Tennessee)—collectively represent a vast and diverse patient population across multiple states. This geographic breadth is a critical asset, ensuring that Echo Health Ventures considers a wide array of unique perspectives and local healthcare needs. The firm recognizes that adaptability is paramount when confronting the complex and often region-specific challenges inherent in healthcare.

Despite the distributed nature of its Alliance members, Echo Health Ventures fosters a culture of constant collaboration. This interconnectedness ensures that innovation does not become siloed. Findings, best practices, and successful solutions are actively shared across the Alliance, creating a virtuous cycle of learning and improvement that benefits health plans and millions of members nationwide. This model allows for the rapid dissemination of successful strategies, accelerating the adoption of transformative innovations across different markets.

Defining the Portfolio: A Shared Vision for Impact

The defining characteristic of Echo Health Ventures’ portfolio is its inherent emphasis on shared impact. The firm’s model is built on the fundamental belief that achieving meaningful change in healthcare is not a solitary endeavor. It is a collaborative effort that involves Echo Health Ventures, its health plan partners in the Echo Innovation Alliance, and the portfolio companies themselves. The Alliance members are united by a common desire to reshape the future of healthcare and a steadfast commitment to partnering with like-minded industry players to realize this ambitious goal. They actively engage in a deep and continuous pursuit and adoption of innovative solutions to some of the most pressing issues confronting the healthcare sector.

Investment Criteria: Seeking Catalysts for Change

When evaluating potential portfolio companies, Echo Health Ventures looks for entities that demonstrate a clear and unwavering commitment to catalyzing healthcare transformation. This commitment is the primary filter, transcending financial metrics in the initial stages of consideration. The firm’s "stage-agnostic" approach means it is equally interested in early-stage disruptors and more mature companies seeking to amplify their impact. What unites these diverse entities is their boldness, their entrepreneurial spirit, and their innovative approaches to problem-solving. Echo Health Ventures seeks out companies that are not afraid to challenge the status quo and are driven by a genuine desire to create a better healthcare system.

Jessica Zeaske: Echo Health Ventures - National Venture Capital Association - NVCA

Core Values and Team Dynamics

At the heart of Echo Health Ventures’ philosophy lies an unwavering focus on impact. This translates into a rigorous approach to aligning private investment with measurable improvements in health and healthcare outcomes. The firm actively seeks out portfolio companies that resonate with this core value. Impact is not merely an afterthought; it is integrated into the very fabric of the investment process, including formal evaluation within Investment Memos. Performance metrics and observational data are meticulously analyzed to quantify the impact of portfolio companies.

A notable example of this commitment is highlighted in a 2024 public report, which detailed Echo Health Ventures’ work with three portfolio companies—Aledade, DispatchHealth, and Strive Health—and two Alliance members, Blue Cross NC and Cambia Health Solutions. This assessment examined how these portfolio companies had grown and positively influenced affordability, quality, and patient outcomes for the Alliance members. For instance, in collaboration with Aledade, the focus was on achieving shared savings for the health plan and meeting accountable care organization (ACO) quality metrics for both Commercial and Medicare Advantage members. This rigorous impact assessment underscores the firm’s dedication to demonstrable, real-world improvements.

Echo Health Ventures places significant value on the leadership and teams within its portfolio companies. The firm seeks individuals who possess not only technical expertise and entrepreneurial drive but also a shared vision for transforming healthcare. The ability to collaborate, adapt, and demonstrate resilience in the face of complex challenges are highly prized qualities. The firm recognizes that the success of any innovative venture hinges on the strength and dedication of its people.

Empowering Portfolio Companies and Advancing the VC Industry

Echo Health Ventures goes beyond mere financial investment to actively support its portfolio companies. The firm is a proactive facilitator, encouraging and fostering connections among its portfolio companies. This interconnected ecosystem sparks new ideas, facilitates strategic collaborations, and connects talented individuals across the healthcare innovation landscape. Portfolio companies often express appreciation for the fluidity and interconnectedness of this network, as it exposes them to fellow leaders dedicated to healthcare transformation, introduces them to new capabilities that can accelerate their growth, and provides a clear understanding of their position within the broader industry.

Furthermore, Echo Health Ventures serves as a trusted advisor to its portfolio companies. Leveraging its deep market intelligence, extensive industry knowledge, and unique perspective, shaped by access to some of the most innovative companies in healthcare, the firm offers invaluable guidance. This advisory role helps entrepreneurs navigate the complexities of the market, refine their strategies, and maximize their potential for success. By nurturing these strong relationships, Echo Health Ventures not only helps its portfolio companies thrive but also contributes to the advancement of the broader venture capital industry, particularly within the specialized field of healthcare.

The Value of NVCA Membership

Membership in the National Venture Capital Association (NVCA) is viewed by Echo Health Ventures as an extension of its own collaborative ethos. Much like the connections it cultivates within its own network, the NVCA provides a vital platform for forming relationships and engaging meaningfully with industry peers who are at the forefront of innovation. The organization’s professional development opportunities are considered invaluable touchpoints, enabling Echo Health Ventures to remain keenly attuned to the evolving trends and dynamics of the venture capital industry. This engagement allows for the sharing of best practices, the exploration of emerging challenges, and the collective advancement of the VC ecosystem.

Looking Ahead: The Evolving Landscape and Future Initiatives

The trajectory for Echo Health Ventures is clearly defined by a continued commitment to impact-driven investment. In 2024, the firm launched its dedicated Impact program, a significant initiative focused on systematically aligning private investment with measurable improvements in health and healthcare. This program is essential for evaluating investment Return on Investment (ROI) in a manner that reflects the nuanced realities of the healthcare sector. Working collaboratively with its Alliance members and portfolio companies, Echo Health Ventures has identified four core pillars for its Impact initiative: affordability, quality and outcomes, experience and access, and growth and competitiveness.

To operationalize these goals, Echo Health Ventures has been actively facilitating forums that bring together leaders from the Echo Innovation Alliance and its portfolio companies. These dialogues provide a critical space for candid discussions about impact from the perspective of payer partnerships, fostering a deeper understanding of mutual objectives and challenges.

Looking forward to 2025, Echo Health Ventures is set to release its first annual Impact report. This comprehensive report will document the firm’s progress in embedding impact assessments into the initial diligence stages of investment and will highlight the strengthening of partnerships between its portfolio companies and the Alliance. As Echo Health Ventures continues to deepen its engagement with its portfolio companies and Alliance members, the central theme of Impact will remain a paramount focus and priority for the firm, guiding its investment strategies and shaping its contributions to the future of healthcare.

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Venture Capital & Startup Funding

Could Pixels Hold the Keys to Training Useful Agents? Standard Intelligence Bets Big on Video Pre-Training for General AI

by Muslim
written by Muslim

In the rapidly evolving landscape of artificial intelligence, a fervent race is underway to develop sophisticated agents capable of complex reasoning and autonomous action. While the current paradigm heavily favors language models, leveraging their ability to process text, understand instructions, and interact with tools, a nascent startup, Standard Intelligence, is charting a distinctly different course. The company is making an ambitious wager that the most direct and scalable path to achieving truly general artificial intelligence lies not in the intricacies of human language or the abstraction of screenshots, but in the raw, unadulterated stream of video data.

This contrarian approach positions Standard Intelligence against the prevailing wisdom, which has largely focused on enhancing language models and building agent ecosystems around them. Coding agents, for instance, have demonstrated remarkable capabilities in understanding problems and generating code to solve them, pushing the boundaries of what AI can achieve in software development. However, Standard Intelligence’s thesis posits that this approach, while effective, might be inherently limited in its ability to achieve true generality.

The core of Standard Intelligence’s strategy is to train AI agents directly on the visual, sequential data of human computer usage. Instead of predicting discrete text tokens, their models learn to perform actions by analyzing pixels, predicting the precise sequence of mouse movements, clicks, and keystrokes required to navigate and interact with a computer interface. This methodology draws a compelling parallel to the advancements in autonomous driving technology, particularly the "Tesla Full Self-Driving" approach, which relies heavily on processing real-world visual data to enable vehicles to perceive and react to their environment. Standard Intelligence aims to apply a similar foundational principle to the realm of knowledge work and digital interaction.

This "bitter lesson"-inspired philosophy, as described in their publications, emphasizes learning from direct experience and scaling data aggressively, rather than relying on meticulously engineered workflows or intricate language model wrappers. By feeding their models the raw stream of computer use, they anticipate that general capabilities will emerge organically from the sheer volume and diversity of the data. This represents a significant departure from current trends, which often involve complex prompting strategies and the integration of numerous specialized tools.

The Unconventional Path Through Video

The decision to focus on video data is, by all accounts, a challenging one. Video is notoriously unwieldy, demanding immense computational resources and presenting significant technical hurdles. Historically, attempts to scale video processing for advanced AI applications have often faltered due to these inherent difficulties. The Standard Intelligence team openly acknowledges their non-traditional background in video processing, stating they are "not video people." This lack of pre-existing biases or ingrained assumptions about video as a medium, however, appears to have fostered a unique problem-solving approach. They have had to derive solutions from first principles, tackling each challenge with a blend of optimism, ingenuity, and a pragmatic, resourceful attitude.

Their efforts have yielded impressive, tangible results, defying expectations for a young company. Standard Intelligence has reportedly amassed an 11-million-hour computer action dataset, a figure that stands as the largest of its kind in the industry. This dataset forms the bedrock of their training regimen. Furthermore, they have developed a video encoder that demonstrates a remarkable level of efficiency, being approximately 50% more token-efficient than existing competing approaches. This breakthrough allows for the ingestion of nearly two hours of 30 frames-per-second video data within a single, colossal 1-million-token context window. This capability is crucial for capturing the nuances and temporal dependencies inherent in computer usage.

Beyond the computational and algorithmic advancements, Standard Intelligence has also made significant strides in infrastructure. They have established a 30-petabyte storage cluster in San Francisco for an estimated cost of under $500,000. This figure represents a substantial cost saving, being approximately 20% cheaper than comparable solutions offered by major cloud providers, often referred to as hyperscalers. This cost-effective infrastructure management is a testament to their resourceful approach and ability to innovate beyond core AI development.

FDM-1: A Glimpse of Video-Driven Generality

The culmination of these efforts is evident in their foundational model, FDM-1. This model, trained directly and at scale on computer-use video, offers an early preview of the potential unlocked by this video-first pre-training paradigm. FDM-1 has demonstrated a surprising breadth of generalist capabilities. For instance, it can reportedly extrude a CAD gear in Blender, a complex 3D modeling software. In a more dynamic test, it was able to navigate a simulated car around a San Francisco block after just an hour of fine-tuning. Furthermore, the model has shown an aptitude for identifying software bugs by exploring the state space of applications in a manner analogous to a curious human user. These diverse applications highlight the model’s ability to generalize its learning across different tasks and domains, a key objective in the pursuit of artificial general intelligence.

The Visionaries Behind Standard Intelligence

The company’s trajectory is shaped by its young and driven founders, Galen Mead and Devansh Pandey. The pair first met as teenagers in 2022 during the Atlas Fellowship, a prestigious program for high school students focused on AI alignment and AGI. This early exposure to the critical questions surrounding advanced AI has seemingly instilled in them a profound sense of responsibility and urgency. Both Mead (21) and Pandey (20) have deferred their undergraduate studies, driven by a shared commitment to accelerating progress in the field of AGI.

Their leadership is characterized by a rare combination of refined taste, resourcefulness, technical audacity, and ambitious vision. This is not only reflected in their product development and research direction but also in the detailed and insightful reporting of their FDM-1 model. Their commitment to pursuing this challenging mission, eschewing more conventional career paths such as lucrative offers from established tech giants or immediate pursuit of advanced degrees, underscores their dedication.

The broader Standard Intelligence team, though small, comprises six exceptionally talented individuals. Neel, Yudhister, Ulisse, and Ryan are described as quirky and exceptional, each bringing unique skills and perspectives to the table. Their collective decision to join Standard Intelligence signifies a shared belief in the company’s unconventional but potentially groundbreaking approach to AI development.

A New Pre-Training Regime for the Age of AI

The concept of using video as a training ground for AI is not entirely new. Early successes, such as DeepMind’s Deep Q-Networks (DQN), demonstrated that agents could learn complex behaviors directly from pixel data in Atari video games. More recently, companies like Tesla have scaled video-centric models to enable autonomous navigation for vehicles and robots in the physical world. However, within the specific context of developing general knowledge agents and sophisticated digital assistants, video-first pre-training has remained a relatively unconventional idea, often overshadowed by the dominance of large language models.

Standard Intelligence is now betting that this will not remain the case for long. Their work suggests that by unlocking the potential of raw visual data, a new era of AI agents, capable of understanding and interacting with the digital world in a more profound and generalizable way, may be on the horizon. This strategic pivot towards video pre-training represents a significant challenge to the established norms in AI research and development.

The company has recently secured Series A funding, with Standard Intelligence announcing that they are "thrilled to lead Standard Intelligence’s Series A alongside Miko and Yasmin from Spark Capital." This investment signifies a strong vote of confidence from the venture capital community in Standard Intelligence’s vision and its potential to disrupt the current trajectory of AI agent development. The infusion of capital will likely accelerate their research, data acquisition, and model development, allowing them to further validate their video-centric approach and push the boundaries of what is currently considered possible in artificial intelligence. The implications of their success could extend far beyond the immediate applications, potentially reshaping how we interact with technology and how AI agents are developed and deployed across a multitude of industries.

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Venture Capital & Startup Funding

The Dawn of Agentic Development: How AI Coding Tools are Redefining Developer Infrastructure Distribution

by Nana
written by Nana

For two decades, the established playbook for distributing developer infrastructure centered on a familiar, yet often challenging, process: securing developer buy-in to install an SDK, integrate it into a single team’s workflow, and then orchestrate expansion across the organization. The primary hurdle in this model was invariably "developer bandwidth"—the inherent difficulty in convincing developers to adopt a new dependency, manage credentials, and implement initial API calls. However, the advent of AI coding tools like Cursor and Claude Code is fundamentally disrupting this long-standing bottleneck. With AI now contributing to a significant and rapidly growing percentage of public GitHub commits, developer infrastructure companies face a critical imperative: adapt their products for AI coding agents not as an optional enhancement, but as a strategic necessity for survival and growth.

The Evolving Landscape of Developer Tool Distribution

The traditional approach to developer tools distribution has long recognized that success hinges on minimizing friction. Pioneers like Stripe, with its famously concise seven-line integration, and Twilio, offering copy-paste quickstarts, exemplified this principle. Datadog’s one-command agent installation further underscored the importance of a seamless initial experience. The entire Product-Led Growth (PLG) movement was an optimization around this core insight: making the initial five minutes of interaction with a product "magical" to foster organic adoption and word-of-mouth referrals.

Yet, this initial success often masked a more profound challenge: expansion within an organization. While getting an SDK installed was a critical first step, achieving comprehensive adoption—instrumenting it correctly across every service, feature, and team—remained an arduous and often unglamorous undertaking. This ongoing work determined whether a tool delivered its promised value or languished at a mere 20% coverage, failing to realize its full potential.

Sudhee Chilappagari, reflecting on his experience as a product manager at Segment, highlighted these dual challenges. The first involved securing developer time and resources to install analytics.js and instrument events. The second, more complex issue, was ensuring the effectiveness of these implementations—guiding developers on what events to track and adhering to correct API syntax to deliver tangible downstream value to customers. Segment’s initial solution to the developer bandwidth problem was to simplify the installation process of analytics.js. To address the best-practices challenge, they developed "Protocols," a tracking plan product designed to enforce a "plan first, track effectively later" discipline. Further innovations like "Typewriter," a type-safety plugin, aimed to auto-complete event code, reducing the cognitive load on developers. While these were significant advancements, they necessitated the creation of entire product lines and dedicated engineering teams, underscoring the substantial investment required to bridge the gap between initial installation and effective, widespread instrumentation. This experience illustrated a broader trend: even companies strategically prioritizing this problem required immense effort, while many others saw their tools remain at partial adoption due to human inconsistency rather than technical limitations, thus never achieving their full value proposition.

The Transformative Impact of Agentic Development

The emergence of AI coding agents, such as Claude Code and Cursor, is rapidly reshaping the developer workflow. These agents are increasingly becoming the primary interface for writing and modifying code. This represents a paradigm shift, transforming the developer’s default interaction from hands-on coding to a "describe what I want, review the code" model. In this new paradigm, AI agents act as powerful intermediaries between developer intent and the codebase. Crucially, this intermediary is programmable.

With AI agents handling the bulk of code generation, the historical barrier of "developer bandwidth" effectively dissolves. The excuse of "we can’t get engineering cycles" becomes obsolete. The real unlock lies in the ability to imbue these agents with the deep, context-aware knowledge previously held by a company’s most experienced solutions engineers. The critical question becomes: can an AI agent understand a customer’s industry, ideal customer profile (ICP), goals, and desired outcomes well enough to instrument an SDK comprehensively and correctly across an entire application?

If the answer is yes, the implications are profound. This unlocks the potential for a "10x solutions engineer" for every customer account—an agent that operates on every pull request, never takes a vacation, and consistently adheres to naming conventions and best practices. This capability is powered by "agent skills." These are compact, installable context packages that educate an AI coding agent on how a specific tool operates, the desired patterns to follow, and common pitfalls to avoid. With a single command, every interaction an agent has with a codebase can be infused with deep, opinionated knowledge of an SDK. This represents a fundamentally new and powerful distribution surface, unlike anything seen in the previous two decades of developer tooling.

Agent Skills Are the New SDK (And You Should Be Building One)

Amplifying Value Through Organizational Coverage

The value proposition of many infrastructure products is intrinsically tied to their coverage within an organization. Historically, achieving this coverage has been constrained by human memory and discipline, rather than technical capability. Agent skills, however, effectively dismantle these constraints.

Consider the domain of observability tools. While OpenTelemetry has seen widespread adoption across metrics, logs, and traces, its true value is unlocked by comprehensive coverage. The more of a system that is instrumented, the more coherent the traces, and the more effective the debugging becomes. This requires every developer, on every new service and endpoint, to remember to add spans, propagate context, attach the correct attributes, and configure appropriate exporters. This is less a technical challenge and more a human memory challenge.

A well-designed OpenTelemetry skill can fundamentally alter this default behavior. The AI agent, guided by the skill, would automatically instrument new HTTP handlers, wrap database calls, and propagate context across service boundaries. The developer is freed from the burden of remembering these crucial steps; the agent handles them proactively.

This impact extends beyond mere adoption metrics. For developer infrastructure products that employ usage-based pricing models—such as per-span volume, tracked events, monthly active users, or workflow executions—depth of coverage directly correlates with revenue. An account with only 20% instrumentation is generating, at best, 20% of its potential billing. Agent skills can close this gap without requiring new customer acquisition. Each pull request an agent instruments represents incremental Annual Recurring Revenue (ARR) that previously would have necessitated a dedicated sales motion.

This principle applies across a wide array of developer infrastructure categories:

  • Product Analytics (e.g., Pendo, Segment, Amplitude): While initial installation is straightforward, realizing value hinges on tagging every meaningful user interaction with precise event names, properties, and user context. This is a continuous instrumentation task distributed across all frontend developers. A skill that understands an organization’s event taxonomy can transform sporadic tagging into comprehensive coverage, directly influencing customers’ event tiers and driving automatic upsells.

  • Feature Flags (e.g., LaunchDarkly, Statsig): Best practices dictate that every new feature should be wrapped in a flag. However, friction often leads to only a fraction of features being flagged. A skill that enforces a "new feature equals flag by default" policy and understands organizational naming conventions not only boosts adoption but also subtly shifts engineering behavior, making the right thing the easy thing.

  • Authentication SDKs (e.g., Auth0, Descope): Identity verification, correct token validation, session handling, and logout logic are critical for every new route, API endpoint, or user-facing flow. Under pressure to deliver quickly, developers may shortcut these processes. A skill that enforces "every new endpoint validates identity before execution" and understands preferred SDK patterns transforms authentication from an inconsistently applied step into a ubiquitous default.

    Agent Skills Are the New SDK (And You Should Be Building One)

  • Authorization SDKs (e.g., Styra, Permit.io, Oso): Authorization logic is notoriously inconsistent across codebases. A skill that grasps an organization’s permission model and automatically integrates authorization at every new endpoint simultaneously enhances security posture and SDK adoption.

  • Runtime Application Security (e.g., Contrast Security, Arcjet): Runtime Application Self-Protection (RASP) tools face a similar partial-coverage challenge, with more severe consequences. A missed instrumentation point can create an unprotected attack surface rather than just a reporting gap. A skill that enforces protection hooks at every new route by default transforms RASP from a partial perimeter into a pervasive runtime fabric, where "the developer forgot" is an unacceptable security vulnerability.

  • Secrets Management (e.g., HashiCorp Vault, Hush): Every new database connection string, API key, or credential presents a potential point of failure where a developer might hardcode sensitive information instead of retrieving it from the organization’s secrets store. A skill that intercepts these moments, flagging direct environment variable access like os.environ['STRIPE_KEY'] and replacing it with a secure retrieval call like hush.get('stripe_key'), enforces hygiene precisely at the point of risk. Such hardcoded secrets can easily be overlooked during code reviews, making agent intervention critical.

  • Testing Frameworks (e.g., Playwright, Testcontainers, Cypress): While testing tools are generally not difficult to install, maintaining test coverage can be challenging. Coverage often drifts, not due to a lack of developer appreciation for testing, but because test writing is frequently deprioritized under velocity pressure. A skill that generates opinionated, framework-consistent tests alongside new functions or components shifts the agent’s default output from merely delivering a feature to delivering the feature with its accompanying tests.

  • Durable Workflow Engines (e.g., Orkes, DBOS, Temporal): These tools often present steep learning curves, with opinionated APIs, subtle correctness requirements regarding determinism, and specific patterns for handling retries and failures. A skill that encapsulates this complex context ensures that developers interacting with the workflow layer, regardless of their tenure, adhere to the same robust patterns established by earlier team members.

Early Evidence of a Paradigm Shift

The transformative potential of agent-native distribution is already evident. Neon, a serverless PostgreSQL company, made a strategic investment in this area by publishing AI rules, developing Claude Code plugins and Cursor integrations, and releasing a comprehensive agent skills library on GitHub. The impact was remarkable: over 80% of databases provisioned on Neon were created by AI agents, a statistic so compelling it was cited as a key factor in Databricks’ $1 billion acquisition of Neon in 2025. Neon’s success was not merely in building a database but in embedding itself into the default workflows of AI agents, a distribution advantage that proved to be worth an extraordinary valuation.

A Strategic Reorientation for Competitive Advantage

The rise of agent skills signifies a fundamental shift in where competitive advantage is built within the developer tools market. Historically, companies that excelled at the initial installation and the "first five minutes" often dominated. However, if skills become the primary adoption surface, winning will increasingly depend on the depth and accuracy with which a tool’s capabilities are embedded within an agent’s context. This dynamic favors a different set of capabilities: the quality and completeness of a company’s agent skills, the trust developers place in their agent-layer recommendations, and the agility with which these skills are updated as APIs evolve.

This has significant implications for investments in developer relations and documentation. The most effective SDK documentation has always served as a form of distribution, making it easy for developers to understand and correctly utilize a tool. Agent skills represent an evolution of this concept: they are documentation that executes. Companies that proactively invest in developing high-quality, opinionated, and well-maintained skills are effectively pre-loading their adoption curve into every agent-assisted developer workflow.

Agent Skills Are the New SDK (And You Should Be Building One)

Furthermore, skills act as a potent discovery channel within organizations. When a new developer joins a team and begins using an AI coding agent, the agent, equipped with relevant skills, can suggest tools and patterns already in use by the organization. This bypasses the need for manual searching or reliance on informal recommendations, enabling tools to spread virally through the agent’s contextual awareness.

Implications for Builders and Investors

For founders building developer infrastructure, the agent skill is no longer a peripheral documentation task but a core product artifact. The critical question shifts from "how do we make the first install easy?" to "how do we make every subsequent instrumentation decision effortless for every developer, perpetually?" The answer lies in developing a skill that imbues an AI agent with the same nuanced understanding of an API as a company’s most seasoned solutions engineer.

Investors evaluating developer tools companies must recognize that distribution moats are being fundamentally rebuilt. A company with a modest PLG motion but an exceptional skill embedded across enterprise accounts within agent contexts may possess a more robust expansion engine than one with a polished quickstart but limited agent-layer presence. Key metrics are evolving; coverage depth within accounts, rather than solely seat count, is increasingly indicative of durable value.

Agent skills also address critical coverage gaps that traditional PLG models struggled to penetrate. This includes retrofitting legacy codebases, where agents can systematically refactor existing uninstrumented code to meet current standards without requiring dedicated engineering sprints. It also extends to DevOps workflows, where infrastructure-as-code, CI/CD pipelines, and deployment scripts can benefit from the same pattern-enforcement logic applied to application code.

The first wave of PLG focused on removing friction at the point of installation. The current wave, driven by agentic development, is about removing friction at every code commit, indefinitely. Companies that strategically build for this second wave will be exceptionally well-positioned in the coming years. The pivotal question for founders and investors alike today is: "What is your AI coding-agent skills strategy, and when does it launch?"

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Venture Capital & Startup Funding

Executive Interview: Bria AI

by Rifan Muazin
written by Rifan Muazin

The rapidly evolving landscape of artificial intelligence, particularly in the realm of visual content generation, presents both immense opportunities and significant challenges for businesses. At the forefront of this transformative wave is Bria AI, a company carving out a distinct niche by providing the foundational infrastructure that empowers enterprises to harness the power of generative AI for visual content. Rotem Sarfaty, Chief of Staff at Bria AI, recently shared insights into the company’s strategic vision, its understanding of the market, and its commitment to addressing critical customer needs, especially in the context of an increasingly complex regulatory and ethical environment.

Defining the Market and Bria AI’s Unique Proposition

Bria AI operates within the enterprise visual generative AI infrastructure market, a segment of the broader AI stack that is becoming increasingly critical for businesses across diverse industries. Sarfaty articulated that Bria AI is not developing customer-facing creative tools but rather providing a robust Platform-as-a-Service (PaaS) solution. This infrastructure layer is designed to enable developers, platforms, and large enterprises to build and integrate their own AI-driven workflows for generating, editing, and validating visual content at scale.

"We give builders, platforms, and enterprises an infrastructure to power their own AI-driven workflows," Sarfaty explained. This foundational approach positions Bria AI as a key enabler for businesses looking to embed advanced visual AI capabilities directly into their existing operations and products, rather than relying on standalone applications.

The company’s differentiation, according to Sarfaty, is rooted in three core pillars that competitors, he asserts, do not effectively combine:

  1. Professional Creative Control via Visual Generative Language (VGL): Bria AI’s proprietary Visual Generative Language (VGL) is a key differentiator. This technology aims to provide a higher degree of precision and control over the generated visual output, moving beyond the often unpredictable nature of some generative AI models. For enterprises, especially those in creative industries or with stringent brand guidelines, this level of control is paramount. It allows for the generation of visuals that are not only aesthetically pleasing but also align precisely with specific creative briefs, brand identities, and desired moods. This granular control is essential for professional applications where consistency and adherence to specific artistic visions are non-negotiable.

  2. Deployment Flexibility: Recognizing the diverse operational environments of enterprises, Bria AI offers unparalleled deployment flexibility. This includes the ability to deploy on public cloud infrastructure, in a customer’s private cloud environment (build-your-own-cloud), or even on-premise. This adaptability is crucial for businesses with varying data governance policies, security requirements, and existing IT architectures. For instance, highly regulated industries or companies with sensitive intellectual property may opt for on-premise solutions to maintain complete control over their data and AI models, while others might leverage the scalability of cloud-based deployments.

  3. 100% Licensed Data Foundation: Perhaps the most significant differentiator in the current climate of legal scrutiny surrounding AI-generated content, Bria AI emphasizes its commitment to a "100% licensed data foundation." This means that the data used to train their AI models is legally sourced and licensed, thereby eliminating the risk of copyright infringement for their clients. This is a critical concern for enterprises, as the use of unethically or illegally sourced training data can lead to substantial legal liabilities, reputational damage, and the invalidation of generated content. In a market where the legal frameworks surrounding AI-generated intellectual property are still evolving, Bria AI’s approach provides a significant layer of security and compliance.

The Evolving Generative AI Market: Context and Trends

The emergence of powerful generative AI models, such as those capable of creating text, images, and even video, has sparked a revolution in how digital content is produced. Early iterations of these technologies, while impressive, often lacked the precision, control, and legal clarity that enterprises require. This has created a market vacuum that Bria AI aims to fill with its infrastructure-focused approach.

Timeline of Generative AI Advancements (Simplified):

Executive Interview: Bria AI
  • Pre-2010s: Foundational research in deep learning and neural networks.
  • Early 2010s: Rise of Generative Adversarial Networks (GANs), enabling more sophisticated image generation.
  • Mid-2010s: Development of transformer architectures, paving the way for large language models.
  • Late 2010s – Early 2020s: Public release and widespread adoption of large-scale generative models (e.g., DALL-E, Midjourney, Stable Diffusion) for text-to-image generation, showcasing impressive capabilities but also raising concerns about data sourcing and copyright.
  • Present: Increasing demand for enterprise-grade solutions that offer control, scalability, and legal compliance, leading to the rise of infrastructure providers like Bria AI.

The market for generative AI is experiencing exponential growth. According to a recent report by Grand View Research, the global generative AI market size was valued at USD 11.03 billion in 2022 and is projected to expand at a compound annual growth rate (CAGR) of 33.4% from 2023 to 2030. Within this vast market, the enterprise segment, which Bria AI targets, is expected to be a primary driver of growth, fueled by the need for efficient content creation, personalized marketing, product design, and enhanced customer experiences.

However, this rapid growth has been accompanied by significant legal and ethical debates. High-profile lawsuits have been filed by artists and content creators alleging that their copyrighted works were used without permission to train AI models. These legal challenges underscore the critical importance of Bria AI’s commitment to a licensed data foundation. Businesses are increasingly aware that relying on AI models trained on potentially infringing data could expose them to significant legal risks.

Addressing Customer Needs in the Enterprise Space

Sarfaty’s emphasis on professional creative control directly addresses a fundamental need for many enterprises. While consumer-facing generative AI tools may prioritize ease of use and rapid iteration, businesses often require:

  • Brand Consistency: Ensuring that all generated visuals adhere to strict brand guidelines, color palettes, and stylistic elements.
  • Specific Use Cases: Generating visuals for particular marketing campaigns, product visualizations, or internal documentation that require precise attributes and narratives.
  • Iterative Refinement: The ability to fine-tune generated content through multiple iterations based on feedback from creative teams, marketing departments, or product managers.
  • Integration with Existing Workflows: Seamlessly incorporating AI-generated visuals into existing content management systems, design software, and marketing automation platforms.

The deployment flexibility offered by Bria AI is another key aspect of meeting enterprise needs. Data security, privacy regulations (like GDPR or CCPA), and the desire for greater control over AI model performance all drive the demand for diverse deployment options. An enterprise that handles sensitive customer data or operates in a highly regulated industry, for example, will find the on-premise or private cloud deployment options indispensable.

Furthermore, the "100% licensed data foundation" is not merely a technical feature but a strategic imperative for enterprises. It mitigates the risk of costly litigation and reputational damage. This is particularly relevant as regulatory bodies worldwide begin to examine and potentially legislate aspects of AI-generated content and intellectual property. Companies are actively seeking solutions that offer a clear path to legal compliance.

Background and Context: The Rise of Generative AI Infrastructure

The evolution of generative AI has moved from research labs to widespread public access, creating a demand for more sophisticated, enterprise-grade solutions. Early generative AI models, while groundbreaking, often operated as standalone applications. This meant that businesses had to find ways to integrate these tools into their existing workflows, which could be complex and inefficient.

Bria AI’s strategy of providing an infrastructure layer addresses this challenge by allowing businesses to build their own AI-powered visual content workflows. This is akin to how cloud computing infrastructure providers like AWS or Azure enable businesses to build and deploy a wide range of applications without needing to manage the underlying hardware. Bria AI offers the foundational AI capabilities, allowing its clients to focus on developing the unique applications and workflows that serve their specific business needs.

The market has seen a proliferation of AI models and tools, leading to a need for standardization and reliable infrastructure. Companies that previously might have experimented with off-the-shelf AI tools are now looking for more robust, scalable, and secure solutions. This shift is driving the demand for PaaS providers like Bria AI, who can offer the underlying technology and flexibility that enterprises require to innovate.

Implications for the Enterprise Visual Content Market

Bria AI’s approach has several significant implications for the broader enterprise visual content market:

  • Democratization of Advanced AI for Visuals: By providing a foundational infrastructure, Bria AI enables a wider range of businesses, including those without extensive AI expertise, to leverage advanced visual generative AI capabilities. This can lead to increased innovation and competition in sectors reliant on visual content.
  • Reduced Legal and Copyright Risks: The emphasis on a licensed data foundation sets a new standard for responsible AI development and deployment in the enterprise space. This could push other AI providers to adopt similar practices, leading to a more ethically sound generative AI ecosystem.
  • Enhanced Creative Workflows: The combination of VGL for precise control and flexible deployment options can lead to more efficient and effective creative workflows. Designers, marketers, and product developers can iterate faster and achieve more tailored results.
  • New Business Models: By empowering platforms and builders, Bria AI facilitates the creation of new AI-driven services and products that were previously not feasible. This could lead to novel revenue streams and competitive advantages for its clients.
  • Increased Enterprise Adoption of Generative AI: As concerns about control, scalability, and legal compliance are addressed, enterprises are likely to accelerate their adoption of generative AI for a multitude of applications, from marketing and e-commerce to product design and virtual reality.

The ongoing development and adoption of visual generative AI infrastructure providers like Bria AI signal a maturing market. As the technology becomes more sophisticated and its applications more widespread, the focus is shifting towards ensuring that these powerful tools are deployed responsibly, ethically, and in a way that genuinely benefits businesses and their customers. Bria AI’s strategic emphasis on control, flexibility, and legal compliance positions it as a key player in shaping the future of enterprise visual content creation.

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Venture Capital & Startup Funding

AI Drives Europe’s Second Straight Quarter of Funding Gain as Deal Volume Falls Sharply

by Raul Delapena Setiawan
written by Raul Delapena Setiawan

European venture funding experienced a robust surge in the first quarter of 2026, reaching an impressive $17.6 billion. This figure represents a significant year-over-year increase of nearly 30%, marking the second consecutive quarter of growth for the continent’s venture capital landscape. This upward trend, mirroring global and North American patterns, was overwhelmingly propelled by the burgeoning Artificial Intelligence (AI) sector, which for the first time commanded over half of Europe’s total venture funding for the quarter. The sustained growth above prior quarters suggests that European venture funding is not only recovering but gaining considerable momentum.

However, this surge in capital investment was accompanied by a notable contraction in deal volume. Europe saw more capital being deployed into fewer companies, with deal volume plummeting by 40% year over year. This decline was particularly pronounced at the seed stage, which saw a 44% decrease, and early-stage funding, down 30%. Late-stage deal volume, in contrast, remained stable, aligning with the figures observed over the preceding four quarters. This dichotomy between increasing investment amounts and decreasing deal numbers highlights a potential shift towards larger, more concentrated funding rounds, likely driven by the immense capital requirements of cutting-edge AI development.

AI Dominates European Venture Funding Landscape

The first quarter of 2026 witnessed an extraordinary ascent for AI startups in Europe, attracting a substantial $9.2 billion in funding. This amount not only signifies a dramatic increase in investment within the sector but also represents a historic high, accounting for over 50% of all venture capital allocated to European companies during the period. This milestone underscores the continent’s growing strategic focus and investment in AI technologies, positioning it as a key player in the global AI race.

The magnitude of this AI-driven funding is further exemplified by the four largest funding rounds secured by European startups in Q1 2026. All of these were attributed to AI-related ventures. Nscale, a company specializing in data center construction, secured a significant investment. Wayve, a developer of autonomous driving technology, also garnered substantial backing. Advanced Machine Intelligence, a frontier lab focused on physical AI, and Legora, an AI-powered legaltech platform, each raised over a billion dollars and more than $500 million, respectively. These colossal investments signal immense investor confidence in the transformative potential of AI across diverse industries, from infrastructure and transportation to cutting-edge research and specialized software solutions.

The concentration of capital in these large-scale AI projects suggests a strategic pivot towards foundational technologies and ambitious, long-term development. Companies building the infrastructure for AI, such as Nscale, are crucial for enabling the widespread deployment of AI solutions. Similarly, advancements in autonomous systems and the exploration of physical AI by entities like Wayve and Advanced Machine Intelligence indicate a move beyond theoretical applications towards tangible, real-world integration. The success of AI legaltech platforms like Legora also points to the broad applicability of AI in optimizing existing professional services.

United Kingdom and France Lead the Growth Charge

In terms of geographical distribution, startups based in the United Kingdom and France emerged as the primary beneficiaries of this venture capital influx. The UK saw its startups raise a total of $7.4 billion in Q1 2026, while France attracted $2.9 billion. These figures represent significant year-over-year growth for both nations, underscoring their robust startup ecosystems and their ability to attract substantial investment, particularly in the AI domain. In contrast, German startups raised $1.9 billion, a figure that remained flat compared to the previous year, indicating a more stable but less dynamic investment environment in that region during this quarter.

France, in particular, has solidified its position as a European leader in the development of advanced AI research and frontier labs. The Q1 2026 period saw Paris-based Advanced Machine Intelligence, founded by the esteemed AI pioneer Yann LeCun, formerly of Meta AI, secure $1 billion in funding. This monumental round not only propelled France to the forefront of AI innovation but also marked the largest seed funding round ever recorded on the continent. Furthermore, this deal represented only the second instance of a European frontier lab receiving over a billion dollars in funding, following the precedent set by Mistral AI’s $2 billion Series C round in the previous year.

The emergence of France as a hub for cutting-edge AI research is a testament to its strategic investments in scientific talent and its supportive ecosystem for deep technology ventures. The success of Advanced Machine Intelligence, backed by a figure as influential as Yann LeCun, validates the potential for European institutions to compete on a global scale in fundamental AI research. The dual success of Mistral AI and Advanced Machine Intelligence in attracting such significant capital highlights a growing trend of European AI companies challenging established global players and attracting top-tier talent and investment.

Stage-Specific Investment Trends in Europe

Examining the funding landscape by investment stage reveals a dynamic and somewhat bifurcated picture. Late-stage funding to European startups experienced a remarkable surge in Q1 2026, nearly doubling from the previous year. A total of $9.2 billion was invested across 83 late-stage deals, representing a substantial 91% increase in funding volume year over year. The largest rounds at this stage were distributed across a diverse array of sectors, including AI hardware, fintech, agentic AI, productivity software, sensors, defense, e-commerce, and energy. This broad distribution suggests that mature companies across various industries are leveraging substantial capital injections, likely for scaling operations, market expansion, and further technological development, with AI being a significant catalyst in many of these growth strategies.

Conversely, early-stage funding for European startups saw a contraction compared to the previous year, declining by approximately 20%. Crunchbase data indicates that early-stage investment totaled $5.3 billion in Q1 2026, spread across more than 240 funding rounds. Within this early-stage segment, larger Series A rounds proved to be the predominant funding mechanism in sectors such as semiconductors, energy, and healthcare. This trend suggests that while early-stage investment has slowed overall, there remains a strong appetite for significant funding in promising ventures within these critical technological and industrial domains, particularly those with a clear path to commercialization and potential for disruptive impact.

Seed funding, while experiencing a notable increase in total amount, also presented a nuanced picture. The total seed funding reached $3.1 billion across more than 790 deals in Q1 2026. This represents a 50% year-over-year increase in the funding total. However, this substantial growth was significantly influenced by the aforementioned $1 billion round for Advanced Machine Intelligence, which skewed the overall figures. Without this outlier, the seed funding landscape might present a more moderate growth trajectory. Nevertheless, the sheer number of seed deals indicates continued activity at the foundational stages of startup development, albeit with a potential for larger initial investments in select high-potential companies.

The interplay between late-stage growth and early-stage contraction, coupled with the outlier effect on seed funding, points to a venture capital market that is increasingly focused on backing proven business models and companies with clear technological advantages, especially within the AI domain. Investors appear more willing to commit larger sums to later-stage companies with established traction and to disruptive seed-stage ventures with the potential for exponential growth, while mid-stage funding might be experiencing a period of recalibration or increased selectivity.

Implications and Broader Market Context

The Q1 2026 European venture funding report paints a compelling picture of a market invigorated by the transformative power of Artificial Intelligence. The substantial capital flowing into larger rounds, particularly in AI infrastructure, frontier labs, autonomous systems, and AI-driven applications, underscores a strategic focus on foundational technologies that are poised to redefine industries. This trend is not unique to Europe; it mirrors a global shift where capital is increasingly concentrating into the largest deals within sectors experiencing exponential growth, largely fueled by AI advancements.

The participation of both Europe-based and US-based investors in these significant funding rounds highlights the interconnectedness of the global venture capital ecosystem. American VCs, with their deep pockets and established expertise in scaling technology companies, are actively seeking opportunities within the European AI landscape, recognizing the continent’s growing innovation capacity and access to talent. This cross-border investment activity can foster knowledge transfer, accelerate technological development, and contribute to the overall maturation of the European startup ecosystem.

The strong performance of the UK and France in attracting venture capital, particularly for AI startups, suggests that these regions are developing distinct competitive advantages. Their ability to nurture world-class AI research institutions and attract leading AI talent, as evidenced by the success of Mistral AI and Advanced Machine Intelligence, positions them as formidable contenders in the global AI race. This could lead to further concentration of AI development and investment within these countries, potentially creating innovation hubs that attract talent and capital from across Europe and beyond.

However, the simultaneous decline in deal volume, especially at the seed and early stages, warrants careful observation. While larger rounds can fuel rapid scaling for promising companies, a sustained decrease in the number of early-stage deals could indicate potential bottlenecks in the pipeline of future growth companies. This might necessitate increased focus on supporting nascent startups, fostering innovation at the grassroots level, and ensuring that the venture capital ecosystem remains accessible to a diverse range of entrepreneurs with groundbreaking ideas, even if they require smaller initial investments.

The methodology employed by Crunchbase, which relies on reported data and incorporates currency conversions at prevailing spot rates, provides a standardized yet dynamic view of the venture funding landscape. It is important to acknowledge that data lags, particularly at the seed stage, can mean that the full extent of early-stage investment may become more apparent in subsequent reporting periods. This dynamic nature of data collection underscores the need for continuous monitoring and analysis to fully grasp the evolving trends in venture capital.

In conclusion, the first quarter of 2026 marks a pivotal moment for European venture funding, characterized by a strong rebound driven by AI and a strategic shift towards larger, more focused investments. The continent is demonstrating its capacity to foster groundbreaking AI innovation, attracting significant capital and talent. While challenges related to deal volume at earlier stages persist, the overall trajectory suggests a dynamic and evolving venture capital market poised for continued growth and technological advancement.

Glossary of Funding Terms

To provide clarity on the metrics used in this report, the following definitions are provided based on Crunchbase’s methodology:

  • Seed and Angel: This category encompasses seed, pre-seed, and angel rounds. It also includes venture rounds of unknown series, equity crowdfunding, and convertible notes valued at $3 million USD or less (or its equivalent).
  • Early-Stage: This includes Series A and Series B rounds, as well as other round types. Crunchbase incorporates venture rounds of unknown series, corporate venture, and other rounds ranging from above $3 million up to $15 million.
  • Late-Stage: This category comprises Series C, Series D, Series E, and subsequent lettered venture rounds following the "Series [Letter]" convention. It also includes venture rounds of unknown series, corporate venture, and other rounds exceeding $15 million. Corporate rounds are included only if a company has previously raised an equity funding round from seed through a venture series.
  • Technology Growth: This refers to private equity rounds raised by companies that have previously secured a "venture" round, essentially encompassing any round from the previously defined stages.

The data presented in this report is derived directly from Crunchbase and reflects reported data as of April 2, 2026. Funding values are denominated in U.S. dollars unless otherwise specified, with foreign currencies converted at the prevailing spot rate on the date of reporting.

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Venture Capital & Startup Funding

NextFab Ventures: Pioneering Hard-Tech Innovation for a Sustainable Future

by Nila Kartika Wati
written by Nila Kartika Wati

Todor Raykov, Managing Partner of NextFab Ventures, sat down for an exclusive conversation as part of the "Meet a VC" member spotlight series, detailing the firm’s unique investment thesis, its strategic positioning within the venture capital landscape, and its unwavering commitment to backing hard-tech startups that address critical environmental and labor challenges. Raykov articulated a clear vision for supporting transformative technologies, emphasizing the firm’s role as an early-stage, long-term partner for companies shaping a more productive, safer, and environmentally responsible future.

Defining the NextFab Ventures Portfolio: A Focus on Deep Tech and Impact

NextFab Ventures distinguishes itself through a sharply defined investment thesis: nurturing pre-seed to Series A hard-tech startups that are actively working to mitigate environmental harm and eliminate hazardous manual labor. The firm’s core objective is to foster advancements that simultaneously enhance productivity, bolster safety, and promote resource efficiency across key sectors.

The firm’s primary areas of focus are cleantech, agtech, and robotics. This strategic alignment reflects a recognition of the profound impact these fields can have on addressing some of the most pressing global challenges of our time. Cleantech innovations are crucial for transitioning away from fossil fuels and reducing carbon emissions, while agtech solutions are vital for ensuring food security and sustainability in the face of a growing global population and changing climate. Robotics, when applied thoughtfully, offers the potential to automate dangerous or repetitive tasks, freeing human capital for more complex and creative endeavors.

Within these sectors, NextFab Ventures has cultivated a portfolio of groundbreaking companies. Flux Marine, for instance, is developing 100% electric outboard motors, a significant advancement in reducing pollution and noise in marine environments. Burro.ai is revolutionizing agricultural workforces with its user-friendly autonomous robots, designed to amplify labor in diverse settings such as berry farms, vineyards, greenhouses, and nurseries. Strella Biotech is employing biosensor technology to predict fruit ripeness, a development that promises to reduce food waste and optimize supply chains. ConnectDER is addressing the growing demand for renewable energy infrastructure with its innovative meter collar adapters, facilitating quick and cost-effective home connections for solar panels and electric vehicle chargers.

What truly sets NextFab Ventures apart, according to Raykov, is its early and deep commitment to promising ventures. "In many cases, we serve as the first institutional investor," Raykov stated, highlighting the firm’s willingness to take on significant early-stage risk. This early backing is often complemented by a long-term support structure, including the capacity for follow-on funding across multiple investment rounds. Beyond capital, NextFab Ventures provides extensive assistance in product development and commercialization, recognizing that hard-tech companies often require a more hands-on approach and a longer runway to achieve market traction. This comprehensive support model underscores the firm’s dedication to nurturing its portfolio companies through the entire growth journey, often spanning many years.

Navigating the Regional VC Landscape: A Niche in Hard Tech

The venture capital ecosystem in NextFab Ventures’ geographical region, which Raykov indicated is heavily influenced by the strong presence of universities and research hospitals, is predominantly oriented toward medical and biomedical ventures. This concentration of investment in life sciences reflects the region’s academic strengths and established research infrastructure. However, Raykov noted a burgeoning trend of promising hard-tech startups emerging from both academic institutions and regional innovation catalysts, citing the Philadelphia Open Innovation Tournament as an example of a program fostering such entrepreneurial activity.

Despite the prevailing regional focus, NextFab Ventures stands out as one of the few investors in its area dedicated to deploying capital and resources specifically for hard-tech startups. This strategic niche allows the firm to carve out a unique position and cultivate expertise in a less crowded investment space. Raykov also highlighted a compelling trend of "VC arbitrage" among many of their portfolio companies. These startups are successfully raising capital from higher-cost markets like New York and San Francisco, while concurrently establishing and expanding their operational teams locally. This strategy offers a significant competitive advantage, allowing them to acquire top talent and maintain operational efficiency at a fraction of the cost typically associated with major tech hubs. This approach not only optimizes financial resources but also taps into a rich pool of regional talent, fostering local economic growth and innovation.

The emergence of hard-tech startups in regions traditionally dominated by other sectors can be seen as a positive sign of economic diversification and the increasing recognition of the value of tangible, physical innovations. As the world grapples with challenges ranging from climate change to supply chain resilience, the importance of hard-tech solutions is only set to grow. The ability of companies to leverage regional strengths while accessing capital from broader markets demonstrates a sophisticated and strategic approach to startup growth.

The Value of NVCA Membership: Policy and Network Synergy

Membership in the National Venture Capital Association (NVCA) holds significant value for NextFab Ventures, particularly in two key areas: policy advocacy and network building. Raykov emphasized the critical importance of the NVCA’s work in defending carried interest, a fundamental element of the venture capital structure. He articulated a strong belief that any significant alteration or elimination of this structure could have a detrimental impact on capital flow to entrepreneurs nationwide. Carried interest, often referred to as "promote," is the share of profits that general partners (GPs) of a venture capital fund receive, typically after limited partners (LPs) have received their initial investment back plus a preferred return. Its structure incentivizes GPs to generate high returns, aligning their interests with those of their investors. Disrupting this mechanism could reduce the appetite for risk among VCs, potentially leading to less capital being available for early-stage, high-growth companies.

Beyond its crucial policy work, the NVCA’s extensive network has been a primary driver for NextFab Ventures’ membership. Raykov highlighted the invaluable opportunity to connect with and learn from fellow NVCA members. This facilitated knowledge sharing and relationship building are essential for strengthening the firm’s investment approach and enhancing its industry positioning. In the fast-evolving world of venture capital, staying abreast of market trends, emerging technologies, and best practices is paramount. Peer-to-peer learning and collaboration within a respected industry body like the NVCA provide a platform for such crucial exchanges. This interconnectedness can lead to co-investment opportunities, strategic partnerships, and a deeper understanding of the broader venture capital landscape, ultimately benefiting both NextFab Ventures and its portfolio companies.

The NVCA plays a vital role in advocating for the venture capital industry’s interests at the federal and state levels. Its efforts to educate policymakers on the economic contributions of venture capital and to ensure a favorable regulatory environment are crucial for fostering innovation and job creation. For firms like NextFab Ventures, which are focused on specific, often capital-intensive sectors like hard tech, having a strong advocacy voice is particularly important.

The Road Ahead for NextFab Ventures: Accelerating Hard-Tech Transformation

Looking forward, NextFab Ventures remains steadfast in its mission to empower visionary hard-tech startups to develop transformative technologies for a safer, more productive, and environmentally responsible future. Raykov acknowledged that the development of hard-tech startups presents unique and often substantial challenges. These obstacles can range from longer product development cycles and higher capital requirements to complex manufacturing processes and regulatory hurdles. However, he expressed confidence that these challenges become surmountable with the robust backing of NextFab Ventures’ experienced team of technical and business advisors.

The firm’s advisory board comprises individuals with deep expertise in various technical fields and business operations. This collective knowledge allows NextFab Ventures to provide critical guidance to its portfolio companies, accelerating their time-to-market and significantly enhancing their prospects for success. This hands-on approach is particularly vital for hard-tech ventures, where technical feasibility and market adoption are intrinsically linked to the successful engineering and manufacturing of physical products.

Raykov articulated a clear and compelling philosophy: "We firmly believe that software alone cannot build the future. Hardware is paramount." This statement underscores a critical perspective in today’s tech landscape, where software often garners the lion’s share of attention and investment. NextFab Ventures’ commitment to hardware innovation recognizes the foundational role that physical products and tangible technologies play in addressing real-world problems and driving progress.

The firm is dedicated to identifying, supporting, and growing the next generation of hard-tech entrepreneurs. These are individuals who envision a world where automation and connected devices unlock creative innovation, boost productivity, and, crucially, protect our planet. This vision extends beyond mere technological advancement; it is rooted in a commitment to sustainability and responsible innovation. The focus on automation and connected devices within the hard-tech sphere suggests an interest in the Internet of Things (IoT), advanced manufacturing, industrial automation, and other fields where the convergence of physical and digital technologies is creating new possibilities.

The future for NextFab Ventures appears to be one of continued focus and strategic growth. By concentrating on the challenging yet rewarding domain of hard-tech, the firm is positioning itself to be a key player in developing solutions for some of humanity’s most pressing issues. The emphasis on environmental responsibility, labor safety, and productivity gains suggests a long-term investment horizon, aligned with global trends towards sustainability and efficiency. As the world increasingly turns to technological innovation to solve complex problems, firms like NextFab Ventures, with their deep understanding of hard-tech challenges and commitment to long-term support, will be instrumental in shaping the future. The ongoing success of its portfolio companies will serve as a testament to its distinctive approach and its conviction that tangible innovation is fundamental to building a better tomorrow.

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Venture Capital & Startup Funding

Flapping Airplanes Emerges as a New Paradigm in AI Research, Focusing on Data Efficiency and Raw Talent

by Raul Delapena Setiawan
written by Raul Delapena Setiawan

Palo Alto, CA – January 28, 2026 – A new venture, Flapping Airplanes, has officially launched with a mission to redefine the trajectory of artificial intelligence development. The company positions itself as a "young person’s AGI lab," aiming to unlock the potential of data-efficient models, which it identifies as the critical bottleneck preventing the next leap in AI intelligence. This ambitious undertaking is spearheaded by a trio of founders with distinct yet complementary backgrounds: Ben Spector, a co-founder of the prominent Silicon Valley talent incubator Prod; his brother Asher Spector, a former debate champion and Stanford statistics Ph.D. candidate; and Aidan Smith, a Thiel Fellow and former Neuralink employee who studied at Georgia Tech.

The company’s approach diverges sharply from the prevailing trend of acquiring established "brand name" AI talent at any cost. Instead, Flapping Airplanes is deliberately cultivating a team of exceptionally gifted individuals, predominantly those who might have historically pursued Ph.D.s or entered quantitative finance. This focus on raw, untapped potential, the founders believe, is the key to unlocking novel solutions in AI research.

A Countercultural Approach to AI Development

Flapping Airplanes’ philosophy is built upon a foundational recognition that sheer scale of computational power or data volume alone is insufficient for achieving Artificial General Intelligence (AGI). The era of readily available, exponentially expanding datasets may be drawing to a close, with the internet’s data offering a finite resource. This makes data efficiency a paramount concern for future AI advancements. This perspective aligns with recent discussions within the AI community, notably highlighted by researchers like Dwarkesh Patel in his interviews with prominent figures such as Andrej Karpathy and Richard Sutton, who have emphasized the current inefficiency of large AI models. The company’s name itself, "Flapping Airplanes," is an intentional nod to the intricate and data-efficient mechanisms observed in biological systems, suggesting a source of inspiration for more economical AI architectures.

The Research vs. Scaling Debate in AI

The launch of Flapping Airplanes arrives at a pivotal moment in the AI landscape, characterized by a fundamental debate between the "scaling paradigm" and the "research paradigm." The scaling approach advocates for channeling immense societal and economic resources into expanding the capabilities of current Large Language Models (LLMs), with the hope that emergent properties will lead to AGI. This methodology prioritizes immediate, quantifiable gains and extensive computational infrastructure.

Conversely, the research paradigm posits that AGI is likely attainable through a series of significant, albeit potentially long-term, research breakthroughs. Proponents of this view suggest that the field is merely a few fundamental discoveries away from achieving AGI and that dedicated, long-horizon research, spanning five to ten years, is essential. While these two approaches are not mutually exclusive – research advancements can enhance the efficiency of compute investments, and new research often requires significant computational power – they lead to distinct strategic trade-offs. A compute-first strategy would heavily favor investments in hardware and short-term improvements, potentially neglecting the foundational scientific inquiry necessary for true paradigm shifts. A research-first strategy, however, would embrace a diversified portfolio of bets, including those with a lower probability of immediate success but with the potential to broaden the scope of what is achievable.

Concerns have been voiced within the AI community that the current emphasis on scaling, coupled with the diversion of top talent away from fundamental research, could inadvertently extend the timeline for AGI development. As previously articulated in analyses of AI capital expenditure, corporate environments often favor established, consensus-driven ideas over more radical, unconventional concepts. These unconventional ideas, however, are frequently the bedrock of significant scientific breakthroughs, which may require years to validate. Flapping Airplanes aims to bridge this gap by offering a research environment that mirrors the intellectual independence and long-term horizons of a Ph.D. program, while also addressing the compensation disparities between academia and large technology firms.

The last decade has underscored the immense economic value derived from fundamental scientific progress. Recognizing that traditional academic institutions may currently face challenges in competing with the resources of Big Tech in AI research, Flapping Airplanes is taking a proactive stance. Their singular focus on developing data-efficient AI models, inspired by biological intelligence, is a strategic bet on the most likely defining characteristic of the next wave of AI innovation. This guiding principle will inform their diversified investment in various research avenues.

Profit Motive vs. AGI Motive: Dual Drivers in AI

Flapping Airplanes Emerges as a New Paradigm in AI Research, Focusing on Data Efficiency and Raw Talent

The current AI ecosystem is driven by two principal, coherent motivations. The first is the profit motive, which rightly propels application builders to capitalize on the vast opportunities presented by AI’s underpenetration relative to its capabilities. This era is anticipated to foster a sustained period of innovation and wealth creation for developers and entrepreneurs.

The second, equally significant, motivation is the pursuit of AGI. This objective is rooted in the conviction that having unlocked a form of machine intelligence, the imperative is to continuously enhance and elevate its capabilities. Economically, progress in this domain promises substantial returns. The critical challenge lies in pursuing this objective efficiently, with a reasonable assurance that invested resources will translate into long-term positive outcomes. Flapping Airplanes positions itself as a pure-play entity dedicated to this AGI-centric objective.

The New Guard: A Team Assembled for Ambitious Goals

A key factor in the investment decision for Flapping Airplanes was the caliber of its founding team. Having closely observed nearly every candidate interviewed and every individual hired by the company, there is a strong consensus that this represents one of the most impressive teams assembled at the outset of such an ambitious endeavor.

Flapping Airplanes distinguishes itself to top talent through two core tenets that challenge the prevailing norms in Silicon Valley:

  • Focus on Raw Talent over Prestige: The company prioritizes individuals with exceptional cognitive abilities and a proven track record of creative problem-solving, irrespective of their institutional pedigree or previous company affiliations. This contrasts with the current trend of valuing "brand names" and high-profile affiliations above all else.
  • Commitment to Long-Term Research: Flapping Airplanes explicitly champions long-term research projects, providing the space and resources for deep investigation into fundamental AI challenges. This approach is in direct opposition to the pressure for immediate product releases and short-term deliverables that often characterize the tech industry.

These two foundational principles place Flapping Airplanes at odds with a prestige-obsessed culture. It is precisely this unwavering commitment to substance and the recognition of raw individual talent that propelled Ben Spector’s success at Prod. This "flame spotting" ability instills confidence in the audacious mission that Flapping Airplanes is now undertaking.

The company is actively seeking individuals who resonate with its vision. Interested candidates are encouraged to reach out to [email protected].

Broader Implications for the AI Ecosystem

The emergence of Flapping Airplanes signifies a potential recalibration of the AI development landscape. By actively addressing the data efficiency bottleneck and by prioritizing fundamental research, the company could accelerate breakthroughs that have eluded more conventional approaches. Its model of attracting and nurturing nascent talent, offering a research-intensive environment with long-term horizons, could serve as a blueprint for other organizations seeking to push the boundaries of AI.

The investment from a prominent venture capital firm like Sequoia Capital, as indicated by the publication of this article on their platform, signals a belief in this alternative approach. This endorsement suggests that the broader investment community is beginning to recognize the limitations of purely scale-driven AI development and is open to backing ventures that focus on foundational research and novel methodologies.

The success of Flapping Airplanes could have far-reaching implications, potentially leading to AI models that are not only more intelligent but also more sustainable and accessible, given their reduced reliance on massive datasets and computational power. This could democratize AI development and accelerate its application across a wider spectrum of industries and societal challenges. The company’s commitment to a "profit motive vs. AGI motive" duality suggests a balanced approach, aiming to achieve both impactful scientific progress and substantial economic value, a combination that has historically driven significant technological revolutions. The AI field will be watching closely as Flapping Airplanes endeavors to chart a new course toward the future of intelligence.

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Venture Capital & Startup Funding

Qdrant CEO Andre Zayarni Defines the Exploding Vector Search Market and Its $18 Billion Potential

by Evan Lee Salim
written by Evan Lee Salim

Andre Zayarni, Founder and CEO of Qdrant, a prominent player in the burgeoning vector database market, has offered a comprehensive perspective on the sector’s definition, its current trajectory, and Qdrant’s strategic positioning within it. In a recent interview, Zayarni articulated that the core of his company’s market is "vector search for AI," a foundational technology essential for nearly every modern artificial intelligence application. This encompasses critical functionalities such as Retrieval-Augmented Generation (RAG), recommendation engines, agentic workflows, and semantic search, all of which necessitate robust retrieval layers to bridge the gap between AI models and their underlying data.

The Evolving Landscape of Vector Search

Zayarni’s analysis underscores a fundamental shift in how AI applications are being built. Historically, AI models operated with more direct access to structured or semi-structured data. However, the advent of large language models (LLMs) and the increasing demand for nuanced, contextually relevant AI responses have highlighted the limitations of traditional data retrieval methods. Vector search, by representing data as high-dimensional numerical vectors, allows AI models to understand semantic relationships and retrieve information based on meaning rather than exact keyword matches. This capability is crucial for applications that need to process and generate human-like text, understand complex queries, or make personalized recommendations.

The vector database market, while relatively nascent, is experiencing exponential growth. Zayarni estimates the current market size to be approximately $3 billion, with projections suggesting it could expand to as high as $18 billion by the early 2030s, depending on various industry forecasts. These figures are indicative of a compound annual growth rate (CAGR) in the range of 25%, a testament to the rapid adoption and increasing criticality of this technology. Qdrant, therefore, operates within a highly dynamic and expanding technological frontier.

Qdrant’s Role and Vision

Qdrant’s mission, as defined by Zayarni, is to provide the essential retrieval infrastructure that powers these advanced AI applications. By offering a sophisticated vector database, the company enables developers and organizations to efficiently store, index, and query vast amounts of vector data. This capability is not merely an add-on but a core component for unlocking the full potential of AI, especially in scenarios requiring access to extensive, unstructured, or dynamically updated information.

The company’s positioning is clear: to be the backbone of AI’s information retrieval needs. This involves addressing the complex challenges of scaling vector search, ensuring low latency, high accuracy, and robust security for sensitive data. As AI models become more integrated into business operations and consumer products, the demand for efficient and reliable data retrieval will only intensify. Qdrant aims to meet this demand by offering a specialized, high-performance solution.

The Broader Addressable Market

Beyond the direct vector database market figures, Zayarni points to a significantly broader addressable market. This implies that the impact and adoption of vector search technology extend far beyond dedicated database solutions. It suggests that vector search capabilities will become an embedded feature across various data management and AI development platforms. The implications are that any entity dealing with large datasets and aiming to leverage AI for insights, automation, or enhanced user experiences will, in some capacity, benefit from or require vector search technology.

This expansion could be driven by the integration of vector search functionalities into existing cloud platforms, data warehouses, and AI development frameworks. As developers become more accustomed to using vector embeddings, the demand for seamless integration and simplified access will grow, further broadening the reach of this technology.

Market Dynamics and Competitive Landscape

The vector database market, while growing, is also becoming increasingly competitive. Several established technology companies and emerging startups are vying for market share. This competition, however, is often seen as a positive indicator of the market’s vitality and potential. It drives innovation, pushes for better performance, and ultimately benefits end-users by offering a wider range of choices and more advanced solutions.

Qdrant CEO Andre Zayarni Defines the Exploding Vector Search Market and Its Billion Potential

Key differentiators in this market, as alluded to by Zayarni’s focus on Qdrant’s core offering, include performance (speed and accuracy of searches), scalability (ability to handle massive datasets), ease of use and integration, cost-effectiveness, and specialized features tailored to specific AI use cases. Companies like Qdrant are likely investing heavily in research and development to stay ahead of these evolving demands.

Supporting Data and Industry Trends

The growth in AI investment is a significant tailwind for the vector search market. Venture capital funding for AI startups has surged in recent years, with a particular focus on companies developing foundational AI technologies and infrastructure. For instance, reports from various market research firms indicate a consistent upward trend in AI-related funding, with billions of dollars being deployed annually. This influx of capital fuels innovation and accelerates the adoption of technologies like vector databases.

Furthermore, the proliferation of large language models (LLMs) from companies like OpenAI, Google, and Anthropic has been a primary catalyst. These models, while powerful, often require external knowledge bases to provide accurate and up-to-date information, especially for specialized domains or proprietary data. RAG systems, which heavily rely on vector search for retrieving relevant context, have emerged as a dominant paradigm for enhancing LLM capabilities. This trend alone is a major driver for vector database adoption.

Timeline of Key Developments in Vector Search and AI:

  • Early 2010s: Initial research and development in vector embeddings and approximate nearest neighbor (ANN) search algorithms begin to gain traction in academic circles.
  • Mid-2010s: Libraries like Faiss (Facebook AI Similarity Search) and Annoy (Approximate Nearest Neighbors Oh Yeah) are released, making ANN search more accessible to developers.
  • Late 2010s: The concept of vector databases starts to emerge as specialized solutions for managing and querying vector data at scale. Early players and open-source projects begin to appear.
  • 2020-2021: The rapid advancement and public release of powerful LLMs like GPT-3 by OpenAI significantly increase the awareness and practical application of AI, highlighting the need for efficient information retrieval.
  • 2022-2023: The widespread adoption of RAG architectures becomes a dominant trend, directly driving demand for robust vector databases. Startups in the vector database space, including Qdrant, gain significant attention and funding.
  • 2024 onwards: The vector database market continues to mature, with increasing competition, feature development, and broader integration into enterprise AI stacks. Projections indicate substantial market growth into the next decade.

Customer Needs and Qdrant’s Solutions

Zayarni’s perspective on customer needs likely centers on reliability, performance, and ease of integration. For businesses looking to deploy AI-powered applications, the ability to retrieve relevant information quickly and accurately is paramount. Downtime, slow response times, or inaccurate results can have significant negative impacts on user experience and business operations.

Qdrant addresses these needs by offering a highly optimized vector search engine designed for production environments. Key features typically include:

  • High Performance: Optimized algorithms for fast indexing and querying of massive vector datasets.
  • Scalability: Architected to handle growing data volumes and user traffic, often with distributed deployment options.
  • Flexibility: Support for various vector types, data structures, and filtering mechanisms.
  • Developer Experience: Tools and APIs that simplify integration into existing AI workflows and applications.
  • Reliability and Durability: Features that ensure data integrity and system availability.

The company’s focus on RAG, recommendation engines, and agentic workflows suggests a deep understanding of the current and future demands of the AI development community. These are areas where sophisticated retrieval is not just beneficial but essential for creating truly intelligent and responsive systems.

Broader Impact and Implications

The growth of the vector search market, and by extension, companies like Qdrant, has profound implications for the broader technology landscape and the development of AI.

  • Democratization of Advanced AI: By providing accessible and scalable vector search solutions, companies like Qdrant are lowering the barrier to entry for developing sophisticated AI applications. This allows smaller businesses and individual developers to leverage powerful AI capabilities that were previously only accessible to large tech giants.
  • Enhanced AI Capabilities: The ability to efficiently retrieve and synthesize information from vast datasets empowers AI models to perform more complex tasks, generate more accurate and nuanced responses, and exhibit a deeper understanding of context. This leads to more capable chatbots, more personalized recommendations, and more intelligent automation.
  • New Business Models: The rise of vector search technology can enable entirely new business models centered around information retrieval, knowledge management, and AI-powered insights. Industries such as healthcare, finance, legal, and research can leverage these technologies to unlock new value from their data.
  • Data Privacy and Security: As vector databases handle sensitive information, there is an increasing focus on data privacy and security features. Solutions that offer robust encryption, access control, and compliance with regulations will be crucial for widespread adoption, especially in regulated industries.
  • Edge AI and Decentralization: While current vector databases are largely cloud-centric, future developments may see more efficient and distributed solutions for edge AI devices, enabling more intelligent and responsive applications at the point of data generation.

In conclusion, Andre Zayarni’s insights into the vector search market paint a picture of a critical and rapidly expanding sector that is fundamental to the advancement of artificial intelligence. Qdrant’s strategic focus on providing robust and scalable vector search solutions positions it as a key enabler of the next generation of AI applications, with a market potential that underscores the transformative impact of this technology on industries worldwide. The projected growth figures, coupled with the increasing integration of AI across all facets of business and society, suggest that the vector search market is poised for sustained and significant expansion in the coming years.

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Venture Capital & Startup Funding

I Sold My Startup A Year After Founding It. Here’s Why That Was The Fastest Way To Build Real-World Healthcare AI

by Azzam Bilal Chamdy
written by Azzam Bilal Chamdy

In October 2024, Louis Blankemeier and his co-founders embarked on a mission to translate their advanced Ph.D. research in artificial intelligence into tangible real-world applications. Their groundbreaking AI models possessed the remarkable ability to interpret complex medical imaging, including X-rays and CT scans, identifying tens of thousands of potential diagnoses and generating comprehensive radiology reports that closely mirrored the sophisticated reasoning processes of human radiologists. At a time when AI in radiology was largely confined to flagging a limited set of specific conditions, this represented a fundamental paradigm shift in diagnostic capabilities.

Less than a year after its inception, the nascent company found itself at a critical juncture. The founders were presented with a significant decision: pursue venture capital funding to maintain independent operations, or accept a strategic acquisition offer from Radiology Partners, the world’s largest radiology practice. This decision point underscored a common dilemma for tech startups, where the conventional wisdom often champions independence as the ultimate measure of ambition. However, for Blankemeier and his team, the question was reframed: what would it truly take to achieve their overarching mission of transforming healthcare accessibility on a global scale? Their analysis led to a divergent conclusion.

The landscape of clinical AI is characterized by stringent regulatory frameworks, protracted sales cycles, and intricate stakeholder dynamics. In such an environment, established structural advantages tend to solidify market positions and yield compounding benefits over time. Recognizing these inherent complexities, the founders concluded that merging forces—under a carefully structured agreement designed to preserve their operational velocity—would significantly enhance their probability of realizing their core mission: to dramatically increase global access to healthcare.

From Research Prowess to Clinical Readiness: Bridging the Gap

Blankemeier’s doctoral research at Stanford University involved training radiology AI foundation models on datasets that, at the time, seemed immense, comprising tens to hundreds of thousands of studies. While these models demonstrated strong academic potential and enabled the prototyping of novel capabilities across various diagnostic tasks, they fell short of the stringent safety and consistency standards required for production-level deployment in real-world clinical settings.

The persistent narrative suggesting that AI will render radiology obsolete often overlooks the profound complexity of the field. A single CT scan, for instance, can encompass ten high-resolution volumetric series, effectively generating a billion pixels of data when considering multiple scans and prior patient studies. This vast quantity of data encodes an amount of medical information equivalent to entire medical textbooks. Furthermore, real-world radiology frequently involves encountering rare but critical pathologies—edge cases that demand sophisticated diagnostic acumen. The team encountered a hard-learned truth early in their development: models that perform admirably in controlled research environments often falter when confronted with the inherent complexities of actual clinical practice.

This challenge is not unique to radiology. The development of self-driving cars offers a compelling parallel. A decade ago, progress in autonomous vehicle technology appeared impressive. However, the unpredictable nature of the real world continuously introduced new failure modes. Despite over a decade of substantial capital investment, only a limited number of companies have achieved a consistent level of reliability.

Essential Components for Building Robust AI Models

The pursuit of reliable AI in complex domains like radiology has illuminated several key patterns. Companies that have achieved the most significant advancements typically exert control over the entire system and attain scale early in their development. This includes ownership of the hardware (vehicles in the self-driving car analogy), the sensor technology, the data collection pipeline, sophisticated simulation environments, and the deployment infrastructure. This integrated approach, operating at scale, enables the continuous collection of rare edge cases, iterative model retraining, validation of improvements, and safe redeployment.

The principles governing success in radiology AI are remarkably similar. Achieving real-world efficacy necessitates access to massive, diverse historical datasets, augmented by live data feeds that consistently surface rare edge cases and detect distributional shifts. It requires substantial clinical resources and operational infrastructure to seamlessly integrate AI into existing clinical workflows, engineer systems capable of reliable performance at scale, conduct extensive research studies, secure necessary regulatory approvals, refine models safely, and implement continuous post-deployment performance monitoring.

Furthermore, advancements in frontier language models have underscored the critical role of continuous, high-quality human feedback in rendering AI models truly useful. This principle extends directly to radiology. In a future where AI assists in drafting radiology reports, every AI-generated draft must undergo rigorous review, editing, and final sign-off by a human radiologist. These edits serve as invaluable, high-quality signals that can be leveraged to enhance the AI models. The development of more accurate AI models can elevate radiologists’ diagnostic accuracy and capacity. Consequently, improved radiologist accuracy enhances the quality of future training data. Increased capacity allows radiologists to assume additional responsibilities, which, in turn, generates more data and high-quality corrections. This creates a powerful, self-reinforcing flywheel. Access to this critical correction data is a rare commodity in AI development and can only be meaningfully leveraged at a massive scale. Building these essential capabilities as a standalone AI startup would present formidable challenges.

Healthcare Adoption: Where Growth is Fueled by Evidence

In the healthcare sector, trust is a hard-won commodity, built upon demonstrable clinical efficacy, unwavering reliability, robust security, and meticulous regulatory compliance. For healthcare systems or radiology groups to adopt technology from a new startup, especially for workflows directly impacting patient care, requires compelling, real-world evidence.

Evidence generation in healthcare is not a byproduct of small-scale pilot programs. Instead, it is meticulously constructed through sustained performance across a diverse array of clinical settings, patient demographics, imaging modalities, and critical edge cases. When a system proves its mettle within the operational framework of the world’s largest radiology practice, it simultaneously establishes credibility across multiple crucial dimensions: efficacy, reliability, security, and scalability.

In sectors where human lives are at stake and the objective is to create enduring solutions, the most effective path to development often lies in building from within the very system one aims to improve. The decision by Cognita to be acquired by Radiology Partners, rather than pursuing independent venture funding, was not a concession but a strategic acceleration. This move provided them with the foundational infrastructure and immediate scale necessary to effectively deliver on their mission of significantly expanding global access to healthcare.

I Sold My Startup A Year After Founding It. Here’s Why That Was The Fastest Way To Build Real-World Healthcare AI

Background and Timeline of Cognita’s Development:

  • October 2024: Louis Blankemeier and co-founders launch their AI venture, drawing from their Ph.D. research in AI for medical image interpretation. Their initial focus is on developing sophisticated models capable of generating comprehensive radiology reports, a significant departure from existing AI capabilities in the field.
  • Early 2025: The company achieves notable advancements in AI model development, demonstrating strong performance on research-scale datasets. However, the team recognizes the substantial gap between academic demonstration and clinical readiness.
  • Late 2025: Cognita faces a pivotal decision point: secure venture capital for independent growth or accept an acquisition offer from Radiology Partners.
  • Early 2026: The acquisition by Radiology Partners is finalized. This strategic move is framed by the founders as an acceleration of their mission, providing access to the scale, data, and clinical infrastructure necessary for real-world impact.
  • 2026 – Present: Cognita, now operating as the AI business unit of Mosaic Clinical Technologies within Radiology Partners, focuses on integrating its advanced AI solutions into clinical workflows. The emphasis is on leveraging the practice’s vast operational footprint and data to refine AI models, generate robust clinical evidence, and drive widespread adoption.

Supporting Data and Context:

The global market for AI in medical imaging is experiencing rapid growth. Projections indicate a compound annual growth rate (CAGR) of approximately 20-30% over the next five to seven years, with market size expected to reach tens of billions of dollars. This growth is driven by the increasing volume of medical imaging procedures, the demand for improved diagnostic accuracy and efficiency, and the potential for AI to alleviate radiologist shortages.

However, the pathway to market for clinical AI is fraught with challenges. Regulatory hurdles, such as FDA clearance in the United States, can be lengthy and resource-intensive. The integration of new technologies into established hospital and clinic workflows requires significant change management, physician buy-in, and interoperability with existing IT systems. Furthermore, the financial models for AI adoption in healthcare are still evolving, with reimbursement for AI-driven services being a key area of development.

Radiology Partners, as the largest radiology practice in the U.S., manages radiology services for hundreds of hospitals and health systems, interpreting millions of imaging studies annually. This scale provides an unparalleled environment for validating and deploying AI solutions. The practice’s commitment to innovation and its extensive network of radiologists offer a unique platform for Cognita to demonstrate the clinical utility and economic benefits of its AI technology.

Analysis of Implications:

The acquisition of Cognita by Radiology Partners signifies a broader trend in the healthcare AI sector: the increasing importance of strategic partnerships and integration for market success. For AI startups, navigating the complexities of healthcare regulations, sales cycles, and clinical validation can be a formidable challenge. Partnering with established healthcare providers can provide the necessary scale, data, and clinical validation pathways to accelerate product development and adoption.

This strategic move by Cognita suggests a pragmatic approach to achieving impact in a highly regulated and complex industry. By embedding their AI technology within the operational fabric of Radiology Partners, they are positioned to:

  • Accelerate Data Acquisition and Model Refinement: Access to millions of real-world radiology cases will enable continuous improvement of their AI models, particularly in identifying rare conditions.
  • Generate Robust Clinical Evidence: Sustained performance across diverse clinical settings will provide the credible evidence required for broader market adoption and regulatory approval.
  • Influence Workflow Integration: Working directly with a large practice allows for the seamless integration of AI into existing workflows, maximizing its utility for radiologists.
  • Establish Credibility: Successful implementation within the largest radiology practice globally lends significant weight to their claims of efficacy and reliability.

This approach contrasts with the traditional startup narrative of prioritizing independence above all else. In the context of healthcare AI, where the ultimate goal is patient benefit and improved outcomes, a collaborative or integrated strategy may prove more effective in achieving widespread, meaningful impact. The success of Cognita’s model will likely serve as a case study for other AI companies operating in the healthcare space, highlighting the potential advantages of deep integration with established industry players.

Official Statements and Reactions (Inferred):

While direct quotes from Radiology Partners leadership regarding the acquisition of Cognita are not provided in the source material, their strategic decision to acquire the AI startup strongly suggests an alignment with the vision of enhancing diagnostic capabilities and operational efficiency through advanced technology. It can be inferred that Radiology Partners views Cognita’s AI as a critical tool to augment their existing services, improve radiologist performance, and ultimately deliver higher quality patient care at scale. The acquisition likely represents a commitment to staying at the forefront of medical imaging innovation and leveraging AI to address the growing demands on radiology services.

The article concludes by emphasizing that in sectors where lives are at stake and the goal is to build something enduring, the most effective approach is often to build from within the system one seeks to improve. The early sale of Cognita did not represent an endpoint but rather a strategic acceleration, providing the essential foundation to fulfill their mission of substantially increasing global access to healthcare. This strategic integration of cutting-edge AI research with the vast operational infrastructure of a leading radiology practice marks a significant step forward in the evolution of AI in medical diagnostics.

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Venture Capital & Startup Funding

7wire Ventures: Pioneering the Informed Connected Health Consumer Amidst Healthcare’s Digital Revolution

by Evan Lee Salim
written by Evan Lee Salim

The venture capital landscape, particularly within the dynamic and rapidly evolving healthcare sector, is constantly shaped by firms that possess a clear vision and the operational acumen to execute it. Among these leaders is 7wire Ventures, a Chicago-based firm dedicated to early-stage investments in healthcare technology. At the heart of their strategy lies a prescient thesis: the ascendance of the "Informed Connected Health Consumer." This philosophy posits that the future of healthcare hinges on empowering individuals with the knowledge, data, and support necessary to make more informed decisions, access care with greater ease, and ultimately achieve superior health outcomes at a reduced cost.

This deep dive into 7wire Ventures, as part of the "Meet a VC" series, offers an exclusive look into the firm’s distinct approach, its portfolio’s defining characteristics, its strategic positioning within the Chicago and Midwest venture ecosystem, and its forward-looking perspectives on the future of healthcare innovation. The conversation with Alyssa Jaffee, Partner at 7wire Ventures, illuminates the firm’s unique blend of investment expertise, hands-on operating experience, and an extensive strategic network, all geared towards catalyzing the next wave of transformative healthcare solutions.

The 7wire Ventures Differentiator: A Vertically Integrated Powerhouse

What truly sets 7wire Ventures apart in a crowded venture capital market is its vertically integrated model. This approach seamlessly combines deep investment insight with decades of practical operating experience and a robust strategic network. The firm is helmed by a quartet of seasoned healthcare operators and investors: Glen Tullman, Lee Shapiro, Alyssa Jaffee, and Robert Garber. Their collective experience spans the entire lifecycle of healthcare businesses, from their inception and scaling to successful exits, including significant public offerings and multi-billion-dollar mergers.

This deep operational bench is not merely a theoretical advantage; it translates into tangible support for portfolio companies. 7wire Ventures actively engages in board-level governance, facilitates crucial commercial introductions, assists with executive recruitment, and provides strategic guidance that goes far beyond typical investor involvement. This hands-on approach is exemplified by the firm’s early investment and instrumental role in the growth of Livongo, a pioneering digital health company. Livongo’s subsequent $18.5 billion merger with Teladoc Health stands as one of the most significant and successful digital health exits in history, underscoring 7wire’s ability to identify and nurture category-defining ventures.

Furthermore, 7wire Ventures has cultivated the Connected Consumer Health Coalition, a formidable network comprising senior-level relationships across the healthcare and technology industries. This coalition brings together key stakeholders, including major payers, leading health systems, and influential pharmaceutical companies. This extensive network not only provides 7wire with proprietary deal flow – access to promising companies before they reach the broader market – but also offers invaluable strategic support to their portfolio companies. These relationships act as powerful distribution channels and provide critical real-world insights, helping portfolio firms navigate the complexities of the healthcare landscape and accelerate their market penetration.

Defining the Portfolio: A Thesis-Driven Approach to Empowering Consumers

The 7wire Ventures portfolio is not a random collection of investments; it is meticulously curated through a high-conviction, thesis-driven strategy centered on the Informed Connected Health Consumer. Every investment decision is anchored in the firm’s unwavering belief that the future of healthcare is one where individuals are empowered to take greater ownership of their health journeys. Consequently, the portfolio consists exclusively of digital health companies designed to enhance access to care, reduce healthcare expenditures, and improve patient outcomes across a diverse spectrum of healthcare needs. This includes innovations in virtual care delivery, sophisticated chronic condition management platforms, user-friendly benefits navigation tools, and critical behavioral health solutions.

A common thread weaving through these companies is their reliance on scalable, technology-enabled platforms that effectively bring care closer to the consumer. Whether delivered through intuitive mobile applications, immersive virtual interfaces, or intelligent AI-powered tools, the companies backed by 7wire offer digital-first solutions that are designed to integrate seamlessly into the workflows of payers, providers, and employers. This integration is crucial for driving meaningful improvements in both health outcomes and affordability.

7wire Ventures maintains a deliberately concentrated portfolio, typically comprising 10 to 15 companies per fund. This selective approach reflects an exceptionally rigorous sourcing process, with fewer than 1% of evaluated opportunities being pursued. This disciplined strategy allows the firm to engage deeply and collaboratively with each investment. They provide dedicated board leadership, strategic advisory services, crucial executive hiring support, and active assistance in accelerating commercial growth.

While each company addresses a distinct segment of the healthcare ecosystem, they are unified by a consumer-first orientation and the potential to emerge as category-defining platforms. The portfolio spans a range of innovative models, including virtual and hybrid care delivery systems, specialized solutions for conditions like oncology and mental health, tools for navigating complex care pathways, and platforms promoting cost transparency. Notable examples of fast-growing ventures within the portfolio include Transcarent, NOCD, and Payzen. Collectively, these investments demonstrate 7wire Ventures’ consistent, research-backed strategy to champion the next generation of healthcare innovators who are committed to transforming the system through a consumer-centric lens.

The Chicago and Midwest Advantage: A Hub for Healthcare Innovation

The venture capital landscape in Chicago and the broader Midwest region offers a unique and strategically advantageous position, particularly for fostering healthcare innovation. As one of the largest and most diverse healthcare markets in the United States, the region is home to a formidable concentration of leading health systems, major payers, prominent pharmaceutical companies, thriving life sciences hubs, world-renowned academic medical centers, and a significant number of Fortune 500 employers. This dense and multifaceted ecosystem provides 7wire Ventures with a distinct advantage: close proximity to the very stakeholders that their portfolio companies aim to serve and partner with.

This geographical concentration facilitates easier collaboration, faster feedback loops, and more efficient pilot programs, all of which are critical for early-stage healthcare technology companies seeking to validate their solutions and scale their operations. The Midwest’s inherent strengths in healthcare infrastructure and established industry players create a fertile ground for innovation that directly addresses real-world challenges.

Moreover, the Midwest location often imbues innovators with a capital-efficient mindset, reinforcing 7wire Ventures’ own value-focused and operator-driven investment strategy. Companies emerging from this region tend to exhibit a disciplined approach to building their businesses, coupled with a profound understanding of the intricacies of healthcare delivery. This aligns perfectly with 7wire’s mission to support companies that are dedicated to empowering the Informed Connected Health Consumer and delivering tangible value.

The NVCA’s Role: Amplifying Advocacy and Collaboration

Membership in the National Venture Capital Association (NVCA) plays a significant role in 7wire Ventures’ broader strategy and its commitment to advancing the venture capital ecosystem. Alyssa Jaffee’s active participation on the NVCA Board underscores the firm’s dedication to furthering the goals of the venture capital community and advocating for policies that foster innovation.

Through the NVCA, 7wire Ventures gains access to a comprehensive suite of resources, cultivates valuable relationships, and benefits from robust advocacy efforts that directly support its mission and investment thesis. The NVCA serves as the preeminent voice for the U.S. venture capital industry, providing a powerful platform to influence public policy on critical issues related to innovation, capital formation, and entrepreneurship. For a firm like 7wire, which operates at the intersection of healthcare and technology, representation on pivotal topics such as healthcare regulation and tax policy is invaluable. These policy areas profoundly impact the growth trajectory and scalability of early-stage digital health companies.

Beyond policy advocacy, the NVCA fosters invaluable peer-to-peer connections and professional development opportunities through its extensive network of member firms. These forums enable 7wire’s team to engage with other leading investors, contribute to thematic working groups, and remain at the forefront of emerging venture trends and market dynamics.

Finally, NVCA membership enhances 7wire Ventures’ visibility within the broader innovation ecosystem. Through speaking engagements, thought leadership features, and participation in industry-wide initiatives, the association helps to spotlight 7wire’s differentiated investment thesis and elevate the profiles of its portfolio companies. Collectively, NVCA membership strengthens 7wire Ventures’ capacity to advocate, collaborate, and lead in building the next generation of consumer-first healthcare companies that are poised to reshape the industry.

The Road Ahead: Digital Health’s Imperative Moment

7wire Ventures believes that the current era represents one of the most pivotal moments in the history of digital health. The confluence of escalating healthcare costs, the pervasive challenge of chronic conditions, and a surging demand for improved access and greater health equity has placed the traditional healthcare system under immense pressure to transform. In this environment, digital solutions are increasingly recognized not as supplementary tools, but as fundamental answers to these pressing issues. Stakeholders across the entire healthcare continuum are accelerating their adoption of digital technologies, and the venture capital market is responding with robust capital flows, significant exit activity, and a wave of strategic consolidation.

Looking forward, 7wire Ventures is strategically focusing its investment efforts on areas where both urgent societal needs and outsized opportunities converge. These include critical domains such as aging-in-place solutions, advanced chronic condition management platforms, and AI-driven tools that can deliver highly personalized and proactive care. The firm remains steadfast in its commitment to backing companies that empower the healthcare consumer, providing them not only with essential capital but also with invaluable operational expertise and strategic networks to facilitate their growth. True to its core thesis, 7wire Ventures continues to champion initiatives aimed at improving health outcomes, reducing healthcare costs, and fundamentally transforming the healthcare experience through relentless innovation and the unwavering empowerment of the consumer. The firm’s consistent track record and forward-looking strategy position it as a key player in shaping the future of a more accessible, affordable, and effective healthcare system for all.

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