Jack Kendall

Executive Overview

Jack Kendall is the Co-Founder and Chief Technology Officer (CTO) of Rain AI (Rain Neuromorphics), a venture-backed technology company specializing in brain-inspired AI hardware and architecture. Based in San Francisco, CA, Jack Kendall brings an interdisciplinary background spanning neuroscience, physics, and computing to his leadership at Rain AI. Kendall’s mission, as articulated on Rain AI’s official page, is to “build a brain,” reflecting his vision of advancing artificial general intelligence (AGI) through neuromorphic engineering.

Recent public statements and news coverage indicate that Rain AI, under Kendall’s technical leadership, is operating in a highly competitive and dynamic emerging market. In April 2025, Kendall publicly acknowledged to stakeholders that the company was “rapidly depleting its cash reserves and urgently” seeking additional funding, underscoring the financial pressures common in the deep-tech AI sector. As of May 2024, Rain AI had raised $8.1 million in venture funding, with earlier participation from Epic Venture Partners and an initial seed round of $150,000 in 2018 (source: Clay, Omdia Market Radar).

Professional Profile

Current Role & Responsibilities

• Position: Co-Founder & CTO, Rain AI (Rain Neuromorphics)

• Location: San Francisco Bay Area (Rain AI was founded in Florida, operations now in California)

• LinkedIn: [Jack Kendall - LinkedIn](http://www.linkedin.com/in/jack-kendall-21072887)

• Notable Achievements:

• Oversaw rapid prototyping and development of analog/memristor-based AI chips.

• Guided Rain AI through Y Combinator and subsequent venture funding rounds.

• Leads technical strategy, IP development, and external research partnerships.

Technical Expertise & Research

Kendall is recognized as an inventor and interdisciplinary researcher with advanced knowledge in the following domains:

• Neuromorphic Engineering: Architecting AI processors that mimic biological brains for increased energy efficiency and scalable learning.

• Memristor Crossbars: Published research on analog and sparse matrix architectures for deep neural computation.

• Key paper: Activity-difference training of deep neural networks using memristor crossbars (Nature Electronics 6, 2023, co-authored as JD Kendall).

• Deep Learning & Materials Science: Explores the hardware-software co-design of AI chips and new materials critical for next-generation edge and data center AI applications.

Jack Kendall’s research work is accessible via [Google Scholar](https://scholar.google.com/citations?user=4DRTV4sAAAAJ&hl=en), where he is cited for his expertise in deep learning, circuit theory, and advanced materials.

Industry Position & Public Visibility

• Thought Leadership: Kendall is a frequent speaker at conferences (e.g., Gainesville Innovation Summit 2024), and has discussed the implications of AGI and neuromorphic chips publicly, including recent predictions on the timeline for achieving AGI (“by 2025”).

• Media Citations: Cited in technology analyses (Omdia, Deepwater Asset Management), and interviewed regarding the technological and financial challenges in the competitive AI processors market.

• Collaborative Ventures: Drives Rain AI’s industry and research partnerships, including contributions to academic workshops (e.g., NeurIPS 2024) and support for external research in brain-inspired learning algorithms.

Company Context: Rain AI

• Founded: 2017 (by Gordon Wilson, Jack Kendall, and Juan Nino)

• Core Product: Neuro-inspired AI processors leveraging analog/memristor technology for scalable, energy-efficient computation across edge and data center environments.

• Recent Developments: The company is working on pivoting to SRAM-based compute architectures in response to market and technical feedback (as of late 2024).

• Financial Status: As of April 2025, Rain AI had raised over $8.1 million and was actively seeking additional funding due to cash runway constraints.

Strategic Insights

Jack Kendall’s positioning as CTO places him at the intersection of cutting-edge research and product commercialization in a rapidly evolving market for AI hardware. His public focus on “building a brain” signals openness to innovative partnerships in neuromorphic and edge AI, as well as an emphasis on scalable, energy-efficient solutions over traditional digital AI accelerators.

Kendall’s technical credibility is well-established through his publications, patent filings, and conference presentations. His direct engagement with both researchers and investors positions him as a key decision-maker in evaluating new technologies and strategic collaborations essential for Rain AI’s product development and competitive positioning.

Given recent statements about funding urgency and technology pivots, Rain AI under Kendall’s technical leadership is likely to be highly responsive to differentiated value propositions that address power performance, scalability, ecosystem integration, and time-to-market for novel AI systems.

Key Public Content

• [How AI Processors Can Help Us Understand the Brain (YouTube, 2018)](https://www.youtube.com/watch?v=w4z2Ov2_0F8)

• [The Future of AI: Jack Kendall – Gainesville Innovation Summit (2024)](https://www.youtube.com/watch?v=TDQbV5Izggo)

• [Research Publications](https://scholar.google.com/citations?user=4DRTV4sAAAAJ&hl=en) on Memristor Crossbars and In-Memory Computing

Summary of Actionable Points

• Engagement with Jack Kendall should be highly technical and solution-oriented, with a focus on next-generation AI hardware, efficiency, and scalability.

• Opportunities exist for value-added partnerships in research, hardware ecosystem development, and co-innovation given Rain AI’s current technology and funding trajectory.

• Kendall is a primary technical stakeholder for external collaborations, especially those involving advanced semiconductor materials, analog computing, and brain-inspired architectures.

Jack Kendall’s current priorities center on bridging the gap between neuroscience and AI hardware at scale, ensuring Rain AI can compete as the industry transitions toward ever more efficient and intelligent machine learning platforms.