AgenticAI

[chris macrae]

Agent Jensen Huang's guided tour to most unique partnerships in best for ai and human worlds

You could of course apply different genius subjects to // analyses of purposeful ai partnership mapping

leading genii 

• Demis Hassabis / Google DeepMind
  DeepMind has been a foundational force in reinforcement learning and embodied AI (e.g., Gemini Robotics models, AlphaFold for biology-to-robotics crossover). NVIDIA partners closely (e.g., for robotics simulation and agentic AI in smart cities/drug discovery), but DeepMind develops its own reasoning/planning architectures. Huang has praised their work on physical world understanding (e.g., causality, multi-modal perception), making them a peer innovator in the "perception → reasoning → action" loop essential to robotics.
• Musk
• Dario Amodei / Anthropic
  Focused on safe, interpretable AI with strong reasoning (Claude models). Anthropic collaborates on NVIDIA infrastructure while pursuing alignment-first agentic systems. This complements NVIDIA's push for trustworthy physical AI (e.g., in robotics safety), positioning Anthropic as a philosophical/ethical co-platform builder in high-stakes embodied applications.
• Palantir (Alex Karp)
  Palantir integrates NVIDIA Nemotron models into its Ontology for enterprise agentic AI (e.g., fraud detection, supply chain reasoning). Karp's vision of "software-defined everything" aligns with Huang's tool-use philosophy—Palantir acts as a co-platform for turning raw AI into operational intelligence across industries, especially defense/government robotics-adjacent workflows.
• Jensen Huang himself (via NVIDIA ecosystem builders like Siemens, Dassault Systèmes)
  These industrial giants co-develop platforms (e.g., Siemens' Industrial AI OS with Omniverse digital twins, Dassault's agentic 3DEXPERIENCE platform). They aren't pure AI natives but extend NVIDIA's stack into real-world physical/digital convergence—much like Musk does with manufacturing scale.

Hrok Case fed in cisco and jensen huang transcript fen 2029- whats unique purpose of their partnership (2 frames chartering; jensen's purpose agency) -more detail on method ask [e-mail], 1995 author of brand architecture UPP (Unique Partnering Purpose) chartering genre with EIU

Tying the Two Purpose Analysis Ideas Your first idea from 30 years ago—auditing uniquely purposeful organizations and networks for intergenerational win-wins across stakeholders (owners, consumers, coworkers, partners)—aligns well with evaluating AI ecosystems today. It emphasizes long-term trust and productivity over short-term extraction, auditing what would be uniquely missed if the network vanished. Jensen Huang's lens complements this: for most AI users, skip deep foundation model scrutiny and instead audit the purpose of those building and deepening the models (e.g., their unique data maps and intent). This shifts focus to the "why" behind AI creators, ensuring alignment with win-win outcomes.Applying both to the transcript: NVIDIA and Cisco emerge as purposeful leaders in an AI network that's reinventing computing. The dialogue highlights a shift from explicit programming (pre-recorded, rigid) to implicit, generative AI (dynamic, contextual, abundant). This creates win-wins by democratizing intelligence—reducing costs, accelerating problem-solving, and enabling industries to tackle "it depends" challenges (intuition-based, no fixed equations). Trust is built through secure, scalable infrastructure that benefits stakeholders: enterprises gain transformative tools; partners like chip designers (Synopsis, Cadence) revolutionize workflows; end-users get agentic AI (reasoning, planning, tool-using) for real-world impact. If this partnership didn't exist, enterprises would miss out on accessible, secure AI factories, leading to slower innovation and competitive disadvantages.Unique Purpose of NVIDIA and CiscoFrom the transcript, NVIDIA's unique purpose is to create an "abundance of intelligence" by reinventing the entire computing stack for AI. This goes beyond processing to include storage, networking, and security, evolving AI from memorized chatbots to agentic systems that reason, plan, use tools, and solve novel problems. Huang emphasizes applying "infinity" (unlimited scale) to hard problems, pivoting NVIDIA to enable every company to become a "technology-first" entity—escaping atomic limitations (physical constraints) via electrons (digital abundance). Unique data maps include vast training on global data, self-supervised learning, and models scaling to trillions of parameters. This purpose mediates win-wins: accelerating Moore's Law by a millionfold over a decade, exposing IT to a $100T global economy (e.g., digital chauffeurs in self-driving cars worth more than the vehicle), and urging experimentation ("let a thousand flowers bloom") while focusing on core essence (e.g., chip design for NVIDIA itself).Cisco's unique purpose is to provide the controllable, secure backbone for this AI reinvention, integrating NVIDIA's tech into its networking and security pillars. Robbins highlights the partnership on AI factories—high-performance AI with Cisco's manageability—to ensure enterprises don't fall behind. Cisco mediates win-wins by making AI deployable at scale: performance without chaos, sovereignty for proprietary data (on-prem vs. cloud), and human-AI loops where AI captures experiences as IP. If Cisco vanished, the AI ecosystem would lack trusted networking/security, leading to fragmented, insecure deployments—undermining stakeholder productivity (e.g., no safe "on-prem" for sensitive questions, as Huang notes).Together, they form a purposeful network: NVIDIA drives the AI engine; Cisco ensures it's steerable and safe. This addresses intergenerational spans by building AI that learns perpetually (refreshing models, patching via learning), creating abundance for coworkers (augmented labor), consumers (personalized solutions), and partners (tool integration like SAP/ServiceNow).Neighboring Partnerships of NVIDIA Advancing Agentic AI in Win-Win WaysBased on the transcript and current ecosystem analysis, here are key business/tech partners of NVIDIA purposefully advancing agentic AI (autonomous, reasoning/planning/tool-using systems). These are "neighboring" in that they build on NVIDIA's stack (e.g., Nemotron models, NIM microservices, Cosmos platform) for collaborative innovation. They emphasize win-wins: shared expertise, open models/data, industry-scale efficiency, and long-term value (e.g., reducing costs, accelerating discovery, augmenting human skills without replacement). I've focused on practical, verifiable examples from 2025-2026 developments, listing 8 to keep it concise.

Partner	Description of Partnership and Win-Win Advancement of Agentic AI
Dassault Systèmes	Integrates NVIDIA's AI infrastructure (Nemotron models, CUDA-X) with Dassault's Virtual Twins for an "agentic 3DEXPERIENCE platform." Agents act as virtual companions, tapping industrial context for trusted intelligence in biology, materials, and engineering (e.g., predicting physics behaviors instantly). Win-win: Dassault gains AI scale; NVIDIA accesses validated world models; industries get faster innovation (e.g., molecule discovery) without atomic limits, benefiting stakeholders like researchers and manufacturers over generations.
Synopsys	Expands accelerated computing/digital twins for engineering/design, infusing NVIDIA's agentic AI into Synopsys' tools. Agents reason through complex simulations (e.g., chip dependencies with trillions of nodes/edges). Win-win: Synopsys revolutionizes workflows (as in transcript); NVIDIA accelerates its own chip design; partners/enterprises gain abundance (year-long tasks in hours), fostering trust via precise, explicit tools (e.g., F=ma integrations).
Alphabet (Google)	Decade-long expansion for agentic AI in robotics, drug discovery, grid optimization, and smart cities. Uses NVIDIA's platforms for autonomous planning/acting (e.g., Gemini integration from transcript). Win-win: Google scales agentic models; NVIDIA taps Alphabet's data maps; global industries benefit (e.g., robotics for human suffering alleviation), creating intergenerational productivity (e.g., curing diseases at scale).
Palantir	Integrates Nemotron models into Palantir's Ontology for specialized AI agents in fraud detection, supply chain. Agents plan/reason autonomously on enterprise data. Win-win: Palantir builds integrated stacks; NVIDIA extends open ecosystem; enterprises get actionable insights, turning data into IP with win-wins for security/coworkers (e.g., augmented decision-making).
Salesforce	Adopts Cosmos/Nemotron for physical AI robots as enterprise agents (e.g., workplace productivity). Agents turn robots into reasoning companions for tasks like inventory. Win-win: Salesforce enhances CRM with physical actions; NVIDIA advances agentic blueprints; businesses gain efficiency, escaping atomic constraints for long-term stakeholder value (e.g., human-AI loops capturing experiences).
ServiceNow	Builds on NVIDIA's tool-use breakthroughs (transcript mention) for agentic workflows in IT/service management. Agents automate reasoning/planning in operations. Win-win: ServiceNow leverages explicit tools; NVIDIA promotes abundance; enterprises reduce costs (e.g., real-time problem-solving), benefiting coworkers/partners with trusted, scalable systems.
Hitachi	Uses Cosmos for traffic/ workplace AI agents, integrating NVIDIA's physical AI for smart cities. Agents optimize grids/dependencies autonomously. Win-win: Hitachi applies domain expertise; NVIDIA scales perception/reasoning; societies gain sustainability (e.g., energy abundance), mediating win-wins across consumers/partners over intergenerational infrastructure.
Uber	Adopts Cosmos/Alpamayo for autonomous vehicle agents (e.g., digital chauffeurs from transcript). Agents handle perception/planning in real-time. Win-win: Uber transforms mobility; NVIDIA exposes to $100T economy; riders/drivers benefit from safer, abundant transport, with long-term productivity for urban networks.

AMD agentic AI collaborations

Could you be more specific anout cisco and nvidia partnership pyepose. For example when you say cisco ensures nvidia ai engines are steerable and safe- I imagine many nvidia enterprise to enetrprise partners would claim that. Is it that cisco has some indutsry (value chain) segments or geonomic regions that it is uniquely purposeful in. I forget exact o0rigin of cisco but imaginme it has about 30 years ;earning curve in midst of west cpast scaling digital and big business to business

Specifics on Cisco and NVIDIA Partnership PurposeTo address your query directly: Yes, many NVIDIA enterprise partners (e.g., Dell, HPE, Microsoft Azure) could broadly claim to make NVIDIA's AI engines "steerable and safe" by providing infrastructure layers like compute, storage, or cloud orchestration. However, Cisco's role is uniquely differentiated through its historical dominance in enterprise networking and security, which forms the "backbone" for AI deployments. This isn't just about hosting AI; it's about integrating NVIDIA's high-performance AI tech (e.g., GPUs, Spectrum-X Ethernet) into Cisco's controllable ecosystems to ensure scalability, low-latency connectivity, and built-in security at the network level—critical for real-world enterprise rollouts where data sovereignty and manageability are non-negotiable. This partnership, expanded significantly in 2025, positions Cisco as the go-to for turning NVIDIA's raw AI power into enterprise-ready "AI factories" that are secure, hybrid (on-prem/cloud), and optimized for specific value chains.Cisco was founded in 1984 (about 42 years by 2026) by Stanford University computer scientists Leonard Bosack and Sandy Lerner on the West Coast (San Francisco Bay Area). It originated from the need to connect disparate computer networks at Stanford, evolving into a global leader in B2B networking hardware, software, and services. Over decades, Cisco has scaled digital infrastructure for enterprises, focusing on interconnectivity amid the rise of the internet, cloud, and now AI. This gives it a deep "learning curve" in B2B ecosystems, particularly in managing complex, secure data flows for Fortune 500 companies—something that aligns perfectly with NVIDIA's push for abundant, agentic AI.Core Partnership Details (2025-2026 Developments)The partnership, initially deepened in February 2025, focuses on co-developing "AI factories"—integrated stacks for building, securing, and running AI at scale. Here's a breakdown:

• AI Factory Architecture (Cisco Secure AI Factory with NVIDIA): Launched March 2025 at NVIDIA's GTC conference. This is a validated, unified blueprint combining NVIDIA's compute (e.g., HGX/MGX platforms with H200 GPUs, BlueField-3 DPUs, SuperNICs) and AI software (NVIDIA AI Enterprise) with Cisco's networking (e.g., Nexus 9000/6000 Series switches, Silicon One chips) and security tools. It emphasizes security at the core, using Cisco's observability (e.g., via Splunk integration) to monitor and protect AI workloads. Two deployment options:
  • Vertically Integrated: Full-stack solution for quick setup, including Cisco UCS servers, NVIDIA hardware, VAST Data storage, and Cisco Optics.
  • Modular: Customizable with partners like Pure Storage, NetApp, Hitachi Vantara, and Red Hat for open-source automation.
  • Availability: General purchase by end of 2025; key components like Nexus Hyperfabric AI orderable from November 2025.
• Networking Integration (Making AI "Steerable"): Cisco embeds NVIDIA's Spectrum-X Ethernet (e.g., Spectrum-4 ASIC) into its switches like the N9100 Series (64-port, 800Gb Ethernet for ultra-low latency AI/HPC). Managed via Cisco's Nexus Dashboard, this provides "performance of AI with the controllability and manageability of Cisco." It handles congestion control and predictable performance for massive GPU clusters (thousands to hundreds of thousands), enabling "neocloud" (next-gen cloud) and sovereign cloud setups. This steerability comes from Cisco's control planes, which allow enterprises to orchestrate AI without chaos—e.g., diversifying supply chains for resilient deployments.
• Security Focus (Making AI "Safe"): Cisco's security pillar (reinvented for AI) includes built-in protections for data sovereignty—e.g., on-prem options to keep proprietary IP/questions private (as Huang emphasized in the transcript). It integrates with NVIDIA's DPUs for secure data processing, preventing leaks in hybrid environments. This is bolstered by Cisco's acquisitions like Splunk for AI-driven threat detection, making it safer than generic cloud partners.

Why Cisco is Uniquely Purposeful (vs. Other NVIDIA Partners)While partners like AWS or Google Cloud offer broad cloud hosting for NVIDIA AI, and hardware firms like Dell/HPE provide servers/storage, Cisco stands out in the networking and security layers—the "glue" for AI at enterprise scale. This isn't replicable easily because:

• Industry/Value Chain Segments: Cisco excels in segments requiring robust, secure connectivity:
  • Telecom/Service Providers: Powers AI innovations in neocloud and telecom networks, enabling low-latency AI for 5G/6G edge computing.
    cisco.com
  • Government/Sovereign AI: Focus on data locality (e.g., compliant with regulations like GDPR), ideal for public sector or defense where on-prem security is mandatory.
  • Finance and Manufacturing: Secure data centers for high-stakes AI (e.g., fraud detection, supply chain optimization) with trillion-node graph analytics.
  • Enterprise IT/Hybrid Cloud: Bridges hyperscale to on-prem, supporting AI workload fabrics for training/inference in B2B value chains like retail (e.g., Walmart analogies in transcript) or automotive.
• Geographic/Regional Strengths: Global reach but rooted in West Coast/US innovation hubs. Uniquely positioned for:
  • US/Western Markets: Decades of scaling digital B2B (e.g., Fortune 500 connectivity), with strong ties to Silicon Valley ecosystems.
  • Sovereign/Emerging Regions: Enables "sovereign clouds" in Europe/Asia (e.g., data residency for privacy laws), diversifying from US-centric hyperscalers. This supports regional AI adoption without full cloud dependency.
    blogs.cisco.com

In purpose terms (tying to your lenses): If this partnership vanished, enterprises in networking-heavy segments would miss secure, steerable AI scaling—leading to fragmented deployments, higher risks, and slower win-wins across stakeholders. Cisco's 40+ years mediate trust by ensuring NVIDIA's abundance is productively accessible, not just powerful.HPE NVIDIA partnership comparison

Jensen frequently celebrates companies like servicenow salesforce /// which seem to be leaping into deep data (and agentic personalization) which could never have been analysed before. Is Cisco more about updating existing big enterprises around operational minimums interfacing nvidia full stack so that they can then be confident that the new deep data analyses will offer cisco's best clients total transformatio0n eg cisco, nvidia and one of these deep mapmakers already used to the accelerated computing full stack as opposed to the traditional central computing one.

Jensen Huang has indeed highlighted companies like ServiceNow and Salesforce as exemplars of tools that agentic AI systems will leverage rather than replace, emphasizing their role in handling deep, contextual data that was previously unanalyzable at scale. In the transcript you provided earlier, he explicitly frames them as "tools" for AI—e.g., "If you were an Artificial General Intelligence, would you use the tools like ServiceNow and SAP... or would you reinvent a calculator? Of course you would just use a calculator." This aligns with his broader vision of agentic AI (systems that reason, plan, and act autonomously using existing tools) unlocking "abundance" by integrating with established platforms for personalization and efficiency. Recent quotes from Huang reinforce this: he argues that AI shifts from generative to agentic phases, where models fuse with enterprise tools to handle complex workflows, moving intelligence from clouds to real-world applications like factories and robotics.

He celebrates these companies for their domain expertise in areas like CRM (Salesforce) and workflow management (ServiceNow), which enable AI to apply "infinity" to previously intractable problems—e.g., trillion-node graph analytics for personalized insights—without starting from scratch.Cisco's purpose in this ecosystem is indeed more about modernizing existing big enterprises' operational foundations ("minimums") by seamlessly interfacing NVIDIA's full accelerated computing stack (e.g., GPUs, Spectrum-X networking, NIM microservices). This isn't just generic enablement; Cisco provides the secure, manageable "backbone" (networking, security, hybrid cloud) that gives enterprises confidence to layer on deep data analyses for total transformation. Unlike pure hyperscalers or hardware providers, Cisco's 40+ years in B2B networking allow it to bridge traditional central computing (explicit, pre-recorded systems) to accelerated, agentic AI (implicit, generative, abundant). This positions Cisco as the enabler for enterprises to "lift the hood" on AI (as Huang advises), ensuring sovereignty, low-latency, and governance while accelerating from Moore's Law pace to millionfold gains.

For instance, Cisco's Secure AI Factory with NVIDIA (launched March 2025, expanded September 2025) validates architectures for agentic AI, accelerating retrieval-augmented generation (RAG) pipelines with faster data extraction—directly enabling deep mapping of unstructured data that legacy systems couldn't handle.

This setup paves the way for tripartite collaborations where Cisco + NVIDIA + a "deep mapmaker" (like ServiceNow or Salesforce) deliver end-to-end transformation on accelerated computing. Here's a focused breakdown:Cisco + NVIDIA + ServiceNow (Strongest Tripartite Example)

• Partnership Dynamics: Expanded in March 2025 at NVIDIA GTC, ServiceNow integrates NVIDIA's Llama Nemotron reasoning models (for advanced agentic capabilities like decision-making in workflows) into its platform, while Cisco provides the secure infrastructure via Secure AI Factory and AI PODs.
  By April 2025, Cisco and ServiceNow deepened ties for AI security (e.g., integrating Cisco AI Defense with ServiceNow SecOps), enabling enterprises to govern agentic AI at scale.
  
  investor.cisco.com
  At ServiceNow's Knowledge 2025 (May 2025), Cisco was highlighted as a key partner for AI Agent Fabric integrations, ensuring seamless workflows across third-party agents.
  
  NVIDIA's AI Enterprise software further integrates for federal/high-assurance environments.
  
  blogs.nvidia.com
• Transformation Focus: This trio shifts enterprises from traditional computing to accelerated stacks for agentic personalization—e.g., ServiceNow's AI Control Tower evaluates and orchestrates agents using NVIDIA models on Cisco's secure networks, unlocking deep data maps (e.g., multimodal workflows) for real-time optimization. It's ideal for operational upgrades in IT/service management, where Cisco handles the "steerable" layer, NVIDIA the compute abundance, and ServiceNow the domain-specific mapping.
• Win-Win Outcomes: Enterprises gain confidence in transformation (e.g., vulnerability analysis agents co-developed by NVIDIA/ServiceNow, available early 2025).
  tahawultech.com
  This mediates intergenerational value: augmented labor, reduced risks, and perpetual learning (AI in the loop).

Cisco + NVIDIA + Salesforce (Emerging, Less Direct Tripartite)

• Partnership Dynamics: Cisco and NVIDIA's February 2025 expansion created a unified AI-ready architecture (e.g., Cisco Silicon One with NVIDIA Spectrum-X), simplifying data center builds.
  Salesforce integrates indirectly via Cisco's ecosystem—e.g., Webex AI Assistant (launched March 2025) connects with Salesforce for CRM actions like lead creation or record updates.
  
  nojitter.com
  NVIDIA's agentic tools (e.g., Nemotron) could extend to Salesforce's Einstein AI, but no explicit tripartite announcement by early 2026; it's more via broader integrations (e.g., Glean in Webex searches Salesforce data).
• Transformation Focus: Focuses on CRM personalization—e.g., agentic AI using Salesforce's deep data maps on NVIDIA-accelerated Cisco infrastructure for real-time insights. This updates ops minima (e.g., secure hybrid deployments) for transformation, but it's looser than the ServiceNow tie-up.
• Win-Win Outcomes: Enables enterprises to apply agentic AI to customer data without reinvention, aligning with Huang's tool-use philosophy.

In summary, Cisco acts as the operational updater and trust mediator, interfacing NVIDIA's stack to empower deep mapmakers like ServiceNow (more prominently) for full-stack transformation on accelerated computing. This creates win-wins: enterprises avoid falling behind, as Huang warns, by blooming experiments safely into curated, abundant AI ecosystems. If these partnerships vanished, big enterprises would miss secure scaling of agentic personalization, reverting to siloed, traditional computing.