Jeetu Patel

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Jeetu Patel

Jeetu Patel

@jpatel41

Technology Executive & Board Member. President & CPO, Cisco. Proud dad. Love design. Views are mostly my own, but sometimes not entirely my own ;-)

Silicon Valley Katılım Ekim 2008
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Jeetu Patel
Jeetu Patel@jpatel41·
There is such a profound shift occurring in the way that Agents alter our infrastructure requirements. We are entering a networking supercycle. It’s not because humans are consuming more content. It’s because machines are beginning to think, act, and transact continuously. Cisco's latest research on AI traffic patterns points to something much bigger than incremental bandwidth growth. Enterprise WAN traffic without agentic AI was projected to grow roughly 2.5x over the next decade. With agentic AI, that projection jumps to ~9x. And here’s the craziest part! After following this data closely, I believe even those numbers may prove to be wildly conservative. This is the first time when we have published a study like this where I feel that the projections might be off significantly and what we might think takes a decade happens in 3 years. Why? Because most people are still modeling AI like software. It is not. AI behaves more like a new species of digital labor. A SaaS app waits for humans. Agents do not. Agents continuously reason, retrieve, coordinate, negotiate, execute, and loop. At software speed. Without pause. 7x24. They never get sick. Don’t need a vacation. Dont get tired. Don’t need sleep. That creates a fundamentally different traffic architecture. The industry spent decades optimizing networks for bursty downloads, video streaming, and human-paced interactions, almost all of it flowing downstream to a person on the other end. AI traffic inverts that. A single agentic task can generate 450% more traffic than a human doing the same work. Roughly 70% of that is inference. And nearly 10% of AI flows now carry more upstream than downstream data, versus 0.5% for typical web traffic, because context continuously moves back into models. Network traffic is not just increasing in bandwidth. It is fundamentally getting reshaped. This last point matters most. The internet was built as a distribution system for content. AI is turning it into an active system for cognition. The path between agents and models is becoming the spinal cord of intelligence itself. When that path degrades, the agent degrades. Networking stops being a passive transport layer and becomes part of the intelligence stack. That changes everything about how we think about resiliency, observability, security, and capacity at the edge. We may be grossly underestimating what is coming. The future will not simply have more users online. It will have trillions of digital coworkers operating continuously on behalf of humans, enterprises, applications, and eventually physical systems. Humans click. Agents swarm. That difference is what creates a supercycle. This supercycle of inference infrastructure will not just be compute bound, but also memory and network bound. Take a look at the report here: cisco.com/c/dam/en/us/so…
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thomas murphy
thomas murphy@tankenginetom90·
@jpatel41 @grok remind me what was the main reason Cisco said for the last two rounds of layoffs?
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Jeetu Patel
Jeetu Patel@jpatel41·
I’ve been saying this for months, and the early data is beginning to support it. Five years from now, I believe AI will create more jobs than it eliminates. That may sound counterintuitive. But the data is proving this theory. Here are some factors worth considering. AI is exposing new human bottlenecks When one part of a workflow becomes dramatically faster, the constraint simply moves elsewhere. Organizations then need more people to remove the next bottleneck and capture the value AI has unlocked. Automation does not eliminate the need for human contribution. It often reveals how much more could be accomplished with it. AI fluency will become one of the world’s most valuable skills The emerging divide will not be between humans and agents. It will be between people who are highly fluent with AI and those who are not. AI-fluent people will not be 10% more productive. In some forms of work, they could be 50x or even 100x more effective. They will imagine better uses for AI, orchestrate agents and apply judgment where machines still fall short. These people will be scarce and enormously valuable. Productivity creates demand The assumption behind mass unemployment is that the amount of work the world needs is fixed. It isn’t. When technology becomes dramatically cheaper and easier to create, we do not simply produce the same amount with fewer people. We build more products, start more companies, solve previously uneconomic problems and serve markets that could never be served before. AI will lower the cost of ambition. The real risk, therefore, is not that humanity runs out of work. It is that millions of people are not prepared for how quickly the nature of work changes. The future will likely have more jobs. But they will not be the same jobs, performed in the same way, by people with the same skills. The imperative is not to protect people from AI. It is to help every person become fluent in it.
Marc Andreessen 🇺🇸@pmarca

This is happening in plain sight. The leading AI companies themselves are embroiled in the fiercest battle to hire the most highly paid software programmers in the history of the world. And so it goes.

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Jeetu Patel
Jeetu Patel@jpatel41·
@BenBajarin I’ve been on it for very long but I feel like since @elonmusk took it over, it has improved materially.
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Ben Bajarin
Ben Bajarin@BenBajarin·
Glad company execs are finally getting it. The more they realize this platform is the place to be and LinkedIn only leads to middle manager content they will be better off.
Jeetu Patel@jpatel41

The Alpha on X has grown so exponentially in the past two years. It is now the go-to platform for high-density learning. Unlike other platforms where you are drained after spending an hour and have nothing to show for, X tends to be energizing because each time you walk away smarter.

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Daniel Newman
Daniel Newman@danielnewmanUV·
@jpatel41 100% Going on LI is hard. Cringy most of the time. 👊🏻
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Jeetu Patel
Jeetu Patel@jpatel41·
The Alpha on X has grown so exponentially in the past two years. It is now the go-to platform for high-density learning. Unlike other platforms where you are drained after spending an hour and have nothing to show for, X tends to be energizing because each time you walk away smarter.
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Jeetu Patel
Jeetu Patel@jpatel41·
@KevinBCook Knowledge is no longer the difference maker. Questions are. You should share away!
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Kevin Brent Cook
Kevin Brent Cook@KevinBCook·
@jpatel41 You are so right. I’ve been screaming it: I have curated my X feed to be dominated by 30+ accounts that include at least 10 semiconductor engineers & experts, 10 energy grid experts, and 10 software/LLM experts. I can’t tell you who they are or I’d give away my alpha edge 😎
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Vijay Vijayasankar
Vijay Vijayasankar@vijayasankarv·
@jpatel41 Hardly been the case for me - maybe the algo is not helping. My time on X has shrunk by a lot in the last decade
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Jeetu Patel
Jeetu Patel@jpatel41·
@nrmehta I agree with you that heroics is not the answer to this kind of systemic change that is required. Here’s my take: You have to create habit forming behaviors within the organization for sure. Biggest ingredient is an internal executive champion, clear story around the benefit if AI wildly succeed and a culture of speedy execution where being early to market becomes a matter of habit. So in short, you need: 1. Champion 2. Judgement 3. Conviction 4. Speed 5. Economics
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Nick Mehta
Nick Mehta@nrmehta·
That's an interesting twist Jeetu. I guess the followup question is what are the ingredients for moat creation? As an example of it depends on the brilliance of one founder or CEO it could be brittle. Amazon's culture from retail could be an example where they learned to built moats relevant to low margin businesses.
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Jeetu Patel
Jeetu Patel@jpatel41·
This is a fascinating debate because it is the question for every company to ponder. What is the durable moat of your company? @JayaGup10 raises a great question that if the judgment that made your company exceptional becomes embedded in shared AI models, what remains your durable advantage? Who ultimately owns the learning? @levie offers an equally compelling counterpoint. Every company combines its data, workflows, culture, and people differently. That system of execution may continue to create differentiated outcomes, even if everyone has access to similar models. We’ve been wrestling with a version of this ourselves. At @Cisco, we build our own silicon. That’s one of our strategic advantages. AI can dramatically compress the cycle time for solving incredibly hard engineering and physics problems. That’s an enormous opportunity. But it also raises an important question. If the best solutions eventually become part of the collective intelligence available to everyone, does today’s moat simply become tomorrow’s baseline? That leads me to a different way of thinking about it. Maybe AI doesn’t eliminate competitive advantage. Maybe it dramatically shortens its half-life. If that’s true, then the goal isn’t to build a moat that lasts forever. It’s to build an organization that creates new moats faster than competitors can copy the old ones. The enduring advantage isn’t a particular technology, workflow, or even a single breakthrough. It’s the capability to repeatedly invent the next advantage before the current one becomes commoditized. In that world, AI won’t replace humans. It will continually commoditize layers of work, pushing people toward the next frontier where differentiation can still be created. Perhaps the ultimate moat isn’t proprietary knowledge. It’s becoming exceptionally good at continuously creating new instincts. I don’t know if that’s where we’ll end up, but it feels like one of the most important strategic questions every leadership team should be debating right now. Maybe AI shortens the half-life of every competitive advantage. The companies that win won’t have the strongest moat. They’ll have the highest rate of moat creation.
Aaron Levie@levie

Great post on some of the dynamics to think through for the future competitive advantage in world when AI models are shared amongst firms and packing so much for the intelligence of that industry. This is going to become a core question for companies and the economy broadly over the next decade and beyond. If AI is trained on the best datasets in every single industry - like law, finance, healthcare, or life sciences - then how do you compete and differentiate in the future? This is a great open question that I don’t think is perfectly knowable right now because of how fast AI progress is happening. But ultimately it stands to reason that if intelligence is abundant and broadly available to anyone in a field, then the companies that effectively use it the best and against a set of data and knowledge that grows in value over time, will be in a strong position. There’s a huge reinforcing loop between the intelligence from models, a company’s own data, the connection of that data and AI in their workflows, and how employees ultimately interact with that system to create value. There’s no obvious point where this will become uniform across all companies in a vertical because each company will approach this in a different way, just as they already do with their talent and workflows. If anything, there will be compounding returns to those that do this best that accelerate their advantage over time. Overall, super interesting question to see how this plays out over time.

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Jeetu Patel
Jeetu Patel@jpatel41·
@MatthewBerman I think evals will get increasingly more private and specific to what a company wants to carry out for a use case over time. Public evals will still exist but the ultimate indicator of how a model performs for your specific tasks and use cases will need private evals for those specific tasks and use cases. So building robust private evals is a competency every company will need to develop. Over time, these evals will also need to determine the best combination of models that provide the most token efficient outcomes for your specific use cases. We will need to increasingly measure multi-model outcomes for use cases that are measured against multiple metrics like efficacy, cost, performance, etc.
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Nikesh Arora
Nikesh Arora@nikesharora·
Have gotten a few inquiring minds - what happened to Nikesh? Why is he suddenly active on Twitter? I have to credit my friend @HarryStebbings for it. When we did the podcast he said podcasts allow you to take control of your own narrative, and so does X. "Nikesh - you should try and tweet once a day" if you have something interesting to say, your business will be perceived differently. I "rebuffed him", but then I reflected upon it and promised to give it a try, here's me trying in the last week. Should I continue?
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Jeetu Patel
Jeetu Patel@jpatel41·
This is a good way to think about frontier class models. Replace names of the particular models in the diagram, but this general construct is a very logical way to think about containing token costs and aligning outcomes to be in equilibrium with the costs incurred. The largest models will become the advisors to the more efficient executor models. This approach as well as intelligent model routing are just a couple examples of why spiked token costs will have a very short half-life. Cost of tokens will go down to become affordable sooner than we think. The thing that is fantastic about AI is the bottlenecks get addressed in very compressed time-cycles as they emerge. It’s a cool thing to witness. Lately, one of the biggest things I have learned to appreciate is human creativity of the [find obstacle —> tackle obstacle] continuous loop that keeps accelerating in speed and complexity simultaneously. New more complex hurdles are emerging every day and the new solutions being created to overcome or bypass those hurtles in very rapid clock-speeds. It is what is truly fascinating about this time. Big alpha from the past few days is that the cost of tokens will fall to a sufficient enough degree to make it digestible for most faster than we think, frankly at a shockingly fast pace, but yet it will feel much longer than we want it to take.
ClaudeDevs@ClaudeDevs

A few patterns we frequently use with Fable 5: Use Fable 5 as an "advisor." An executor (Sonnet 5) calls Fable 5 for guidance. Most tokens are billed at the lower executor rate.

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Jeetu Patel
Jeetu Patel@jpatel41·
@dee_bosa, great point. This has actually been a thing for the past several months. First, public benchmarks and evals tend to be things that don’t guarantee great results. But more importantly, creating the right evals and driving the model to operate well under those conditions seems like only the normal thing to do. For example, the EZDubs team was given a very hard time pre-acquisition by @Cisco for not being too tied to public benchmarks because they wanted to make the right tradeoffs in the model, because the wrong tradeoffs could get you a rank on the benchmark but not allow you to accomplish the results. Specifically, they didn’t want to trade off latency for accuracy. But to score well on the public evals at the time, that would be a necessary trade-off. So they created their own private evals because it was available in the market was for two generic for them. This will become a more common phenomenon.
Deirdre Bosa@dee_bosa

The customers are now benchmarking the labs A real reversal: official benchmarks don’t mean much anymore. Every lab trains for them & every launch tops them. So DoorDash built its own test and ran the models through it No model won on everything. The best setup was a mix… and it changed when a new model shipped A lot of companies are going to copy this. If you can test the models yourself, you don’t have to take the labs word for anything

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Grace Gong
Grace Gong@gracegongGG·
Excited to be speaking at @Ai4Conferences in Las Vegas this year, hard to top this lineup. @drfeifei @AndrewYNg @agermanidis, Co-Founder of @runwayml @dmitri_dolgov Co-CEO of @waymo my favorite tech legend @jpatel41, President of @Cisco, who spoke at our Smart AI Summit last year! :) and many more iconic AI founders and tech leaders I'll be on stage with @michaelmontano, Partner at @trueventures, and @rshanreddy, founder of @hey_aristotle. I'm also streaming live from the podcast studio with a few standout tech leaders, including Tim Martin, CTO of @audible_com. If you're at Ai4 - Artificial Intelligence Conferences, come find me. #ai4 #ai #vc #startup #sf
Grace Gong tweet mediaGrace Gong tweet media
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Jeetu Patel
Jeetu Patel@jpatel41·
The one fascinating thing about the market we are in is that there is always another scarce bottleneck that becomes suddenly scarce in the infinitely deep supply chain. This is just yet one other example. First people thought it was the chip that was scarce. Then it was the power. Then the supply in the fab. Then Wafer availability. Then ASML. Then raw materials. And it keeps going. We live in such a massively interconnected ecosystem. And constraints in one small part create such disequilibrium in all aspects of life. It’s exciting to understand the interdependence of this highly connected world we live in but also so humbling!
SemiAnalysis@SemiAnalysis_

One of the most underappreciated ways to play the AI semiconductor buildout may be through materials rather than chips themselves. As the industry races to produce more advanced semiconductors, demand isn’t just rising for GPUs and wafer fab equipment, it’s rising for the critical materials that make modern chips possible. (1/6)🧵

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