The Peel with Turner Novak

Benchmark’s Chetan Puttagunta on the Past, Present, & Future of Software

Date: March 4, 2026
Guest: Chetan Puttagunta (Partner, Benchmark)
Host: Turner Novak

Overview

In this engaging episode, Turner Novak sits down with Benchmark partner Chetan Puttagunta for an in-depth conversation about the evolution of software—from mainframes to SaaS to the current explosion of AI-native applications. Chetan shares granular insights into Benchmark’s investment approach, tells the inside story of runaway success Manus, and explains why the next phase of software will disrupt both startups and established giants like Salesforce. Key themes include the new economics of AI apps, venture dynamics, and how infrastructure and application layers are rapidly evolving.

Key Discussion Points and Insights

The Manus Case Study: From Beta to $100M ARR in 8 Months

• Discovery & Diligence

  • Chetan recaps how he discovered Manus’ demo video within hours of its March 2025 YouTube launch ([01:00]):

    “I saw it in the first couple of hours...and I was thoroughly wowed by the experience. Manus was presenting an agent product that could actually get further on tasks than any other AI product had at that point. It really felt magical when I first tried it.”

  • Unique to Manus: Not just multi-model use, but decomposing tasks into thousands of subtasks and solving each in parallel ([02:59]):

    “They’d taken the idea of breaking a task into subcomponents and using lots of models to solve them to such an extreme degree, I don’t think anybody had tried that yet.”

• Growth and Acquisition

  • Product’s closed beta went viral in Japan, leading founders to run user meetups in Tokyo ([04:10]).
  • Manus launched public GA April 2025.
  • Achieved $100M ARR in just eight months; with consumption revenue, $125M run rate—“the fastest company to have ever gone 0 to 100 million” ([06:13]).
  • Meta acquired Manus, “acquiring a team deeply knowledgeable about how these APIs work and how to get further on a task” ([08:44]).

• Core Use Cases

  • Identified three primary consumer demand areas:
    1. Deep research (longform writing)
    2. Coding assistance (especially for nontechnical users)
    3. Slides/presentation creation
  • Manus relied exclusively on Anthropic, OpenAI, and Gemini APIs, blending them for depth and performance ([08:10]).

Investing Amid Twitter Heat and Geopolitical Scrutiny

• Pushback on Social Media
  • Chetan explains that despite public criticisms (including over Manus’ founders’ Chinese heritage and Singapore HQ), Benchmark focused on fundamentals—team quality, technology, and global product fit:

    “For us, we want to invest in great people. The product wasn’t available in China. It was a worldwide business. Those are the kind of businesses you have to back as a generalist investor, especially as somebody looking for consumer AI.” ([11:13])

  • Discusses the global distribution of AI talent:

    “Jensen Huang of Nvidia recently said something, half of the world’s AI scientists are Chinese and half are American.” ([12:49])

Social Media Strategy: Peaks, Pauses, and Community Value

• Chetan recounts how summarizing earnings calls on Twitter unexpectedly built a large audience of software enthusiasts during 2018–2020 ([17:52]).
• After returning to in-person meetings post-pandemic, tweeting declined; he notes real-time commentary creates more value than delayed posts ([21:47]).
• Recent engagement uptick driven by “the fundamental shift at the application layer with AI” and high-impact launch videos ([22:00]).

Software History: From Mainframes to AI-Native Applications

• Era Progression ([23:09]–[31:04]):

  1. Mainframes: First application automation
  2. Client-server: More custom apps
  3. Internet: Huge explosion in app volume (two orders of magnitude, especially for consumer apps)
  4. Cloud: Centralized hosting (EC2/S3, ~2009-2011), removes capital barriers for developers; coincided with mobile App Store (“a capital-intensive process replaced by cloud and credit card”)
  5. SaaS: Incumbents (Salesforce, Workday, ServiceNow) expand into adjacent categories and make distribution difficult for new entrants ([31:24–33:04])
  6. AI layer: New Cambrian explosion—every horizontal category “up for grabs” again
     > “The SaaS comparable looks really bad. When you open an [AI app], the experience is that stark.” ([41:46])
     > “Do not complain about negative gross margin software company...if you’re seeing the patterns you saw in the last phase.” ([55:26])

• Key Insight:

  • Each technological wave slashes development cost and increases distribution reach. AI is seen as the next such tectonic moment ([34:30], [36:43], [47:18]).

AI Applications vs. SaaS: The Next Competitive Frontier

• Why Incumbents Will Struggle

  • On Innovator’s Dilemma ([43:18]):

    “For a SaaS company to beat a native AI application...they would have to break their fundamental architecture, rebuild, redo the business model, and retrain distribution.”

  • Example: Siebel “on demand” vs. Salesforce—a detailed lesson in why incumbents can’t move fast enough ([44:29]).

• Advice for SaaS Giants

  • Acquisitions as defense:

    “If you’re a SaaS company right now, you should really think about spending 10–25% of your market cap to buy AI applications.” ([51:29])

  • History shows incumbents always see deals as “too expensive”—until it’s too late ([57:55]):

    “These AI application companies were much cheaper to acquire in 2023...it’s happening right in front of us.”

• Do founders even need to sell?

  • The late-stage capital environment makes it easier for AI startups to raise and go for long-term independence ([64:28]).

Public Market Dynamics: The Coming AI IPO Wave

• Shifts in investor demand:

  “For the first time...I’m hearing bankers say things like, ‘Yeah, $100 million ARR, we could probably take that public.’ Haven’t heard that in a while.” ([66:48])

• Capital Flows:

  • "If you look at net new ARR, where is it going? It's really just going to all these AI companies." ([68:32])
  • “OpenAI and Anthropic added as much revenue as every single publicly traded software company [since ChatGPT’s launch].” ([69:21])

Benchmark’s Investment Playbook

• Approach to Valuation and Structure

  • Board-centric; ownership is flexible as long as partnership with founders is strong ([70:17], [74:14]).
  • "The valuation, frankly, is not the governor in our investment decisions. The discussion really isn't about the valuation, or the deal structure." ([71:13])
  • Partnership model: Four equal GPs, eight to ten investments a year, highly collaborative ([70:35], [72:39]).

• Historical “Nontraditional” Rounds

  • Benchmark’s flexibility: From Nordstrom.com spinout to a “growth” round at Twitter, and continuous willingness to do “atypical” deals if the founder is compelling ([74:29]–[76:54]).

• What They Look for in Founders

  • Deep domain or technical insight, often at the pre-product stage:

    “Typically investing seed Series A—no product, no revenue, no metrics. But some deep insight that the founder has.” ([82:49])

AI Code Generation and Infrastructure

• “There’s massive demand for codegen products—across consumer, B2B, and prosumer.” ([85:25])
• Margin debates are premature; chip and cloud innovation (e.g., Cerebras) will significantly reduce inference costs, changing future P&Ls ([86:23]).

Notable Quotes & Memorable Moments

• On AI App Unlock:

  "Meta is acquiring a team that's very deeply knowledgeable about how these APIs work and how to get further on a task, depending on the kind of task, with a certain set of APIs." – Chetan ([08:44])

• On Acquisition Opportunity:

  “Companies are about to get gigantic. Manus went 0 to 100 in 8 months. Once they're at $100 million, they continue to scale. These companies are really big, really fast.” – Chetan ([52:33])

• On the Investor’s Mindset:

  “If a company doesn’t work, it’s a 1x error. If a company works, it can generate a lot of returns. So you just want to be in companies that work, and so you don’t worry about the downside.” – Chetan ([84:57])

• History Repeats:

  “It’s really interesting that the SaaS companies have forgotten their own state...They are now the incumbent and not embracing what it takes to buy the upstart.” ([61:00])

• Foundational Philosophy:

  "We want to be a part of an organization where you could do deals like Manus, Sierra, Lagora, Fireworks, and LangChain—all in a span of 12 months..." ([77:06])

Timestamps for Key Segments

• Manus origin and hype — [00:26]–[06:23]
• Manus' use cases & technical edge — [06:23]–[08:44]
• Twitter controversy & global AI talent — [11:08]–[14:23]
• Social media engagement, public market pulse — [15:39]–[22:00]; [66:48]
• Era-by-era evolution of software — [23:03]–[33:04]
• AI vs. SaaS, incumbent challenges — [41:46]–[52:33]
• Acquisition logic, public vs. private market multiples — [57:55]–[62:18]
• Benchmark's deal approach/strategy — [70:17]–[77:06]
• What Benchmark looks for in founders — [82:49]–[84:57]
• AI codegen, margin, infrastructure outlook — [85:25]–[88:03]

Tone and Style

The conversation is candid, technical, and occasionally irreverent—with subtle humor in the banter, e.g. comments on Twitter activity, “directionally correct” IRR stats, and banana-breaking demonstrations at the end. Both Turner and Chetan keep industry lessons accessible by weaving in metaphors, founder-centric language, and concrete case studies.