This started as an analysis of Andrej Karpathy's excellent overview of AI's capabilities:\nhttps://x.com/karpathy/status/1979644538185752935
"Ghosts" is a brilliant metaphor for what we've created. They're not animals, and they're definitely not human. They are imperfect replicas of us. Karpathy describes them as a "statistical distillation of humanity's documents".
AI channels that distribution more effectively than any human and can beat us at Go, schoolwork, analyzing medical images, and many well defined tasks. At the same time, it lacks the reward systems that humans use to improve including curiosity, empowerment, play, intrinsic motivation, and culture.
As a result, AI's capabilities are limited. If you watch how the best work gets done with AI, it happens in chunks where a human supervises the output and gives iterative feedback to the AI. Large scale autonomous agents are brittle and fail quickly. Watch anyone vibe code an application with any level of novel complexity.
AI also faces integration barriers into existing organizations. AI is not capable of pulling a lot of the levers you need to be effective like coordinating with multiple stakeholders, building trust, authenticity, and interacting with different modalities across time and space. You could argue that the average human doesn't either, but people know when they're interacting with an AI and don't allow it the same agency as they do to people. The most successful AI B2B companies actually need more humans to integrate what they've built (forward deployed engineers) than traditional B2B SaaS.
Now, that all said, I think AI will completely reshape SaaS. Incumbents will be killed by those who know how to leverage AI. I've seen countless homepages talk about being the "AI platform for AI agents" but can't even string a demo together. Meanwhile they're trying to pitch a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. They will be the first to be replaced when they get surpassed by AI native companies.
The investors blindly throwing money into companies at 100x multiples are going to lose their money. I spoke with one of the most disciplined investors I know last week. They said they felt they had to play the game on the field even though they knew it didn't make sense. And this was from someone who is closer to the technology than 95% of investors.
On the other hand, companies who deeply understand AI will win everything. Cursor, Glean, Decagon, Sierra, Linear, Lovable, Replit, Bolt, Granola, and many AI natives are off to a great start.
While 90% of incumbents haven't adapted, it is possible. Figma, Notion, Vercel, Box, and Intercom have done a great job of tearing down what they have and rebuilding AI native products. They have teams who are close to the current capabilities of the models. They also understand their problem domain and as new capabilities come out know what capabilities will map well to what problems in what way. They are able to deliver on AI's promise to their customers. Whereas the majority of existing companies will die.
Within Amplitude's space (analytics), the door is wide open. In spite of many of our competitors filling up their homepages with the text "AI", I haven't seen a single compelling demo. We're still working like it's 2015.
We have spent the last year at Amplitude rebuilding our team to be AI native. We've learned about what models are capable of, how to write prompts, and how to leverage evals for building great products. We've worked with our customers to see what gets used in practice and what doesn't.
I know HN isn't one for hype, but we're going to be coming out with a lot over the next 6 months and I genuinely think it will change analytics."},"title":{"matchLevel":"none","matchedWords":[],"value":"The State of AI in SaaS"}},"_tags":["story","author_sskates","story_45634211","ask_hn"],"author":"sskates","created_at":"2025-10-19T14:03:46Z","created_at_i":1760882626,"num_comments":0,"objectID":"45634211","points":2,"story_id":45634211,"story_text":"SaaS is Dead, Long Live SaaS
This started as an analysis of Andrej Karpathy's excellent overview of AI's capabilities:\nhttps://x.com/karpathy/status/1979644538185752935
"Ghosts" is a brilliant metaphor for what we've created. They're not animals, and they're definitely not human. They are imperfect replicas of us. Karpathy describes them as a "statistical distillation of humanity's documents".
AI channels that distribution more effectively than any human and can beat us at Go, schoolwork, analyzing medical images, and many well defined tasks. At the same time, it lacks the reward systems that humans use to improve including curiosity, empowerment, play, intrinsic motivation, and culture.
As a result, AI's capabilities are limited. If you watch how the best work gets done with AI, it happens in chunks where a human supervises the output and gives iterative feedback to the AI. Large scale autonomous agents are brittle and fail quickly. Watch anyone vibe code an application with any level of novel complexity.
AI also faces integration barriers into existing organizations. AI is not capable of pulling a lot of the levers you need to be effective like coordinating with multiple stakeholders, building trust, authenticity, and interacting with different modalities across time and space. You could argue that the average human doesn't either, but people know when they're interacting with an AI and don't allow it the same agency as they do to people. The most successful AI B2B companies actually need more humans to integrate what they've built (forward deployed engineers) than traditional B2B SaaS.
Now, that all said, I think AI will completely reshape SaaS. Incumbents will be killed by those who know how to leverage AI. I've seen countless homepages talk about being the "AI platform for AI agents" but can't even string a demo together. Meanwhile they're trying to pitch a future where fully autonomous entities collaborate in parallel to write all the code and humans are useless. They will be the first to be replaced when they get surpassed by AI native companies.
The investors blindly throwing money into companies at 100x multiples are going to lose their money. I spoke with one of the most disciplined investors I know last week. They said they felt they had to play the game on the field even though they knew it didn't make sense. And this was from someone who is closer to the technology than 95% of investors.
On the other hand, companies who deeply understand AI will win everything. Cursor, Glean, Decagon, Sierra, Linear, Lovable, Replit, Bolt, Granola, and many AI natives are off to a great start.
While 90% of incumbents haven't adapted, it is possible. Figma, Notion, Vercel, Box, and Intercom have done a great job of tearing down what they have and rebuilding AI native products. They have teams who are close to the current capabilities of the models. They also understand their problem domain and as new capabilities come out know what capabilities will map well to what problems in what way. They are able to deliver on AI's promise to their customers. Whereas the majority of existing companies will die.
Within Amplitude's space (analytics), the door is wide open. In spite of many of our competitors filling up their homepages with the text "AI", I haven't seen a single compelling demo. We're still working like it's 2015.
We have spent the last year at Amplitude rebuilding our team to be AI native. We've learned about what models are capable of, how to write prompts, and how to leverage evals for building great products. We've worked with our customers to see what gets used in practice and what doesn't.
I know HN isn't one for hype, but we're going to be coming out with a lot over the next 6 months and I genuinely think it will change analytics.","title":"The State of AI in SaaS","updated_at":"2026-03-05T22:50:27Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"zebirdman"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["decagon"],"value":"Say a company (eg Delta Air Lines) wants to use genAI for their customer service. OpenAI/Anthropic (and maybe Gemini too) can help with this and partner directly with Delta and call them a customer. But there are also specific companies (i.e., application layer companies) that use OpenAI's and/or Anthropic's LLM to build a product that uses genAI for customer service. Examples are Decagon, Sierra, Cresta, MavenAGI, etc. \nThe same applies for other use cases beyond customer service too: legal tech, enterprise search, etc. \nWhy would Delta partner with an application layer company vs. the LLM themselves?"},"title":{"matchLevel":"none","matchedWords":[],"value":"Difference b/w partnering with OpenAI/Anthropic vs. an application layer company"}},"_tags":["story","author_zebirdman","story_41436688","ask_hn"],"author":"zebirdman","created_at":"2024-09-03T16:45:24Z","created_at_i":1725381924,"num_comments":0,"objectID":"41436688","points":1,"story_id":41436688,"story_text":"Say a company (eg Delta Air Lines) wants to use genAI for their customer service. OpenAI/Anthropic (and maybe Gemini too) can help with this and partner directly with Delta and call them a customer. But there are also specific companies (i.e., application layer companies) that use OpenAI's and/or Anthropic's LLM to build a product that uses genAI for customer service. Examples are Decagon, Sierra, Cresta, MavenAGI, etc. \nThe same applies for other use cases beyond customer service too: legal tech, enterprise search, etc. \nWhy would Delta partner with an application layer company vs. the LLM themselves?","title":"Difference b/w partnering with OpenAI/Anthropic vs. an application layer company","updated_at":"2024-09-20T17:44:19Z"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"polygot"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: Pedant: Linting for English texts"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["decagon"],"value":"https://github.com/decagon/pedant"}},"_tags":["story","author_polygot","story_12136165","show_hn"],"author":"polygot","children":[12137648,12152076,12152426,12161514],"created_at":"2016-07-21T11:41:14Z","created_at_i":1469101274,"num_comments":6,"objectID":"12136165","points":15,"story_id":12136165,"title":"Show HN: Pedant: Linting for English texts","updated_at":"2024-09-19T23:28:22Z","url":"https://github.com/decagon/pedant"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"polygot"},"title":{"matchLevel":"none","matchedWords":[],"value":"Hn-sonar: get notified for HN replies to your comments. No email address needed"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["decagon"],"value":"https://github.com/Decagon/hn-sonar/"}},"_tags":["story","author_polygot","story_15997605"],"author":"polygot","children":[15998969],"created_at":"2017-12-24T02:49:03Z","created_at_i":1514083743,"num_comments":1,"objectID":"15997605","points":3,"story_id":15997605,"title":"Hn-sonar: get notified for HN replies to your comments. No email address needed","updated_at":"2024-09-20T01:52:37Z","url":"https://github.com/Decagon/hn-sonar/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"polygot"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: An app to \u201cpredict\u201d hearing loss, records output volume everyday"},"url":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["decagon"],"value":"https://github.com/Decagon/can-you-hear-me"}},"_tags":["story","author_polygot","story_13286876","show_hn"],"author":"polygot","created_at":"2016-12-30T19:51:13Z","created_at_i":1483127473,"num_comments":0,"objectID":"13286876","points":3,"story_id":13286876,"title":"Show HN: An app to \u201cpredict\u201d hearing loss, records output volume 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Will likely be a continual WIP, but the goal is to make my entire music-making stack open source and easy to parse for people trying to deconstruct my compositions!"},"title":{"matchLevel":"none","matchedWords":[],"value":"Show HN: A web based 4-channel Music Tracker"},"url":{"matchLevel":"none","matchedWords":[],"value":"https://johnoestmannmusic.com/1000-SOS/0007/"}},"_tags":["story","author_soundworlds","story_49693539","show_hn"],"author":"soundworlds","created_at":"2026-09-14T08:13:49Z","created_at_i":1789373629,"num_comments":0,"objectID":"49693539","points":1,"story_id":49693539,"story_text":"I'm iterating and adding features as I use it to create my own tracks. Will likely be a continual WIP, but the goal is to make my entire music-making stack open source and easy to parse for people trying to deconstruct my compositions!","title":"Show HN: A web based 4-channel Music Tracker","updated_at":"2026-09-14T08:14:50Z","url":"https://johnoestmannmusic.com/1000-SOS/0007/"},{"_highlightResult":{"author":{"matchLevel":"none","matchedWords":[],"value":"beienchina"},"story_text":{"fullyHighlighted":false,"matchLevel":"full","matchedWords":["decagon"],"value":"According to statistics from the market research firm QYResearch, global sales revenue for call center headsets reached $1.546 billion (approximately 11.1 billion RMB) in 2024. This figure is projected to grow to $2.18 billion by 2031, representing a Compound Annual Growth Rate (CAGR) of 5.1%. If we expand the scope to the broader category of "Office + Call Center" headsets, the global market size reached $6.72 billion in 2024. It is expected to grow by nearly $2.7 billion by 2029, boasting a CAGR of 13.8%-a growth rate that highlights a clear trend: professional\u2011grade headsets are becoming standard equipment for an increasing number of enterprises.
Another statistic offers an even more intuitive perspective: in 2025, global sales volume for commercial headsets is projected to reach approximately 98 million units, with an average unit price of around $34. Every year, nearly 100 million headsets flow into the enterprise, call center, and remote work markets.
Now, let's turn to the Chinese market. As of 2024, the number of call center agent seats in China reached 4.4 million\u2011an 8.6% increase year\u2011on\u2011year. In that same year, the market size of China's call center industry reached 55.3 billion RMB (approximately $7.8 billion). As the world's second\u2011largest market, China accounts for roughly 13% of the global market share.
A simple calculation puts this into perspective: with 4.4 million agent seats and assuming an annual replacement rate of 50%, China alone generates a procurement demand for over 2 million headsets each year.
For procurement professionals facing a vast and highly fragmented supply chain, the choices you make directly impact the quality of service delivered by your agents\u2011as well as your own operational management costs.
While conducting a routine patrol in the call center, you encounter a recurring complaint: agents are repeatedly asked by customers, "Is someone typing over there?" You take a headset to test it and hear your own keystrokes coming through as clearly as your voice. You swap the headset for a different model, and the noise vanishes. Same keyboard, same desk\u2011what makes the difference? What exactly is a noise\u2011canceling headset doing?
To understand this, we must first deconstruct the term "noise cancellation." Many product managers are misled by marketing copy, assuming that noise cancellation is solely about making the environment quiet for the wearer. However, in a call center scenario, the true goal is exactly the opposite: ensuring that the person on the other end of the line cannot hear the background noise. These are two entirely different objectives, each requiring a completely distinct technical approach."},"title":{"matchLevel":"none","matchedWords":[],"value":"How does call center headset noise canceling filter out keyboard sounds?"}},"_tags":["story","author_beienchina","story_49693496","ask_hn"],"author":"beienchina","created_at":"2026-09-14T08:08:33Z","created_at_i":1789373313,"num_comments":0,"objectID":"49693496","points":1,"story_id":49693496,"story_text":"According to statistics from the market research firm QYResearch, global sales revenue for call center headsets reached $1.546 billion (approximately 11.1 billion RMB) in 2024. This figure is projected to grow to $2.18 billion by 2031, representing a Compound Annual Growth Rate (CAGR) of 5.1%. If we expand the scope to the broader category of "Office + Call Center" headsets, the global market size reached $6.72 billion in 2024. It is expected to grow by nearly $2.7 billion by 2029, boasting a CAGR of 13.8%-a growth rate that highlights a clear trend: professional\u2011grade headsets are becoming standard equipment for an increasing number of enterprises.
Another statistic offers an even more intuitive perspective: in 2025, global sales volume for commercial headsets is projected to reach approximately 98 million units, with an average unit price of around $34. Every year, nearly 100 million headsets flow into the enterprise, call center, and remote work markets.
Now, let's turn to the Chinese market. As of 2024, the number of call center agent seats in China reached 4.4 million\u2011an 8.6% increase year\u2011on\u2011year. In that same year, the market size of China's call center industry reached 55.3 billion RMB (approximately $7.8 billion). As the world's second\u2011largest market, China accounts for roughly 13% of the global market share.
A simple calculation puts this into perspective: with 4.4 million agent seats and assuming an annual replacement rate of 50%, China alone generates a procurement demand for over 2 million headsets each year.
For procurement professionals facing a vast and highly fragmented supply chain, the choices you make directly impact the quality of service delivered by your agents\u2011as well as your own operational management costs.
While conducting a routine patrol in the call center, you encounter a recurring complaint: agents are repeatedly asked by customers, "Is someone typing over there?" You take a headset to test it and hear your own keystrokes coming through as clearly as your voice. You swap the headset for a different model, and the noise vanishes. Same keyboard, same desk\u2011what makes the difference? What exactly is a noise\u2011canceling headset doing?
To understand this, we must first deconstruct the term "noise cancellation." Many product managers are misled by marketing copy, assuming that noise cancellation is solely about making the environment quiet for the wearer. However, in a call center scenario, the true goal is exactly the opposite: ensuring that the person on the other end of the line cannot hear the background noise. These are two entirely different objectives, each requiring a completely distinct technical approach.","title":"How does call center headset noise canceling filter out keyboard sounds?","updated_at":"2026-09-14T08:13:50Z"}],"hitsPerPage":50,"nbHits":19,"nbPages":1,"page":0,"params":"query=Decagon&tags=story&hitsPerPage=50&advancedSyntax=true&analyticsTags=backend","processingTimeMS":5,"processingTimingsMS":{"_request":{"roundTrip":21},"afterFetch":{"merge":{"total":1},"total":1},"fetch":{"query":2,"total":3},"total":5},"query":"Decagon","serverTimeMS":6}