To replace the mangers, the AI needs to take care of the following pain points (got some help from chatgpt):
Hiring and Talent Acquisition:
- Candidate pipeline quality is inconsistent.
- Time-consuming resume screening and interview scheduling.
- Lack of diverse candidate pools.
Performance Management
- Biased or inconsistent performance reviews.
- Goal-setting lacks clarity or alignment with org OKRs.
- Lack of real-time performance insights.
Project Planning and Execution
- Estimations are often inaccurate.
- Project scope creep due to unclear requirements.
- Dependencies across teams delay execution.
Technical Debt and Code Quality
- Mounting technical debt slows velocity.
- Inconsistent coding standards.
- Hard to trace ownership for legacy code.
Team Collaboration and Communication
- Cross-team communication breakdowns.
- Time zones complicate decision-making.
- Meeting overload or lack of clarity post-meeting.
Onboarding and Knowledge Transfer
- New hires take too long to ramp up.
- Tribal knowledge isn't well documented.
- Onboarding processes are inconsistent.
Incident Management and Reliability
- Blameless postmortems are rarely actionable.
- Alert fatigue from noisy signals.
- Root cause analysis (RCA) is time-consuming.
Career Growth and Mentorship
- Lack of clarity in career ladders.
- Mentorship is ad-hoc and inconsistent.
- Managers don’t have enough time for coaching.
Engineering Productivity Metrics
- Metrics often feel punitive or misused.
- Hard to attribute impact to engineers’ work.
- Lack of actionable insights from engineering data.
Cross-Functional Alignment
- Product and engineering priorities are misaligned.
- Specs often change mid-cycle.
- Lack of visibility into roadmap tradeoffs.
Each of these categories will probably need an AI agent in itself and probably an AI agent to control all the other AI agents. It would be a complex system, but will still need some monitoring until it is self-sustaining.
Virtually all of these are things that humans are bad at, so to me it seems AI should easily replace management. After all the list itself was generated by AI and not even a human :)
Hi Guys, Flutterbud is my experiment while I was learning swift. The app lets you record videos and share with your friends at a scheduled showtime. The recipients cannot view the video before the showtime. I created the app using parse-server, S3, Heroku. Let me know if you have any questions.
Yunha, Great App! I have been using it every morning for more than a month now. I have explored Calm, Headspace, etc but I have finally settled upon Simple Habit. Easy to understand UX and great gamification.
I launched 2 websites before my current startup.
1. Drawmics.com. It was about social comic creation. It failed because I couldn't find enough creative people to post comic on the website nor could I get them to create comic on it. There was no organic traffic and people wouldn't share the comic. Didn't know how to sell.
2. Findero.us. It was location based QnA iPhone app. People could post questions defined by radius of city/state/country. Failed because I sucked at user-acquisition strategies.
Now that I am on my third start-up I could see what different things I could have done with my previous 2.
1. Build a product that solves a problem, however small, for a big audience. This means that you have to work on your product until you find that problem. If there is none, it will not work. I remember I read something about finding questions before finding answers. Questions are more important than finding answers.
2. Once you know you have the solution, go aggressive with user-acquisition and then fine-tune the product. People will complain about missing features, but you want scale before you go for product enhancements if you are solving a real problem.
I would be interested in knowing about what it includes and does not include. If it seems interesting, we may be interested in it. We are venture funded start-up.
I usually open up my Audible app and listen to books that are totally outside my field of work. Great way to make your brain think outside your domain.
For me, the acquisition is a failure on all levels. Failure to execute, failure in reaching beyond one's capacity to take the startup higher, failure in self-belief. Even though one may have earned money after an acquisition, the real aim of a startup is to change the world and not to get acquired and die.
But failure doesn't come cheap. For you to fail, you will need to put your heart and soul into the startup to succeed. And as spotman says "build something you believe in, that solves a real problem, and gain real traction", he didn't add that once you start getting offers for acquisition, stop believing in your idea and get lured by the offer and the riches.
I personally believe in one learning from "The hard thing about hard things" book by Ben Horowitz, is that you should sell your startup when you have reached your max capability to take the startup to the next level.
@xackpot I totally agree to what you are saying, but what I'm trying to figure out how to build startup/products that could be interesting for some big companies in order to get cash fast and build more bigger and more important startup / products. I'm a cofounder of software service company that I has been running for about 3 years now successfully, but the point of stable/ enough cash flow still a problem
> the real aim of a startup is to change the world
Honestly, not everyone is trying to change the world. The real world is full of businesses that provide a good living for their owners and their employees.
You can read books and articles about startups all you want, but those books aren't going to make choices for you. You have decide what is right for you and your business.
The real aim of a startup is to change the world either in the smallest, unnoticeable way or at perceptibly larger way. When you do a startup, you want to address a problem and change the world by solving that problem. If you don't want to change the world, what is your startup trying to achieve?
Btw, nobody reads books because they make choices for you, or teach you how to make choices. In fact, books don't teach anything if you are not ready to learn.
I don't care about 2014. My long term plan is to continue caring and loving my family and go IPO with my startup. One of the things gonna be tough but who wants an easy life?
Performance Management - Biased or inconsistent performance reviews. - Goal-setting lacks clarity or alignment with org OKRs. - Lack of real-time performance insights.
Project Planning and Execution - Estimations are often inaccurate. - Project scope creep due to unclear requirements. - Dependencies across teams delay execution.
Technical Debt and Code Quality - Mounting technical debt slows velocity. - Inconsistent coding standards. - Hard to trace ownership for legacy code.
Team Collaboration and Communication - Cross-team communication breakdowns. - Time zones complicate decision-making. - Meeting overload or lack of clarity post-meeting.
Onboarding and Knowledge Transfer - New hires take too long to ramp up. - Tribal knowledge isn't well documented. - Onboarding processes are inconsistent.
Incident Management and Reliability - Blameless postmortems are rarely actionable. - Alert fatigue from noisy signals. - Root cause analysis (RCA) is time-consuming.
Career Growth and Mentorship - Lack of clarity in career ladders. - Mentorship is ad-hoc and inconsistent. - Managers don’t have enough time for coaching.
Engineering Productivity Metrics - Metrics often feel punitive or misused. - Hard to attribute impact to engineers’ work. - Lack of actionable insights from engineering data.
Cross-Functional Alignment - Product and engineering priorities are misaligned. - Specs often change mid-cycle. - Lack of visibility into roadmap tradeoffs.
Each of these categories will probably need an AI agent in itself and probably an AI agent to control all the other AI agents. It would be a complex system, but will still need some monitoring until it is self-sustaining.