Startup Investor Red Flags

Many startup founders dream of being funded at an early-stage. But, it might not all be sunshine and rainbows..

You need to consider factors like dilution, not over-bloating your startup, and also a trust factor of the given investor.

There’s a slight stigma when talking to investors. Most people think the fear of failure comes only one way. Startups failing investors. If only this was true..

Just try googling “red flags in startup investors” — all you find is information on what investors should avoid when investing in sketchy startups. Who is looking out for us, founders?

Many investors fail startup founders on a daily basis. To avoid this, here are some red flags you should look for when accepting offer from an investor.

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Red Flags in Investors

Pressures you to accept more money than you needed

When an investors sees a great potential in you and your startup, they try to increase their potential outcome. Sometimes, the number might be so crazy you feel like to decline or re-negotiate might be the biggest mistake you ever make.

Approach carefully. Balancing the greed for money, and keeping the majority in the company is not an easy task. General rule of thumb is only take as much as you think you need. In this scenario — less is more. Worst case scenario, you run out of money and raise more later. If there are lines of investors throwing money at your startup, chances are they will still be there in half a year.

Unrealistic expectations

You showed the investor a TAM for your startup. He wants to see you capture the first 10% of that market in the first 6 months. Huh?

Red flag — either talk him out of it explaining how unrealistic he sounds, or run away. Investors looking for the instant win rather than a sustainable growth will be more harmful than helpful to your company.

Remember, hockey stick projections look great on paper, but they rarely reflect reality. A good investor understands that building a solid foundation takes time. They should be pushing you to set ambitious yet achievable goals, not forcing you into a growth trajectory that could burn out your team and resources.

Micromanagement tendencies

With the rise of founder mode, micromanagement seems to be the cool thing. Brian Chesky and Elon Musk both do it — and they are both famous and well off. Surely, it must work.

There are not many things worse than being micromanaged how to scale a company, by the person you technically owe the money too. Unless he’s also clueless and with 0 experience in your niche. That’s the only worse thing.

You always want partners who trust your expertise and vision, not those who try to run your company from the sidelines.

Zero flexibility in negotiations

You entered a civil marriage with your investor. A marriage, without an option to divorce each other. When a problem arises (and it will), you want to make sure you both play for the same team.

It should be you and him — versus the problem. Not you — versus him. Therefore, before getting in bed with that man (or a woman of course), make sure you both can communicate well.

You can test this as soon as in the first agreement negotiations. If the investor is unwilling to flinch in the first negotiation, what do you reckon are the chances he will flinch 5, 10, 15 years down the line?

Poor track record

Do your due diligence on potential investors. Ask around your network. Did a person you know have a problem with the given investor?

If your investor has a history of conflicts with other startups or a reputation for not following through on commitments, that's a major red flag.

Look for investors with a positive track record of supporting and growing companies.

How NVIDIA accidentally became the backbone of the AI revolution

Today, NVIDIA is a $2.8T company and powers the world’s most advanced AI systems.

But in the early days, they had a big problem:

Most computers didn’t have the horsepower to handle intensive graphics or AI workloads.

Here’s the inside story of how NVIDIA accidentally became the backbone of the AI revolution...

1/ In the 90s, their focus was gaming.

To make games look real, Nvidia needed to push the boundaries of computer graphics.

So, they built powerful GPUs that could process complex visual data faster than any CPU.

It was manual, grueling, and required constant innovation—but it worked.

2/ Nvidia launched the GeForce series in 1999.

It was a hit with gamers. Suddenly, everyone wanted NVIDIA GPUs to play the latest games in high-definition.

But they were about to stumble onto something much bigger than gaming...

3/ By the mid-2000s, researchers started using GPUs for more than games.

They realised that GPUs could crunch huge amounts of data in parallel, making them perfect for scientific computing.

AI researchers saw this and had a big idea...

4/ When the deep learning revolution took off around 2012, GPUs became critical.

AI needed immense computing power to train models, and NVIDIA’s hardware was ready for the challenge.

It wasn’t their original focus, but they were positioned perfectly.

5/ But Nvidia hit a roadblock...

The software wasn’t optimized for AI workloads.

So, they started working closely with AI researchers, tweaking our architecture, and creating tools like CUDA to make our GPUs even more AI-friendly.

It wasn’t easy, but the results were worth it.

6/ By 2015, we were spending millions on R&D for AI.

Their GPUs powered breakthroughs in image recognition, natural language processing, and self-driving cars.

Nvidia wasn't built for gamers anymore — they were building for the future of AI.

7/ Over the next few years, 3 things happened to Nvidia:

↪ they doubled down on AI research partnerships.
↪ built data center-grade GPUs like the Tesla series.
↪ scaled production to meet exploding demand from AI startups and tech giants.

And the results were game-changing.

8/ Today, NVIDIA GPUs are the standard for AI workloads.

From autonomous vehicles to generative AI models like ChatGPT, its technology powers the most critical systems of the future.

Without those early innovations, NVIDIA wouldn’t be the 3-trillion-dollar company it is today.

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