“We are facing an unprecedented revolution. AI will be everywhere”

“I share the view that we are facing an unprecedented revolution. AI will be everywhere. Can we imagine our business world without constant internet access? The same thing will be with AI, and even more,” said Noam Wolf, Partner at Flashpoint Venture Growth. “This isn't limited to 'what you can do over Zoom’, as some investors say. Amazon already employs one million robots. They might not be utilizing AI, but it shows you the scale that an advanced company is willing to grow into.”

Wolf joined CTech to explore how AI will transform industries and change the way investment is done to fully lean into the revolution.

Noam Wolf.

“There are two means of transformation: where humans can be replaced to save costs, and where a new market can be created,” he added. “Companies becoming more efficient will result in more profits in some cases, and cheaper consumer prices in others. But true transformation is where new products and markets are being created.”

You can read more below.

Fund ID
Name and Title: Noam Wolf, Partner
Fund Name: Flashpoint Venture Growth Fund IV
Founding Team: Alex Konoplyasty – Managing Partner, Noam Wolf, Anton Fedorov
Founding Year: Fund I was established 2013; Fund IV launched Q4-2024
Investment Stage: Series A
Investment Sectors: AI, Fintech, Cyber, Enterprise Software, Education Tech, Insurtech, eCommerce. Investments are in US / global high-growth, B2B software companies, founded by Israelis and Eastern Europeans

On a scale of 1 to 10, how has AI impacted your fund’s operations over the past year - specifically in terms of the day-to-day work of the fund's partners and team members?

9 - It feels like AI is everywhere, and we should be the first to adopt it. In fact, at Flashpoint, we have been using AI in the past couple of years extensively for our deal flow. We developed an automated sourcing machine to track and qualify startups that meet our investment criteria. Our AI processes analyze multiple data sources and data points to determine startups’ stage and progress. For example, we look at number of employees, the percentage change in the number of employees, sectoral information, founders location and many other parameters to prioritize a conversation with that team.

Then, when analyzing a startup, we would continue using AI tools in our research to gather information about the market, the company and the product. It's a game changer for us, mostly allowing us to focus on the best opportunities that meet our strategy.

Have you already had any significant exits from AI companies? If so, what were the key characteristics of those companies?

The term "AI company" is a bit tricky. We barely invest in companies that do not demonstrate that AI is a major part of their product. Does that make them an AI company? If their product wasn't made possible because of the introduction of AI then the answer is that, probably, they may be a great business and may be benefiting a lot from AI, but they are not an AI company.

In our portfolio, there are quite a few companies that were made possible because of AI: Deep Keep, providing AI-Native Security for AI Applications; Rep AI, creating an AI-Agent for e-Commerce; Qase - AI-powered software testing platform, and several others. While they are doing really well and are growing at a triple-digit percent pace, they are all relatively young companies that operate in very large markets. Could there be quick exits there? Of course... but this isn't our goal nor our intention. There's a lot more value to create.

Is identifying promising AI startups different from evaluating companies in your more traditional investment domains? If so, how does that difference manifest?

The quick answer is no. We still look for the same fundamentals of team, product thesis, TAM (Total Addressable Market), traction and unit economics.

However, there are some differences that we do take into account. The first issue is separating hype from what's real. Just like your question regarding AI startups, we need to decide how important AI is in the startup's value proposition, how difficult it is to replicate what they are doing, and how much they are relying on other models and infrastructure in their product. Founders naturally tend to emphasize the trendy parts like AI, and we need to be knowledgeable enough to look at the essence of the business and the product and determine if the story is real or not.

The second issue is pace and efficiency. You see startups that develop AI apps and move quickly with a small team. Naturally, as investors, we like the fact that a startup like Tactiq (in-meeting transcriptions and insights) was able to triple every year with a small team of and we look for that kind of efficiency in other startups. Since it might contradict the answer to "how difficult it is to replicate such a product" question, it demands more specialization and focus on AI on our end.

Another aspect, somehow unique to AI (but not only), is the computing cost. We sometimes see startups that show nice topline sales and revenue traction, but currently at a huge cost of revenue due to computing costs, i.e., payment to AI infrastructure. This is, of course, not sustainable. You could compare that to elements such as storage and bandwidth that used to be very expensive and are now cheaper by order of magnitude, but whether this will be the case in specific element of AI, especially when there is a need to scale it up and further improve the quality of the models, it's a question that we need to answer on a case by case basis.

The last aspect that actually relates to any trend, is our analysis of what will happen in five years. Especially in AI, this is super difficult to predict. Is what the startup is doing exciting enough today, so they can keep growing and raising more money? Is consolidation expected in the segment? Tailwind helps everyone and a part of our job is to measure and forecast the tailwind in a few years.

What specific financial performance indicators (KPIs) do you examine when assessing a potential AI company? Are there any AI-specific metrics you consider particularly important?

We spoke about the efficiency and the cost of the infrastructure. If I translate that into financial KPIs then we're talking about a KPI called capital efficiency: in startups, this is how well they utilize money invested to generate revenue growth. Naturally this is harder to achieve in the initial stages when you come up with a unique product that requires a lot of resources to develop and train. The cost of infrastructure is related to the good old Product Market Fit KPI. If the startup demonstrate that customers are willing to pay a lot in order to buy their product, then it means that they create value with AI and the cost of infrastructure shouldn't be an issue. That is a key element of product market fit.

How do you approach the valuation of early-stage AI startups, which often lack significant revenues but possess strong technological potential?

As we focus on Series A, we do not invest in companies that cannot show repeatable and scalable revenue. Naturally even at our stage we look at the product and IP, team, segment and expected trends and not only the financial traction of the company.

What financial risks do you associate with investing in AI companies, beyond the usual technological risks?

High infrastructure costs, data dependencies, and regulatory risks that impact revenue are important, and I want to add one more: how likely are the big players to enter this space, and how well they will do when they try?

Throughout the years, I have seen founders trying to compete with Facebook, Google and Microsoft head to head. This is super tough if you go against them on their core product. The startup graveyard is full of startups that tried to create better search or cooler social networks. Where the big guys fall short is in specific market segments and niches.

Now, a niche could be a $10b niche like medical data records analysis or sub segment of insurance risk management, but it requires focus and knowledge in this segment. This is a risk that we feel comfortable with.

Do you focus on particular subdomains within AI?

We have seen all of the AI technological elements in many startups. I personally like when a founder comes from the "old world" pre-AI, dealt with machine learning afterwards and grew into NLP and later Generative AI. It gives them the perspective of what's important and how to make things work. Sometimes a deterministic process combined with AI is exactly what Open AI will not do and what makes their product win.

How do you view AI’s impact on traditional industries? Are there specific AI technologies you believe will be especially transformative in certain sectors?

I share the view that we are facing an unprecedented revolution. AI will be everywhere. Can we imagine our business world without constant internet access? The same thing will be with AI, and even more. This isn't limited to "what you can do over Zoom" as some investors say. Amazon already employ one million robots. They might not be utilizing AI but it shows you the scale that an advanced company is willing to grow into.

There are two means of transformation: where humans can be replaced to save costs, and where a new market can be created. Companies becoming more efficient will result more profits in some cases, and cheaper consumer prices in others. But true transformation is where new products and markets are being created. We see some tectonic moves in Fintech, Insurtech, transportation, eCommerce and other segments. It might take some time but when the change starts happening in a segment, it will happen very quickly.

What specific AI trends in Israel do you see as having strong exit potential in the next five years? Are there niches where you believe Israeli startups particularly excel?

In Israel, where AI and Cyber converge, exits will happen quicker than in other areas. We have seen exist inside the Israeli ecosystem already, and the market focus will help more deals to mature.

Are there gaps or missing segments in the Israeli AI landscape that you’ve identified? What types of AI founders are you especially looking to back right now in Israel?

I am surprised by the variety and quality of AI technology and founders that we come across. Nevertheless we need to keep monitoring the market data which currently shows that we are lagging mostly in infrastructure. We need to see that areas that lack short term financial incentives are still addressed.