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AI

4 things VCs get wrong about AI

VCs have a detailed playbook for investing in software-as-a-service (SaaS) companies that has served them well in recent years. Successful SaaS businesses provide predictable, recurring revenue that can be grown by acquiring more subscriptions at little additional cost, making them an attractive investment.

But the lessons that VCs have learned from their SaaS investments turn out not to be applicable to the world of artificial intelligence. AI companies follow a very different trajectory from SaaS providers, and the old rules simply aren’t valid.

Here are four things VCs get wrong about AI because of their past success investing in SaaS:

1. ARR growth is not the best indicator of long-term success in AI

Venture capitalists continue to pour money into AI companies at an astonishing — some might say ridiculous — rate. Databricks has raised a staggering $3.5 billion in funding, including a $1 billion Series G in February, followed six months later by a $1.6 billion Series H in August at a $38 billion valuation. DataRobot recently announced a $300 million Series G financing round, bringing its valuation to $6.3 billion.

While the private market is crazy for AI, the public market is showing signs of more rational behavior. Publicly traded C3.ai has lost 70% of its value relative to all-time high that it notched immediately after its IPO in December 2020. In early September 2021, the company released fiscal Q1 results, which were a cause for further disappointment in the stock that caused a further dip of nearly 10%.

So what’s going on? What is happening is that the private markets — funded by VCs — fundamentally do not understand AI. The fact is, AI is not hard to sell. But AI is quite hard to implement and have it deliver value.

Ordinarily in SaaS, the real peril is market risk — will customers buy? That’s why private markets have always been organized around looking at annual recurring revenue (ARR) growth. If you can show fast ARR growth, then clearly customers want to buy your product and therefore your product must be good.

But the AI market doesn’t work like that. In the AI market, many customers are willing to buy because they’re desperate for a solution to their pressing business problems and the promise of AI is so big. So what happens is that VCs keep pouring money into the likes of Databricks and DataRobot and driving them to absurd valuations without stopping to consider that billions are going into these companies to at best create hundreds of millions of ARR. It’s brute-forcing funding of an already over-hyped market. But the fact remains that these companies have failed to produce results for their customers on a systematic basis.

A report from Forrester sheds some interesting light on what’s really happening behind the numbers being claimed by some AI companies with these huge valuations. Databricks reported that four customers had a three-year net positive ROI of 417%. DataRobot had four customers that over three years created a 514% return. The problem is that out of the hundreds of customers these companies have, they must have cherry-picked some of their very best customers for these analyses, and their returns are still not that impressive. Their best customers are barely doubling their annual return — hardly an ideal scenario for a transformative technology that should deliver at least 10x back from your investment.

Rather than focusing on the most important factor — whether customers are getting tangible value out of AI — VCs are obsessing over ARR growth. The fastest way to get to ARR expansion is brute-force sales, selling services to cover the gaps because you don’t have the time to build the right product. That is why you see so many consulting toolkits masquerading as products in the data science and machine learning market.

2. A minimum viable product isn’t the way to test the market

From the world of SaaS, VCs learned to value the minimum viable product (MVP), an early version of a software product with just enough features to be usable so that potential customers can provide feedback for future product development. VCs have come to expect that if customers would buy the MVP, they will buy the full-version product. Building an MVP has become standard operating procedure in the world of SaaS because it shows VCs that customers would pay money for a product that addressed a specific problem.

But that approach doesn’t work with AI. With AI, it’s not a question of building an MVP to find out whether people will pay. It’s really a question of finding out where AI can create value. Put another way, it’s not about testing product-market fit; it’s about testing product-value delivery. Those are two very different concepts.

3. Successful AI pilots don’t always mean successful real-world outcomes

Another rule that VCs have adopted from the world of SaaS is the notion that successful AI pilots mean successful outcomes. It’s true that if you have successfully piloted a SaaS product like Salesforce with a small group of salespeople under controlled conditions, you can reasonably extrapolate from the pilot and have a clear view of how the software will perform in widespread production.

But that doesn’t work with AI. The way AI performs in the lab is fundamentally different from what it does in the wild. You can run an AI pilot based on cleaned-up data and find that if you follow the AI predictions and recommendations, your company will theoretically make $100 million. But by the time you put the AI into production, the data has changed. Business conditions have changed. Your end users may not accept the recommendations of the AI. Instead of making $100 million, you may actually lose money, because the AI leads to bad business decisions.

You can’t extrapolate from an AI pilot in the way that you can with SaaS.

4. Signing up customers for long-term contracts isn’t a good indicator the vendor’s AI works

VCs like it when customers sign up for long-term contracts with a vendor; they see that as a strong indicator of long-term success and revenue. But that’s not necessarily true with AI. The value created by AI grows so fast and is potentially so transformative that any vendor who truly believes in their technology isn’t trying to sell a three-year contract. A confident AI vendor wants to sell a short contract, show the value created by the AI, and then negotiate price.

The AI vendors that put a lot of effort into locking up customers to long-term contracts are the ones who are afraid that their products won’t create value in the near term. What they’re trying to do is lock in a three-year contract and then hope that somewhere down the line the product will become good enough that value will finally be created before renewal discussions happen. And often, that never happens. According to a study by MIT/BCG, only 10% of enterprises get any value from AI projects.

VCs have been trained to think that any vendor that signs lots of long-term contracts must have a better product, when in the world of AI, the opposite is true.

Getting smart about AI

VCs need to get smart about AI and not rely on their old SaaS playbooks. AI is a rapidly developing transformative technology, every bit as much as the Internet was in the 1990s. When the Internet was emerging, one of the lucky breaks we got was that VCs did not obsess over the profitability or revenues of Internet companies in order to invest in them. They basically said, “Let’s look at whether people are getting value from the technology.” If people adopt the technology and get value from it, you don’t have to worry a lot about revenue or profitability at the beginning. If you create value, you will make money.

Maybe it’s time to bring that early Internet mindset to AI and start evaluating emerging technologies based on whether customers are getting value rather than relying on brute-forced ARR figures. AI is destined to be a game-changing technology, every bit as much as the Internet. As long as businesses get sustained value from AI, it will be successful — and very profitable for investors. Smart VCs understand this and will reap the rewards.

Arijit Sengupta is CEO and Founder of Aible.

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Categories
Game

Atari VCS is now available to buy

Three years after its , you can finally buy the Atari VCS. It’s available starting today through the , and . Over on Atari’s website, there are two versions of the console available. You can buy the $300 Oynx base system or the $400 Black Walnut all-in bundle. The latter comes with a joystick and Xbox-like gamepad. Both wireless peripherals retail for $60 separately.

In addition to coming bundled with the Atari Vault, a collection of 100 free retro games, the console can double as a PC. It can run most modern desktop operating systems, including Windows, Linux and ChromeOS, and comes with Google’s . If the bundled games aren’t your thing, the Atari VCS also has access to an expanded and optimized version of game subscription service that includes thousands of retro titles from a variety of vintage platforms.

Between setbacks like the , it seemed like the was doomed to become vaporware. Now the question becomes if there’s an audience for a $400 console that’s mostly a nostalgia play. Projects like prove it’s difficult to break into a landscape dominated by giants like Microsoft, Nintendo and Sony. 

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Categories
Game

Atari VCS console is finally coming to retail stores this month

Crowdfunded projects have come and gone and many that have lasted years never delivered. Given those factors, it’s almost a surprise that the Atari VCS hybrid PC console even launched at all. Yet here we are, almost four years after it first debuted as the AtariBox, the Atari VCS is finally launching in stores. Its commercial success, however, still hangs in the balance and in the hands of those who can figure out what the device is really for.

The Atari VCS was born from an age where every old console or arcade cabinet maker was coming out with revivals of their classic hardware, albeit in smaller forms. Atari, however, had a different vision for what would become the Atari 2600’s spiritual successor. Full-sized or perhaps even a little larger, the Atari VCS was made to be a general-purpose home entertainment and productivity system.

It may look like a classic Atari console but the Atari VCS is actually a PC at heart. Running on an AMD Ryzen chip with 8GB of RAM and 32GB of eMMC storage, the Atari VCS can even run Linux or Windows or even Steam OS. In fact, the custom OS that Atari uses even includes the Google Chrome web browser.

Of course, the main purpose of the Atari VCS is for gaming and it comes with support for all types of games. Each purchase comes with a copy of Atari VCS Vault which contains a hundred arcade and Atari 2600 games as well as Atari’s own Missile Command: Recharged. There’s also free access to Antstream Arcade, a game-streaming service dedicated to retro titles, and a library of indie games that include Boulder Dash Deluxe.

Atari clearly wants the Atari VCS to be a gaming console, a game development platform, an entertainment system, and a productivity machine in one but it remains to be seen if it will hit the mark on even one of those. The hybrid console goes on sale on June 15 from Best Buy, GameStop, Micro Center, and the official Atari VCS online store. The base unit costs $299.99 and the Wireless Classic Joystick and Wireless Modern Controller cost $59.99 each. While the console supports almost any PC controller, retailers also offer a bundle of those three pieces for #399.99.

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Categories
AI

Gartner: 75% of VCs will use AI to make investment decisions by 2025

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By 2025, more than 75% of venture capital and early-stage investor executive reviews will be informed using AI and data analytics. In other words, AI might determine whether a company makes it to a human evaluation at all, deemphasizing the importance of pitch decks and financials. That’s according to a new whitepaper by Gartner, which predicts that in the next four years, the AI- and data-science-equipped investor will become commonplace.

Increased advanced analytics capabilities are shifting the early-stage venture investing strategy away from “gut feel” and qualitative decision-making to a “platform-based” quantitative process, according to Patrick Stakenas, senior research director at Gartner. Stakenas says that data gathered from sources like LinkedIn, PitchBook, Crunchbase, and Owler, along with third-party data marketplaces, will be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” Stakenas said. “The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured. AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”

As the Gartner report points out, current technology is capable of providing insights into customer desires and predicting future behavior. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about a person from real-time or audio recordings. While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organizations will be leveraging it to determine which leadership teams are most likely to succeed.

Already, one venture capital firm — San Francisco, California-based Signalfire — is using a proprietary platform called Beacon to track the performance of more than 6 million companies. At the cost of over $10 million per year, the platform draws on 10 million data sources including academic publications, patent registries, open-source contributions, regulatory filings, company webpages, sales data, social networks, and even raw credit card data. Companies that are outperforming are flagged up on a dashboard, allowing Signalfire to see deals ostensibly earlier than traditional venture firms.

This isn’t to suggest that AI and machine learning are — or will be — a silver bullet when it comes to investment decisions. In an experiment last November, Harvard Business Review built an investment algorithm and compared its performance with the returns of 255 angel investors. Leveraging state-of-the-art techniques, they trained the system to select the most promising investment opportunities among 623 deals from one of the largest European angel networks. The model, whose decisions were based on the same data available to investors, outperformed novice investors but fared worse than experienced investors.

Part of the problem with Harvard Business Review’s model was that it exhibited biases that experienced investors did not. For example, the algorithm tended to pick white entrepreneurs rather than entrepreneurs of color and preferred investing in startups with male founders. That’s potentially because women tend to be disadvantaged in the funding process and ultimately raise less venture capital which may lead to their startups not being as successful. In other words, the AI was projecting into future discrimination the societal mechanisms that make ventures of female and non-white founders die at an earlier stage.

Because it might not be possible to completely eliminate these forms of bias, it’s crucial that investors take a “hybrid approach” to AI-informed decision-making with humans in the loop, according to Harvard Business Review. While it’s true that algorithms can have an easier time picking out better portfolios because they analyze data at scale, potentially avoiding bad investments, there’s always a tradeoff between fairness and efficiency.

“Managers and investors should consider that algorithms produce predictions about potential future outcomes rather than decisions. Depending on how predictions are intended to be used, they are based on human judgement that may (or may not) result in improved decision making and action,” Harvard Business Review wrote in its analysis. “In complex and uncertain decision environments, the central question is, thus, not whether human decision making should be replaced, but rather how it should be augmented by combining the strengths of human and artificial intelligence

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