The Robo-Advisor Arms Race
Can upstart robo-advisors compete against scale?
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It was going to happen sooner or later.
When they launched roughly five years ago, tech-driven companies such as Betterment or Wealthfront had the audacious and laudable goal of taking on the incumbents of the gargantuan wealth management industry. Many traditional wealth managers were skeptical of portfolios being driven by artificial intelligence, and adopted a “wait and see” approach. If it became clear that the machines were indeed taking over the wealth management industry, they could then find some way to reverse-engineer their way into the market, using their scale and connections to make up ground.
As the upstarts won new accounts and proved out the robo-advisor business model, the incumbents that dominate the traditional finance scene leaped into action. In 2015, behemoths like Vanguard and Charles Schwab, which each manage trillions of dollars of assets, fought back by introducing their own robo-advisor products. Meanwhile, Blackrock made an acquisition of an existing platform (FutureAdvisor) to enter the market, and just months ago mutual fund giant Fidelity launched its own robo-product called Fidelity Go.
The scale of these companies meant that domination would become inevitable. Vanguard, for example, took its Personal Advisor Services platform from $0 in assets under management (AUM) last year to $41 billion today. By our math, that’s more than all other major U.S. robo-advisors combined.
Charles Schwab, which has 9.3 million existing customers for its discount brokerage services, had no problem bringing customers over to its new platform. It also has $10 billion in AUM already in just a year, which is more than Betterment and Wealthfront combined.
Spokespeople for the independent robo-advisors will tell you that they are building products for millennials, with an eye on a bigger prize. As wealth is transferred to the millennial generation over the coming years, they will be in position to take advantage of this as the brands that millennials trust. We are certain that these startups can evolve into great companies with this mission, but we also wonder if they ultimately left money on the table.
Were they not aggressive enough? Could they have partnered with a bigger institution to roll out their product faster? Could they have gotten a bigger piece of the pie?
It’s hard to say, but the robo-advisor space continues to be an interesting one to watch. It also teaches us an interesting lesson about trying to compete with mega-sized companies, which have scale, expertise, and resources at their disposal.
Infographic: Generative AI Explained by AI
What exactly is generative AI and how does it work? This infographic, created using generative AI tools such as Midjourney and ChatGPT, explains it all.
Generative AI Explained by AI
After years of research, it appears that artificial intelligence (AI) is reaching a sort of tipping point, capturing the imaginations of everyone from students saving time on their essay writing to leaders at the world’s largest tech companies. Excitement is building around the possibilities that AI tools unlock, but what exactly these tools are capable of and how they work is still not widely understood.
We could write about this in detail, but given how advanced tools like ChatGPT have become, it only seems right to see what generative AI has to say about itself.
Everything in the infographic above – from illustrations and icons to the text descriptions—was created using generative AI tools such as Midjourney. Everything that follows in this article was generated using ChatGPT based on specific prompts.
Without further ado, generative AI as explained by generative AI.
Generative AI: An Introduction
Generative AI refers to a category of artificial intelligence (AI) algorithms that generate new outputs based on the data they have been trained on. Unlike traditional AI systems that are designed to recognize patterns and make predictions, generative AI creates new content in the form of images, text, audio, and more.
Generative AI uses a type of deep learning called generative adversarial networks (GANs) to create new content. A GAN consists of two neural networks: a generator that creates new data and a discriminator that evaluates the data. The generator and discriminator work together, with the generator improving its outputs based on the feedback it receives from the discriminator until it generates content that is indistinguishable from real data.
Generative AI has a wide range of applications, including:
- Images: Generative AI can create new images based on existing ones, such as creating a new portrait based on a person’s face or a new landscape based on existing scenery
- Text: Generative AI can be used to write news articles, poetry, and even scripts. It can also be used to translate text from one language to another
- Audio: Generative AI can generate new music tracks, sound effects, and even voice acting
People have concerns that generative AI and automation will lead to job displacement and unemployment, as machines become capable of performing tasks that were previously done by humans. They worry that the increasing use of AI will lead to a shrinking job market, particularly in industries such as manufacturing, customer service, and data entry.
Generative AI has the potential to disrupt several industries, including:
- Advertising: Generative AI can create new advertisements based on existing ones, making it easier for companies to reach new audiences
- Art and Design: Generative AI can help artists and designers create new works by generating new ideas and concepts
- Entertainment: Generative AI can create new video games, movies, and TV shows, making it easier for content creators to reach new audiences
Overall, while there are valid concerns about the impact of AI on the job market, there are also many potential benefits that could positively impact workers and the economy.
In the short term, generative AI tools can have positive impacts on the job market as well. For example, AI can automate repetitive and time-consuming tasks, and help humans make faster and more informed decisions by processing and analyzing large amounts of data. AI tools can free up time for humans to focus on more creative and value-adding work.
How This Article Was Created
This article was created using a language model AI trained by OpenAI. The AI was trained on a large dataset of text and was able to generate a new article based on the prompt given. In simple terms, the AI was fed information about what to write about and then generated the article based on that information.
In conclusion, generative AI is a powerful tool that has the potential to revolutionize several industries. With its ability to create new content based on existing data, generative AI has the potential to change the way we create and consume content in the future.
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