Imagine: your personal AI agent notifies you that a hair dryer you’ve been eyeing is now on sale. The product page highlights benefits tailored to your curly hair, while the agent confirms it will arrive before your upcoming trip.

With your approval, the agent handles the purchase through your secure wallet. Later, it proactively suggests complementary hair care products for the summer season.

This world of autonomous commerce isn’t as far off as it seems. Tech and e-commerce leaders — including OpenAI, Nvidia, Amazon, Walmart, Google, and Apple — are already building AI systems that are steps away from conducting transactions. 

AI agents will impact each stage of the customer journey, streamlining the path to purchase and fundamentally transforming how businesses build relationships with consumers and drive loyalty.

We use CB Insights data on early-stage fundraising, public companies, and industry partnerships to analyze how generative AI — especially AI agents — is transforming the customer journey.

In the 11-page report, we cover 3 predictions that emerged from our analysis: 

1. First-party transaction data will shape the future of AI-driven personalization. As personalization becomes more sophisticated at the awareness and consideration stages, companies with direct access to first-party data will have an edge.
2. Direct-to-agent (D2A) commerce will kill traditional loyalty. With AI agents handling browsing and shopping, traditional loyalty programs will lose effectiveness as agents optimize shopping across a select group of merchants.
3. A few AI agents will own the customer relationship. Companies like Amazon, Google, and Apple — with critical distribution and financial services infrastructure — are well-positioned in commerce.

RELATED RESEARCH FROM CB INSIGHTS

• How generative AI could supercharge retailers’ ad networks
• AI is revitalizing the “virtual try-on” space — this is what’s coming next, including interactive Google ads
• The enterprise AI agents & copilots market map
• 15 tech trends to watch closely in 2025
• Generative AI — e-commerce search
• AI-optimized e-commerce UX
• Generative AI — customer support operations

For information on reprint rights or other inquiries, please contact reprints@cbinsights.com.

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The divide between open-source and closed-source AI models is reshaping tech industry dynamics. 

Tech leaders have staked out clear positions: Meta and xAI are open-sourcing models like Llama 3.1 and Grok-1, while Google and OpenAI have largely walled off their systems. Investment flows are also split between both approaches. Since 2020, private open-source AI model developers have attracted $14.9B in venture funding, while closed-source developers have secured $37.5B — reflecting different bets on how AI innovation will unfold.

The core difference lies in access: closed-source approaches keep model details and weights proprietary, while open-source development makes these elements available so models can be more freely studied, run, and adapted.

Companies building generative AI applications must understand this evolving landscape as it has crucial implications for the infrastructure they adopt. Based on current trends, we expect:

1. Consolidation around frontier models: Closed-source models from players like OpenAI, Anthropic, and Google will dominate the market. Only tech giants like Meta, Nvidia, and Alibaba are likely to sustain the costs of developing open-source models that can compete on performance with proprietary ones. Frontier model training costs are growing 2.4x annually, driven by hardware, staffing, and energy needs, according to Epoch AI.
2. Revenue and investment gaps threaten open-source model developers’ viability: While burning cash, closed-source leaders like Anthropic and OpenAI lead the private market in funding, revenue, and commercial traction. Open-source developers face similar costs but struggle to generate revenue or attract capital investment ($14.9B vs. closed-source’s $37.5B since 2020). This suggests they will move to commercialize their closed models (e.g., Mistral AI) and/or pivot to smaller, specialized offerings (e.g., Aleph Alpha).   
3. Smaller models drive open-source adoption: Industry leaders, alongside a range of smaller players, are releasing smaller, specialized open-source models, as evidenced by Microsoft‘s Phi, Google’s Gemma, and Apple‘s OpenELM. This suggests a two-tier market for enterprises evaluating the landscape: closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

Below, we use CB Insights data to map out the open-source and closed-source AI landscape. Our analysis focuses on foundation models — the powerful, general-purpose AI systems that form a critical infrastructure layer.

CB Insights customers can track every company mentioned in this analysis using this search. We used the Generative AI — large language model (LLM) developers and Generative AI — image generation market profiles to establish the private market landscape, focusing on companies that have received funding and are developing foundation models. 

Table of contents

Consolidation around frontier models

• Industry leaders are divided in their approaches
• Closed-source developers lead the private market in equity funding
• Performance gaps converge, with largest companies’ models topping leaderboard

Revenue and investment gaps threaten open-source model developers’ viability

• OpenAI dominates LLM adoption and revenue, followed by Anthropic
• Open-source’s path to revenue remains unclear
• Investors hedge their bets

Smaller models drive open-source adoption

• A wave of smaller foundation model players will move away from frontier model development
• Market bifurcation accelerates

Consolidation around frontier models

Industry leaders are divided in their approaches 

Many big tech companies — like Google and Apple — are releasing a combination of open and closed models, typically keeping their flagship models proprietary while releasing lighter-weight open models as an extension of their research efforts.

Meta and Nvidia, meanwhile, are also open-sourcing flagship models. 

Note: When developers “open-source” AI models, they do so on a spectrum, publicly disclosing some combination or element of the: model weights (the learned parameters of a neural network, crucial for the model’s performance and capabilities as they encapsulate the knowledge acquired during training), underlying source code, and original training data. Open-sourcing may also involve licensing the model for free commercial use.

Open-source proponents are preparing for an open-source future

Meta CEO Mark Zuckerberg wrote in July that “Meta is committed to open source AI,” with the belief that an open ecosystem will eventually become the standard. On earnings calls, Meta is the most active big tech company in terms of open-source mentions. 

At the same time, Zuckerberg acknowledged in April on Dwarkesh Patel’s podcast that the company will only continue open-sourcing “as long as it’s helping us.” 

In July 2024, Meta released the model weights for its latest Llama model family so developers can fine-tune the model (train it on custom data). However, the source code and model architecture remain unavailable, limiting full modification or analysis. Meanwhile, Nvidia released both the model weights and training code for its NVLM 1.0 family of large multimodal language models in September 2024. 

Closed-source proponents view revenue as crucial for top resources and talent

For example, Baidu CEO Robin Li said in an internal memo that open-source models “make little sense.” From a business perspective, he noted, “Being closed source allows us to make money, and only by making money can we attract computational resources and talent.”

Safety remains central to the debate

Critics of open-source AI models fear they will be misused by malicious actors to access harmful information (like how to build a bomb or write code for a cyber attack). They also raise national security concerns, with critics suggesting foreign actors’ ability to use open-source models to advance military applications (like weapons systems and intelligence tech) will undermine strategic advantages held by countries that currently lead in AI development. 

Closed models use techniques like Reinforcement Learning by Human Feedback (RLHF) during fine-tuning to limit the harmful content the model can produce. Open models, meanwhile, are more likely to be deployed without these safeguards. 

On the other hand, open-source AI proponents argue, as highlighted in Mozilla’s Joint Statement on AI Safety and Openness with 1,800+ signatories, that increasing access to foundation models will ultimately make them safer, thanks to increased transparency, scrutiny, and knowledge sharing. 

Closed-source developers lead the private market in equity funding

The private market is also split, with closed developers leading in equity funding. 

While both Mistral AI and xAI are proponents of open-source, both of their flagship models are currently closed. 

The cost to develop frontier models — taking into account hardware, staffing, and energy consumption costs — is growing 2.4x per year. This is driving the fundraising race. 

Performance gaps converge, with largest companies’ models topping leaderboard

Leading open-source models, like Meta’s largest Llama model, are making their way onto the MMLU leaderboard — a test that evaluates a language model’s knowledge and reasoning skills. The expanded version, MMLU-Pro, includes more challenging questions to assess advanced reasoning capabilities in AI models.

At the same time, proprietary models continue to outpace open-source ones by several months in terms of release dates. 

The leaderboard itself is dominated by the largest companies in both big tech and the private market, indicating market consolidation at the frontier level. 

At this stage, a16z partner Marc Andreessen has posited we could be approaching a “race to the bottom” — a future point where there are no moats for foundation models, and open-source performance is on par with closed-source. This has come into focus in recent months as frontier labs like OpenAI and Google have focused on smaller model development and other products (like agents) as performance gains slow and as release dates for the largest models (such as a potential GPT-5) get pushed back.

Below we look at how revenue and adoption gaps in the private market also point to increasing consolidation.

Revenue and investment gaps threaten open-source model developers’ viability in the private market

OpenAI dominates LLM adoption and revenue, followed by Anthropic

As LLM developers burn through cash, the focus has shifted to customer adoption — and revenue. 

Based on CB Insights business relationship data, OpenAI is far ahead of its peers in terms of its disclosed partnerships and client relationships. 

This business relationship analysis is limited to publicly disclosed partnership, client, and licensing agreements for pure-play model developers to highlight adoption trends. Relationships are not exhaustive and are directionally representative of trends across model developers’ partner and client relationships.

In terms of revenue, OpenAI leads, with projections of $3.7B in annual revenues for 2024 and $11.6B for 2025. However, it’s also been burning cash: the company projected midway through the year that it would lose $5B in 2024.

Open-source’s path to revenue remains unclear

While revenues for open-source model developers are not publicly available in most cases, reports suggest revenue generation is more limited — especially given the competition from Meta’s Llama.

The embattled Stability AI reportedly generated $8M in 2022 and less than $5M in the first quarter of 2024 (while losing over $30M). In June 2024, it secured an $80M funding deal that included the forgiveness of $100M in debts owed to cloud providers and other suppliers. 

Meanwhile, Mistral AI has an unclear path to revenue, per The Information reporting — it sells access to its API, and under 10% of its users pay for Mistral’s larger commercial models through partners. Most of its smaller, open-source models are free. 

Source: CB Insights — Mistral funding insight

Following the traditional approach to monetizing open-source businesses — building paid support offerings or tools (plugins, security, migration, apps on top) around the open-source core — some model developers are now building more enterprise capabilities into their platforms. 

For example, Databricks offers security and other paid support services around its open-source LLM, DBRX. Similarly, Aleph Alpha launched in August 2024 a “sovereign AI” platform designed to help corporations and governments deploy LLMs (not necessarily its own) with added control and transparency features to serve the European market. 

Investors hedge their bets

Most leading investors in private foundation model developers have backed companies developing both closed and open models.

Corporate investors figure heavily — Nvidia, Alibaba, and Microsoft, for example, have offered computing power and funds for development. These investments are aimed at feeding their core business focuses, such as AI chips and cloud computing. AWS, Azure, and Google Cloud all host both open and closed models.

Venture investors are taking sides:

• Coatue, the leading VC by unique companies backed, has called open source “the heartbeat of AI.” It’s taking a complementary approach: “We see open-source models as firmly having a place alongside proprietary ones.”
• a16z’s founders are proponents of open-source models, arguing that their transparency and accessibility will help ensure that AI is developed securely and ethically. In 2024, the two largest a16z-backed AI deals went to open-source LLM developers xAI and Mistral AI.
• Meanwhile, Founders Fund partner John Luttig has argued that the future of foundation models is closed-source. Khosla Ventures’ Vinod Khosla (a backer of OpenAI) also argues in favor of closed-source AI for safety reasons. 

The investor split reflects uncertainty over which ecosystem will dominate and where the greatest value creation will occur. The relative difference in funding totals ($14.9B in equity funding to open-source model developers vs. $37.5B to closed-source), as well as the data available on revenue, suggests that a closed approach for private developers appears poised to win out, especially given the most performant open models at this point are from big tech leaders.

Smaller models drive open-source adoption

A wave of smaller foundation model players will move away from frontier model development 

The conditions of a) high compute costs, b) limited moats, and c) competition from big tech have created a market ripe for a shake-up.

We’re seeing a wave of smaller foundation model players:

• Collapse into big tech: Adept, Inflection, and Character.AI have all essentially been “acqui-hired” by big tech companies, with founders and large portions of teams joining the acquirers. These deals reflect the high costs of model development, with licensing payments often directed to investors. 
• Paywall frontier models: Some open-source AI developers now sell access to premium models while keeping basic versions free — similar to strategies used by big tech. For example, Mistral AI’s flagship model Mistral Large is built for commercial use (not open-source) and is available on Azure in partnership with Microsoft.
• Focus on smaller, open-source models: Developers like Germany-based Aleph Alpha and Israel-based AI21 Labs have shifted in 2024 from competing on general-purpose LLMs to building lighter-weight, optimized models and related AI tools. These models are open-source, with paid services layered on top.

Market bifurcation accelerates

Based on these trends, the AI model market is splitting into two tiers:

• Frontier models are largely dominated by closed-source offerings from well-funded players (OpenAI, Anthropic, Google), which can sustain growing compute costs. Meta’s Llama remains the most notable open-source alternative.
• Smaller models, optimized for specific use cases or edge deployment, are supported by a growing open-source ecosystem. These small language models (SLMs) have fewer parameters than LLMs, making them cheaper to train and easier to run.

Industry leaders are releasing smaller, open-source models to advance research efforts and to promote edge applications: Google with Gemma, Microsoft with Phi, and Apple with OpenELM. 

For example, Microsoft highlighted in a recent earnings call: 

“We have also built the world’s most popular SLMs, which offer performance comparable to larger models but are small enough to run on a laptop or mobile device. Anker, Ashley, AT&T, EY, and Thomson Reuters, for example, are all already exploring how to use our SLM Phi for their applications.” — Satya Nadella, CEO of Microsoft, Q2’24 Earnings Call  

Meanwhile, of the 11 private SLM development platforms we identified, roughly half are already in the process of deploying their products.

Smaller, open models are also gaining traction in sectors like financial services and healthcare, where keeping sensitive data on-premises can be a need.

For example, a VP of machine learning at a health insurance company needed a solution for training healthcare models and looked to Hugging Face’s open-source library. In our May 2024 conversation, the buyer highlighted the opportunity of SLMs for their use case:

For now, it’s clear a hybrid approach is winning with enterprises: they will look to closed-source frontier models for the most sophisticated applications and open-source smaller models for edge and specialized use cases.

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Humans will still play a crucial role — but instead of assembling parts or operating machinery, they will maintain robots and keep them running.

In these future factories, robots coordinate in unison, completing work automatically — without breaks, every hour of the day — to get products out the door. 

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Prefer to listen in? Check out our discussion of the report here: 

An empowered digital workforce would reshape industries as we know them. The implications would be enormous, changing how companies hire and scale, as well as what they can achieve with a small headcount. 

That future isn’t too far off. 

The idea of autonomous AI agents — LLM-powered bots that can independently reason and execute tasks — caught on like wildfire in 2023, marking an important evolution beyond chatbots and copilots. 

OpenAI CEO Sam Altman has described agents as “AI’s killer function” as recently as May 2024.  

While much of the tech remains limited in its ability to execute tasks reliably, use cases are gaining traction in horizontal enterprise applications like customer support, sales, and engineering.

We mined CB Insights startup, financing, business model, and buyer interview data to map the evolving landscape and analyze its future. 

In the 28-page report, we cover: 

• The state of AI agents: Investment is surging to companies in the space, but limitations — most notably, agent reliability — remain. 
• Leading horizontal applications and impacts: The landscape of VC-backed agent startups is dominated by a focus on horizontal applications — across sales, customer support, and other enterprise and general productivity workflows.
• Emerging industry applications and opportunities: While few agentic companies focus on single industries, companies are emerging to target workflows across financial services, industrials, and more. 

Download the full report to get all of the data and analysis.

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Large language models (LLMs) are enabling new capabilities with simulation and generative design, empowering humanoid robots, and paving the way for copilots to improve productivity across manufacturing, sales, and the aftermarket.

With the growing focus on software-defined, electric vehicles, and as automakers look to build a more digital-forward car buying experience, these technologies are growing increasingly relevant.

Using the CB Insights technology intelligence platform, we analyzed signals such as funding, analyst briefings, business relationships, headcount growth, Mosaic scores, and more to identify 15 high-momentum technologies that auto stakeholders should be watching in 2024.

Our 19-page report digs into trends like:

• AI and quantum computing are accelerating vehicle development and reducing R&D costs, allowing OEMs to introduce new designs, prototypes, and materials.
• Automotive production will see gains from more sophisticated robotics and automation solutions, laying the groundwork for cost-effective, modular production of next-generation vehicles.
• AI is enabling a more personalized, efficient automotive sales process, with new fintech solutions and engagement tools speeding up the sales cycle and improving accessibility for car buyers.
• Connected vehicle technology is enabling real-time visibility into vehicle health and security, providing OEMs with valuable data to enhance other facets of the value chain and the in-vehicle experience.
• Chatbots and computer vision-led inspection solutions are driving efficiencies in vehicle repair, improving service desk productivity and technician accuracy.

And much more. Download the full report to see them all.

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• Virtual power plants to increase grid flexibility during peak demand and reduce the need for distant, centralized power stations
• Microgrids to increase power reliability and security
• Remote drone monitoring to reduce operational costs for maintaining grid assets such as power lines and substations
• Grid storage to increase grid sustainability and reliability by storing renewable energy until it is needed
• Blockchain technology to help track renewable energy credits and facilitate peer-to-peer energy trading

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping the future of the power grid.

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Nowhere is that more visible than the Super Bowl, which has long been the advertiser’s golden opportunity to reach a broad audience of captivated US consumers. Last year’s event drew an estimated 112M TV viewers, and brands paid up to $7M for a 30-second ad slot.

To stand out from the pack, TV advertisers are turning to a range of emerging technologies to make ads that have the potential to go viral, deliver a strong return on ad spend (ROAS), and reach entirely new audiences. 

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• Autonomous tractors capable of tilling, plowing, and fertilizing fields without a human operator in the tractor cabin  
• Connected farm platforms that will allow farm operators to remotely track and manage operations 
• Agricultural drones that can map, spray, and spread across fields and orchards autonomously
• Harvesting robots that have the ability to carefully pick and transport produce
• Automated weed control, which consists of robots that can identify and then remove or treat weeds in fields 
• Soil optimization tech that allows farm operators to understand their soil at a granular level, analyze how to optimize yields, and measure carbon storage in soil 
• Crop biologicals that deploy natural means to treat and protect crops 

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping the farms of the future.

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In the future, conversational AI and AI-powered recommendation engines will make ordering food more human-friendly, accessible, and convenient. These technologies will be able to integrate with drive-thrus, smart speakers, apps, and even metaverse avatars, allowing customers to simply speak their order using a convenient channel while the restaurant makes hyper-relevant recommendations that can increase order value.

This is one of the key technologies that will shape the future of the fast food experience. Below, we dive into this technology: how it works, who has an edge, and how it’s changing the future of fast food.

WHAT are CONVERSATIONAL AI AND AI-POWERED RECOMMENDATION ENGINES?

Conversational AI allows customers to interact with software using their voice or text (via chatbots), with natural language processing (NLP) allowing for human-like communication. AI-powered recommendation engines use data to develop personalized recommendations for customers. 

By pairing these technologies, restaurants can create virtual ordering assistants that can take orders, upsell, integrate loyalty, and answer questions from guests, allowing restaurants to prioritize staff for other tasks like food prep or order fulfillment.

FIRST MOVERS

In a rare move for the fast food giant, McDonald’s acquired AI-powered recommendation engine Dynamic Yield in 2019. While McDonald’s ended up selling the company to Mastercard 2 years later so Dynamic Yield could scale to other industries, McDonald’s still has a partnership that integrates Dynamic Yield’s software into its drive-thrus and app. Through this collaboration, McDonald’s can dynamically change menu boards to make smarter suggestions to customers based on time of day, weather, restaurant traffic, and trending items. 

Meanwhile, the applications of conversational AI for restaurants are not limited to just drive-thrus — the tech could also be used for customer-facing interfaces like phone and SMS orders, voice assistants, and mobile apps. Domino’s has partnered with ConverseNow which provides conversational AI for phone orders. After an order is placed through ConverseNow’s software, which is integrated with the existing point-of-sale system, employees can immediately see the order and prepare it. ConverseNow also provides conversational AI for drive-thrus and has raised an $11M Series B round in Q3’22.

Recently, Panera announced a partnership with OpenCity to begin testing their conversational AI in select drive-thrus. 

Finally, one key instance of a quick-service restaurant combining these two technologies is Wendy’s partnership with Google Cloud. Through Google’s speech-to-text technology, Wendy’s can take drive-thru and phone orders and then in future instances use voice recognition to make personalized recommendations for the customer.

IMPLICATIONS 

• For restaurants with drive-thrus, conversational AI can help reduce labor costs and increase average order size. AI tools can also personalize order recommendations based on local preferences, weather, and item availability. Using this technology to cross-sell during order flows has been shown to increase sales by upwards of 10%.
• To best facilitate consumer adoption, these solutions should initially be positioned in scenarios where guests aren’t expecting to interact face-to-face with employees, like at the drive-thru speaker. Conversational AI solutions can also be integrated into smartphone apps, which can improve accessibility for disabled users. 
• Restaurants should keep in mind that potential challenges to conversational AI can come from unexpected requests or substitutions made by customers, meaning that some sort of human staff will need to be available in these situations. Additionally, programs will need to be updated with any menu changes or limited edition items. 
• The back-end data collected by conversational AI and AI-powered recommendation engines adds another layer of value that would be more difficult to synthesize with human employees. Integrating AI solutions throughout the order flow can help restaurants A/B test different strategies to help increase check size.

Read more in this report about how tech will shape the future of fast food, including:

• The metaverse, which will become an extension of the brand experience
• AI tongues and social listening to help brands quickly develop and test new menu items to meet changing consumer demands
• Robotic food preparation will automate cooking to increase profitability and reduce food waste
• Autonomous delivery will help reduce reliance on gig workers
• NFT loyalty programs will create unique experiences and boost customer loyalty

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In the future, brands will reward their most loyal customers with NFT-based collectibles and experiences to attract younger customers. They will also collect insightful first-party data through resales and trades of NFTs.

This is one of the key technologies that will shape the future of the fast food experience. Below, we dive into this technology: how it works, who has an edge, and how it’s changing the future of fast food.

WHAT ARE NFT LOYALTY PROGRAMS?

Non-fungible tokens, or NFTs, are a blockchain-based way to prove ownership of digital assets — which can range from images to songs to videos to clothes for avatars. NFT loyalty programs can be integrated into an existing digital loyalty program and can allow new ways for brands to interact with customers. 

NFTs that provide customers with more use beyond the digital asset itself, such as access to exclusive events or physical products, are called utility NFTs. Utility NFTs help brands build communities with consumers post-purchase and better connect virtual and IRL experiences.

FIRST MOVERS

In Q3’22, Starbucks announced that it will be rolling out an NFT loyalty program called Starbucks Odyssey. The coffee giant, which generates over half its sales from rewards members, wants to use the program to build deeper emotional connections with customers by creating an immersive brand experience that rewards participants with NFTs. 

While Starbucks is one of the first quick-service restaurants to embed NFTs into their loyalty program, other national chains have experimented with the medium. In early 2021, Taco Bell sold 5 NFTs to raise money for its charity The Taco Bell Foundation. Similarly, Domino’s used the attention garnered from its NFT sales to sell merchandise for its nonprofit — Domino’s Partners Foundation.

Source: Rarible

IMPLICATIONS 

• As customers’ smartphones become increasingly crowded with apps dedicated to loyalty programs and a barrage of notifications, NFT loyalty programs give brands the opportunity to provide a differentiated value proposition by offering unique rewards and experiences, virtual or otherwise.
• Restaurants with customers that already value the brand’s digital assets (like Starbucks) will likely find more success with NFTs. Restaurants without this established digital presence need to consider how they can connect an NFT to substantive rewards, like award points or exclusive access, to make them meaningful for consumers.
• Restaurants can take advantage of the exclusivity and status that early adopters associate with NFTs. Because NFT owners derive value from the NFT’s scarcity, customers are more likely to display and share the branded token on a public platform like a social media account.
• Through NFTs, brands can also appeal to a younger demographic that is more likely to actively seek out and engage with these types of rewards. However, the size of the addressable audience could present a challenge as adoption among consumers remains low.

Read more in this report about how tech will shape the future of fast food, including:

• The metaverse, which will become an extension of the brand experience
• AI tongues and social listening to help brands quickly develop and test new menu items to meet changing consumer demands
• Conversational AI and AI recommendation engines will power online and in-store orders, allowing restaurants to upsell orders through personalized suggestions and prioritize labor for other customer service operations 
• Robotic food preparation will automate cooking to increase profitability and reduce food waste
• Autonomous delivery will help reduce reliance on gig workers

The post How restaurants can capture high-value customers using NFT loyalty programs appeared first on CB Insights Research.

In 2030, the metaverse will be a new digital channel for restaurants to offer engaging, interactive experiences that boost brand awareness and customer experience. In the metaverse, customers can step into a virtual restaurant to browse menus, meet friends, and even order real food that will then be delivered to their door. 

This is one of the key technologies that will shape the future of the fast food experience. Below, we dive into this technology: how it works, who has an edge, and how it’s changing the future of fast food.

WHAT IS THE METAVERSE?

The metaverse is the concept of shared worlds driven by virtual products and digital experiences that are highly immersive and interactive.

Clients can learn more about the companies building the metaverse in our metaverse Market Map.

In the context of the future of fast food, restaurants will be able to build virtual experiences, offer exclusive menu items or digital merchandise, and host virtual events for customers.

FIRST MOVERS

Chipotle was one of the first quick-service restaurant brands to create a virtual experience in the metaverse. In 2021, the fast-casual Mexican chain partnered with Roblox to digitize the brand’s famous Halloween promotion. Players could dress their avatars in Chipotle Halloween costumes, play mini-games, and receive codes for free burritos to redeem in person. 

During the campaign, the virtual restaurant was visited over 8M times and set a company record for digital sales in a single day. In 2022, the fast-casual giant even launched a new menu item on Roblox through an interactive game. 

McDonald’s also made an entrance into the metaverse with a Chinese Lunar New Year event. Partnering with AltspaceVR and Spatial, the brand designed a hall of Chinese zodiacs that players could explore through VR. The food giant has also filed trademark applications that suggest a virtual restaurant may be opening soon that sells both virtual and actual goods with home delivery.

Source: Wendy’s

Wendy’s is another fast food chain experimenting with the metaverse. Wendyverse opened in Horizon Worlds (Meta’s virtual reality world using the Oculus VR platform) in April 2022. Through this partnership with Meta, players were given a set of tasks to complete that would give them a promo code to redeem at a real Wendy’s restaurant.

IMPLICATIONS 

• The metaverse gives restaurant chains space to experiment with marketing and menus. Fast food chains investing in the metaverse can use the virtual space to try out new strategies that build brand awareness among new and existing audiences. This can include collaborations, virtual events, or even just experimenting with tweaks to existing considerations, like restaurant layout and menu items.
• The metaverse is still in its early stages. Restaurant brands and operators should pay attention to the successes and failures of other consumer industries — like fashion and grocery — that are further ahead in their metaverse efforts, to better understand what strategies work best with consumers.
• The metaverse will be an additional channel for facilitating delivery orders and can help drive consumers to stores, growing both revenue and customer loyalty for restaurant brands.

Read more in this report about how tech will shape the future of fast food, including:

• AI tongues and social listening to help brands quickly develop and test new menu items to meet changing consumer demands
• Conversational AI and AI recommendation engines will power online and in-store orders, allowing restaurants to upsell orders through personalized suggestions and prioritize labor for other customer service operations 
• Robotic food preparation will automate cooking to increase profitability and reduce food waste
• Autonomous delivery will help reduce reliance on gig workers
• NFT loyalty programs will create unique experiences and boost customer loyalty

The post How restaurant chains can use the metaverse to create a new digital channel for orders appeared first on CB Insights Research.

In 2030, autonomous vehicles will be routinely delivering food on behalf of restaurants — opening up opportunities for these companies to offer new customer experiences (like 24/7 delivery) and lowering customer costs by removing outlays like the expectation to tip.

This is one of the key technologies that will shape the future of the fast food experience. Below, we dive into this technology: how it works, who has an edge, and how it’s changing the future of fast food.

WHAT IS THE AUTONOMOUS DELIVERY?

Autonomous delivery uses ground-based robots or aerial drones to automate the delivery process, often incorporating features such as temperature control and tracking for perishable goods. Some autonomous logistics vehicles are designed to travel on roads alongside passenger cars, while others are designed for use on sidewalks.

FIRST MOVERS

Early players in autonomous ground vehicles include unicorn Nuro, who has received funding from Chipotle and has partnered with Domino’s. In Q3’22, Uber Eats announced a 10-year partnership with Nuro to make deliveries in Texas with plans to expand to California. 

SOURCE: DOMINO’S

Refraction AI, another player in the space, partnered with Chick-fil-A in 2022 to begin testing autonomous delivery. Both Nuro and Refraction AI use self-contained vehicles smaller than the average car to make contactless deliveries. 

In drone deliveries, Flytrex has seen significant activity from restaurants. In 2022, the company partnered with Brinker International, the holding company of Chili’s Grill & Bar and Maggiano’s, as well as sandwich chain Jersey Mike’s. These partnerships point to the broader corporate interest in drone delivery.

IMPLICATIONS 

• Autonomous delivery still faces a number of barriers on the path to commercialization, most notably a complex regulatory environment that has made it challenging to launch and scale services. However, restaurants should monitor early movers that have been announcing regional tests in the past year.
• Drone delivery offers a number of advantages over ground-based alternatives. For example, drones can carry out deliveries more quickly and affordably (and have been shown to reduce energy usage by about 90%), without adding to road traffic or burning fossil fuels. This can also mean that orders will be fresher they arrive.
• Restaurants should begin investing in their own fleet or partner with third-party autonomous vehicle providers to help reduce delivery fees, reduce reliance on gig workers, and have more control over the branded delivery experience.
• As autonomous delivery grows, restaurants should evaluate how restaurant layout and location will need to adapt. For instance, consider where autonomous vehicles will be stored when not in use and where staff will load the vehicle with orders. Also strategize how different geographies (such as dense, urban areas) might be more suited to smaller ghost kitchens with drones versus full sit-down restaurants with wheeled vehicles.

Read more in this report about how tech will shape the future of fast food, including:

• The metaverse, which will become an extension of the brand experience
• AI tongues and social listening to help brands quickly develop and test new menu items to meet changing consumer demands
• Conversational AI and AI recommendation engines will power online and in-store orders, allowing restaurants to upsell orders through personalized suggestions and prioritize labor for other customer service operations 
• Robotic food preparation will automate cooking to increase profitability and reduce food waste
• NFT loyalty programs will create unique experiences and boost customer loyalty

The post How restaurants can reduce reliance on gig workers using autonomous delivery appeared first on CB Insights Research.

In the future, restaurants will be able to create and test new menu items in a faster, less wasteful, and more cost-efficient way. Combining AI tongues and social listening, restaurants can identify early trends as well as tweak recipes and menus to grow revenue.

This is one of the key technologies that will shape the future of the fast food experience. Below, we dive into this technology: how it works, who has an edge, and how it’s changing the future of fast food.

WHAT ARE AI TONGUES AND SOCIAL LISTENING?

AI tongues and social listening are separate technologies that can work together to speed up R&D. 

AI tongues use sensors and artificial intelligence to replicate how humans will perceive a taste — allowing for rapid taste tests without relying on human subjects. Social listening technology taps into consumer sentiment conveyed in social media posts, online reviews, and other forms of digital content to generate actionable insights about what new ingredients, flavors, or habits might be gaining popularity. 

By combining these solutions, restaurants can predict what new items will gain traction with consumers and hone recipes and flavors that are likely to appeal to a broad variety of tastes. Detecting these new consumer habits early on can also help restaurants adapt other considerations beyond taste, including opening hours, layout, and category offerings.

FIRST MOVERS

Because this technology is still relatively nascent, first movers have typically been in the CPG industry, where players have expansive brands and product lines to experiment with novel ingredient technologies.

In 2019, McCormick & Company partnered with IBM and its newly developed Hypertaste, an AI-assisted “digital tongue.” McCormick says that it reduced the time it took to develop new products by 70% and that it was able to tweak formulations between 50 and 150 times before finalizing.

Source: IBM

Tastewise uses AI and social listening across data sources like menus, reviews, and social media to create dynamic dashboards for restaurants, food service, and CPG companies on consumer trends. The company has raised $25M since 2019. 

Using similar data sources, Singapore-based AI Palette tracks ingredient and flavor trends across 15 different languages to identify opportunities for new products by geographic region. It also predicts how long consumer demand might last. 

Both startups have partnerships with large CPG companies like Nestle, PepsiCo, and Kellogg’s.

For more information on what early-stage companies are disrupting food and beverage R&D, read our early-stage food & beverage product development and distribution Market Map.

IMPLICATIONS 

• AI tongues and social listening technology can help restaurants release new menu items in a fraction of the usual time: traditional R&D can take around a year for restaurants. This tech can help restaurants reduce uncertainty and timelines around product development, meet evolving consumer expectations, and stay ahead of competitors. 
• Restaurants should evaluate partnering or investing in AI tongues early. Because the technology is still nascent, providers will be more likely to form exclusive partnerships to test and scale capabilities — giving both sides advantages over competitors.
• Restaurants should prioritize social listening tools that can easily incorporate new and emerging platforms and social media to get a more accurate picture of consumer sentiment and boost loyalty. These technologies can also help restaurants tweak items to better match regional tastes, making new items more successful and profitable.

Read more in this report about how tech will shape the future of fast food, including:

• The metaverse will become an extension of the brand experience 
• Conversational AI and AI recommendation engines will power online and in-store orders, allowing restaurants to upsell orders through personalized suggestions and prioritize labor for other customer service operations 
• Robotic food preparation will automate cooking to increase profitability and reduce food waste
• Autonomous delivery will help reduce reliance on gig workers
• NFT loyalty programs will create unique experiences and boost customer loyalty

The post How restaurants can reduce the cost of R&D using AI tongues and social listening appeared first on CB Insights Research.

In the future, fast food restaurants will have adapted their store layouts to rely exclusively on robotic food preparation to cook menu items. Robots will work in sync to fill orders, account for customer substitutions, and communicate with autonomous delivery vehicles to quickly bring customers fresh, accurate orders.

This is one of the key technologies that will shape the future of the fast food experience. Below, we dive into this technology: how it works, who has an edge, and how it’s changing the future of fast food.

WHAT IS THE ROBOTIC FOOD PREPARATION?

Robotic food preparation hardware companies develop robotic systems that leverage AI and machine learning to cook, assemble, and package meals. Cooking robots can cook food more consistently and with fewer errors (like over or undercooking), leading to less waste and higher customer satisfaction. Some can be retrofitted to existing kitchens, while others are stand-alone machines.

FIRST MOVERS

Over the past year, Miso Robotics has racked up a significant number of partnerships with national quick-service restaurant chains like Chipotle, White Castle, and Buffalo Wild Wings to automate cooking foods like tortilla chips, burgers, and chicken wings. In Q4’21, the company raised a $35M Series D round, bringing its total funding to $66M.

Source: Miso Robotics

While US brands have significantly ramped up on these partnerships in the last 12 months, cooking robots have already seen significant activity in regions like Asia. For instance, in 2020, KFC partnered with Hyundai Robotics in Korea to develop robot chefs.

To dig deeper into this market landscape and connect directly with vendors, CB Insights clients can check out the Automation in Restaurant Tech Expert Collection and interactive Market Map. 

IMPLICATIONS 

• While initial investments in these solutions can be expensive, national quick-service restaurant chains have begun significant partnerships and testing, paving the way for lower costs in the future. Leaders like Miso Robotics are looking to cut the cost of robots by a third to roughly $20K per robot.
• Restaurants that are looking to test robotic food preparation without fully investing in buying robotics should consider startups that offer Robots-as-a-Service (RaaS) models. In many cases, the cost per hour is cheaper than a part-time employee.
• Robotic food preparation tech for performing a simple repetitive task efficiently, like flipping burgers or manning fryers, has seen the widest adoption in the space. Restaurants should think strategically about how robotic food preparation can be used in their current menus or how new menu items can be created to prioritize these preparation methods.
• Robotic food preparation can help reduce food waste. Because these robots reduce human error, like burning food or cross-contamination, they also help with margins by reducing food waste and enabling more standardized quality control across locations.

Read more in this report about how tech will shape the future of fast food, including:

• The metaverse, which will become an extension of the brand experience
• AI tongues and social listening to help brands quickly develop and test new menu items to meet changing consumer demands
• Conversational AI and AI recommendation engines will power online and in-store orders, allowing restaurants to upsell orders through personalized suggestions and prioritize labor for other customer service operations 
• Autonomous delivery will help reduce reliance on gig workers
• NFT loyalty programs will create unique experiences and boost customer loyalty

The post How restaurants can increase profitability and reduce food waste using robotic food preparation appeared first on CB Insights Research.

• Alternative investing platforms that open access to non-traditional assets like private equity, real estate, art, and crypto for retail investors and their advisors
• ESG powered by AI that gives retail and institutional investors more complete and accurate assessments of investments’ environmental and social impacts
• Asset tokenization that converts financial or physical assets into digital tokens via blockchain technology, providing the infrastructure for companies and investors to more efficiently issue, track, and trade assets

In this report, we dive into how these 3 technologies are poised to have a transformative impact on both retail and institutional investing and the players that have an edge.

The post The Future of Investing: How technology is reshaping wealth and asset management appeared first on CB Insights Research.

• Automated logistics facilitating efficient product delivery and returns
• Interactive product setup and care solutions enabling immersive self-service
• Predictive customer service to provide proactive assistance
• AI-powered virtual agents enabling hyper-personalized interactions
• Utility NFTs creating post-purchase engagement opportunities
• Embedded recommerce solutions integrating with the post-purchase journey

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping what happens after we make a purchase. 

The post The Future of the Post-Purchase Experience: How technology is transforming the way we shop appeared first on CB Insights Research.

• Drone- and robot-based exploration to reduce capital costs and time to production
• Nanosatellite monitoring to reduce ongoing operation & maintenance costs
• Aerial gas leak detection to detect and address methane emissions
• Carbon source capture to reduce overall greenhouse gas emissions
• Geothermal wells to increase oil field productivity and sustainability

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping the future of upstream oil & gas.

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If you’re already a customer, log in here.

The post The Future of the Oil Field: How technology will boost oil well performance & sustainability appeared first on CB Insights Research.

• AR/VR & reality capture to visualize project plans, train workers onsite, and measure daily progress
• Building information modeling (BIM) & digital twins to give realistic representations of project plans and reduce product development costs
• Drones for surveying, monitoring, and maintaining safe operations
• Workplace safety & exosuits to improve safety and reduce the physical strain of work
• Automation & robotics to drive efficiency and productivity by reducing manual labor and minimizing errors
• Industrial internet of everything (IIoE) for overseeing and coordinating entire projects, from design to execution

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping future job sites around the planet.

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If you’re already a customer, log in here.

The post The Future of the Job Site: How robotics, digital twins, and wearable devices are making the job site more connected, efficient, and safe appeared first on CB Insights Research.

The auto insurance industry has already seen significant advances in the last decade in terms of its digital sophistication. But there is still a long way to go.

The future of auto insurance will be built on key technologies such as:

Want to see more research? Join a demo of the CB Insights platform.

If you’re already a customer, log in here.

The post The Future of Auto Insurance: How technology is changing every aspect of the $766B market appeared first on CB Insights Research.

• The metaverse, which will become an extension of the brand experience
• AI tongues and social listening to help brands quickly develop and test new menu items and meet consumer demands
• Conversational AI and AI-powered recommendation engines that will handle online and in-store orders, allowing restaurants to upsell orders through personalized suggestions and prioritize labor for other customer service operations
• Robotic food preparation that will automate cooking to increase profitability and reduce food waste
• Autonomous delivery that will help reduce reliance on gig workers
• NFT loyalty programs that will create unique experiences and boost customer loyalty

In this report, we dive into these technologies: how they work, who has an edge, and how they’re shaping the future of restaurants.

The post The Future of Fast Food: How robot chefs, virtual ordering, and autonomous delivery will feed us in 2030 appeared first on CB Insights Research.

• • Virtual fleets that provide on-demand vehicle-sharing services to eliminate the cost of owning a fleet
  • Electric roads to recharge electric vehicles (EVs) as they drive, allowing fleets to operate more sustainably
  • Digital twins for fleet management to enable better decision-making and scenario management
  • Fully autonomous fleets that sidestep driver shortages while also improving fleet efficiency and road safety
  • In-vehicle payment platforms to improve efficiency by automating payments associated with charging, services, and maintenance

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping the future of the fleet.

Want to see more research? Join a demo of the CB Insights platform.

If you’re already a customer, log in here.

The post The Future of the Fleet: How technology is making transporting freight more efficient and sustainable appeared first on CB Insights Research.

• Autonomous delivery systems, which leverage a combination of self-driving cars, sidewalk robots, and drones to minimize last-mile inefficiencies
• En-route manufacturing, which brings fulfillment closer to the end customer, enabling faster delivery at a lower cost 
• Robotic stores on wheels to bring the shopping experience to the consumer’s doorstep, driving delivery volume with added convenience
• Delivery lockers and storage systems that improve the economics of last-mile delivery and facilitate reverse logistics
• Digital twins to optimize the flow of resources across the supply chain, generating efficiencies in the delivery journey

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping the future of last-mile delivery.

The post The Future of Last-Mile Delivery: How technology is tackling the complexity and cost of fulfillment appeared first on CB Insights Research.

• Automated logistics facilitating efficient product delivery and returns
• Interactive product setup and care solutions enabling immersive self-service
• Predictive customer service to provide proactive assistance
• AI-powered virtual agents enabling hyper-personalized interactions
• Utility NFTs creating post-purchase engagement opportunities
• Embedded recommerce solutions integrating with the post-purchase journey

Below, we dive into these technologies: how they work, who has an edge, and how they’re shaping what happens after we make a purchase. 

Want to see more research? Join a demo of the CB Insights platform.

If you’re already a customer, log in here.

The post The Future of the Post-Purchase Experience: How technology is transforming the way we shop appeared first on CB Insights Research.