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Eight states in the U.S. have been selected for a three-year pilot program that will allow aircraft models to transport passengers and cargo.

When a business self-limits its audience, it makes its chances for success more challenging. Yes, that niche crowd might aggressively support you, but finding broader support may prove elusive.That has broadly been the case with restaurants serving very specific audiences. Whether you target a kosher, gluten-free, halal, vegetarian, or vegan audience, an eatery generally needs to appeal beyond that core crowd to achieve sustainability. “Vegan restaurants face hardships that often mirror those of nonvegan establishments, including rising rent and operating costs. But those challenges are compounded by a limited clientele and, in some locations, the high prices of plant-based protein alternatives like Beyond Meat and Impossible Foods,” Bloomberg Law reported.Only 1% of Americans identify as vegans, according to Gallup, while the same poll showed that 3% consider themselves vegetarian. That means a vegan restaurant, unless it’s in a very large city, needs to appeal to non-vegan customers.That has been a major challenge that has recently driven a number of vegan and vegetarian chains into bankruptcy. Planta, a pioneer in the vegan space, has been under Chapter 11 bankruptcy protection. It has now been forced into Chapter 7 bankruptcy and will be liquidated.Planta moves into Chapter 7 liquidationPlanta’s website makes no mention of any sort of bankruptcy and still shows seven operating locations. The chain’s Facebook page also does not acknowledge the filing or the change in bankruptcy status, but documents on PacerMonitor confirm it. “A Delaware bankruptcy judge has ordered the Chapter 11 case of vegan restaurant group Planta to be converted to a Chapter 7 liquidation, after the debtor said it has no other option,” Law360 reported. The chain has not publicly commented on the shift to a Chapter 7 bankruptcy nor did it immediately respond to a request for comment from TheStreet. David Carickhof has been appointed as interim trustee to oversee the Chapter 7 bankruptcy process, according to a notice filed on PacerMonitor on March 9. What happened in a Chapter 7 bankruptcy”A Chapter 7 bankruptcy case does not involve the filing of a plan of repayment as in Chapter 13 (or Chapter 11). Instead, the bankruptcy trustee gathers and sells the debtor’s nonexempt assets and uses the proceeds of such assets to pay holders of claims (creditors) in accordance with the provisions of the Bankruptcy Code,” according to the USCourts.gov website.Related stories:22-year-old fast-food burger chain closes locations in bankruptcyRestaurant chain closed 50% of locations in Chapter 11 bankruptcyUnlike a Chapter 11 bankruptcy, where efforts are made to find a path for the company to continue as a going concern, a Chapter 7 filing is about maximizing the amount of money returned to creditors.”The division of proceeds is made according to the hierarchy of the claimants’ rights,” according to Cornell Law.

Planta offered vegan sushi.Planta

A quick look at Planta’s strugglesPlanta always faced a challenging market.“Despite hopes that burgers, sausages and chicken made from soy, peas and beans would curb Americans’ love of eating butchered animals – thereby reducing the rampant deforestation, water pollution and planet-heating emissions involved in raising livestock – these alternatives languish at just 1% of the total meat market in the US,” The Guardian reported.Plant-based restaurant chainPlanta filed for Chapter 11 bankruptcy on May 12, 2025, through its parent company CHG US Holdings and 17 affiliated entities in the U.S. Bankruptcy Court for the District of Delaware, according to documents on PacerMonitor.The bankruptcy filing reported very limited assets of roughly $50,000 to $100,000 compared with liabilities estimated between $10 million and $50 million, reported PacerMonitor..The company said the filing was intended to restructure operations as declining consumer spending on dining out pressured the business, according to Reuters.Planta began closing locations as part of the restructuring, including sites in West Palm Beach, Miami Beach, Los Angeles (Brentwood), and Atlanta’s Buckhead neighborhood, according to TheStreet.The chain had expanded quickly after launching in 2016, operating restaurants in cities including Miami, New York, Chicago, Washington D.C., Los Angeles, Atlanta, and Toronto, according to Reuters.The company eventually emerged from bankruptcy through a sale of its assets, with the brand continuing operations at a reduced footprint of about eight locations, according to Nation’s Restaurant News.A Delaware bankruptcy judge converted the Chapter 11 case of vegan restaurant chain Planta to a Chapter 7 liquidation after the company said it had no remaining path to reorganize, reported Law360. The conversion means a court-appointed trustee will now take control of the case and liquidate remaining assets to repay creditors, added Law360.Joseph Barsalona, a Pashman Stein Walder Hayden partner representing Planta, said the chain’s upscale dining appeals to a narrow niche of a limited clientele. He also noted a broader decline in demand for plant-based offerings, Bloomberg Law reported.”Veganism is not going away, but it’s not going to be generally accepted amongst regular burger-eating Americans, and so some of these stores never even got off the ground,” he added.Vegan/plant-based restaurant closures in 2025″Planta’s rapid construction of 12 restaurants in three years exacerbated pandemic-induced disruption and other macroeconomic headwinds,” Restaurant Dive reported. The current operating environment has made an already challenging market even more difficult for plant-based restaurants.“When customers have uncertainty and they’re really watching every dollar, the cost of things becomes more important, which puts specialty outlets perhaps at a competitive disadvantage,” Jason Kaplan, CEO of restaurant and hospitality consulting firm JK Consulting. told Bloomberg Law.Planta is not the only player in the space facing large challenges:Plum Bistro (Seattle, WA): Flagship vegan spot, plus Plum Chopped, closed in Jan. 2025.Paradox Cafe (Portland, OR): Longtime vegan-friendly diner, closed in 2025.Fair Weather (Portland, OR): Vegan/pescatarian brunch spot, reopened briefly but then closed permanently in July 2025.Sage Regenerative Kitchen & Brewery (Pasadena & Echo Park, CA): Formerly Sage Vegan Bistro; closed in Jan 2025 after reintroducing animal products.Veggie Grill: Closed 12 locations (about 40% of units) in 2025 as part of restructuring.Neat Burger: Backed by Lewis Hamilton & Leonardo DiCaprio: Closed all UK locations in April 2025, entered liquidation in July 2025.Related: Iconic brewpub chain shutters 39 locations amid bankruptcy wave

TheStreet aims to feature only the best products and services. If you buy something via one of our links, we may earn a commission.Why we love this dealIf you’re in the mood to swap out your winter bedding with a fresh look for spring, Amazon has an amazing deal on the Wongs Bedding Botanical Quilt Set you shouldn’t sleep on. This beautiful farmhouse-inspired set is on sale for $28, which is a steal considering it’s a queen size and comes with three pieces. It includes a quilt and two matching pillow shams to complete the look. If you’ve never owned a quilt set, you’re in for a treat. Not only does it look like something that one of the esteemed ladies of Bridgerton might be getting her beauty sleep on, but it’s lightweight and suitable for use in every season. And with 13 prints to choose from, it’s easy to find something that matches your personal style.  Wongs Bedding Botanical Quilt Set, $28 (was $29) at Amazon

Courtesy of Amazon

Why should someone buy it?Bedding comes in many different fabrics, and there’s a perfect one for everyone. Hot sleepers might opt for a cooling comforter or linen bedding, while cold sleepers may be happier with polyester or flannel as they retain more heat. Quilts come in every fabric choice, a wide variety of fabrics, so it’s important to familiarize yourself with them so you can pick the right one for you.This quilt and the matching shams are made of an Oeko-Tex Standard 100 certified microfiber polyester. I own a similar one that I got from Amazon that’s made of the same material, and I can confirm that it’s really versatile bedding. I use it in my guest room all year. It’s the perfect weight for spring and summer, and I add a comforter underneath it for extra warmth in fall and winter. My guests always say the bed is comfortable, so I’m happy with the quilt’s performance. It’s easy to care for and washes well, and the color hasn’t faded at all. Best of all, it’s gotten softer over time.This quilt is available in many prints and colors, and 10 of them are on sale. While this one is a great price, there’s also a navy blue floral print that’s only $18. It’s worth noting that all prints are reversible and feature a solid color on one side.Who is this product for?This quilt set is ideal for a guest bedroom, a dorm room, or even your own bedroom. I wouldn’t recommend it for people who get easily overheated during the night, since it is polyester. But if you feel chilly while you sleep and enjoy a blanket that provides more warmth, this set would be a perfect fit for you. It’s also ideal for shoppers who want bedding that’s easy to wash and gets softer over time.Details to knowColors: 13.Material: Microfiber polyester.Sizes: Full, queen, king, and California king.Related: Macy’s $100 4-piece sheet set with cottagecore charm is on sale for only $30Why do shoppers love it?More than 250 shoppers gave this quilt set a five-star rating, highlighting that it washes well, is lightweight, and of good quality. “It is a very light and soft coverlet, perfect for summer,” one shopper wrote. “The print immediately perked up the room. I definitely recommend this purchase, but check the measurements. It is not a bedspread and will not touch the floor.”A second shopper said, “I bought this because the bedspread I was using was so heavy and difficult to launder. This lightweight spread is just perfect. Better than I expected. It fits great, and it’s reversible.”Shop more deals Dobuyly Patchwork Quilt Set, $35 (was $37) at AmazonFlysheep Boho Quilt Set, $35 at AmazonWrensonge 3-Piece Quilt Set, $50 (was $55) at AmazonIf you want to dress your bed in the perfect spring frock, the Wongs Bedding Botanical Quilt Set is an ideal choice for just $28 at Amazon. It’ll make you want to sip tea in bed, which is absolutely a thing you should be doing anyway.

Retirees considering where to live often start with taxes. In Ohio, several tax policies — including the exemption of Social Security benefits — may make the state attractive for retirement, according to Chuck Grimm, a senior wealth advisor with MAI Capital Management.In an interview, Grimm, also a member of the American Institute of CPAs’ PFP Champions task force, said Ohio has reduced income tax rates over the past decade and eliminated its state estate tax. That, combined with other factors, makes Ohio an attractive place to retire.Below is a transcript of that interview, edited for clarity and brevity.Why Ohio may be an attractive place to retireRobert Powell: Ohio’s tax-friendly policies and relatively low cost of living may make the Buckeye State an appealing place to retire. Here to talk about that is Chuck Grimm, senior wealth adviser at MAI Capital Management. Chuck, welcome.Chuck Grimm: My pleasure, Bob.Setting the stage: Living in OhioRobert Powell: Let’s do a little table setting before we talk about the specifics of the tax structure in Ohio. Tell us a bit about the state overall.Chuck Grimm: Well, if you could see out my window, it’s getting better. We experience four seasons in Ohio. Unfortunately, in March we’re wrapping up winter and approaching spring. It’s been challenging this year, as it has been in most parts of the country. But overall, it’s a pretty good experience living here.

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Ohio personal income tax ratesRobert Powell: When people think about taxes, they usually start with the state’s personal income tax. Tell us how that works in Ohio.Chuck Grimm: The personal state income tax situation has improved over the years. Currently, personal income up to $26,050 is not taxed. Income from $26,100 is taxed at 2.75%, and income above $100,000 is taxed at 3.125%.To give some perspective, in 2020 the rate on income above $100,000 was about 3.8%. Back in 2010 it was around 5.7%.I believe Ohio has realized that when people retire they often want to stay in the place where they grew up, and taxes can affect that decision. Lower rates may help keep people here.How retirement income is taxed in OhioRobert Powell: With the state income tax, that obviously applies to earned income. Does it also apply to interest, dividends and capital gains?Chuck Grimm: Yes. Similar to the federal Form 1040, those types of income are generally included.However, Ohio does offer some exclusions for retirees. Social Security benefits are not taxed in Ohio. Military retirement benefits and railroad retirement benefits are also excluded.Robert Powell: Many retirees also rely on income from IRAs or 401(k) or 403(b) plans.Chuck Grimm: Right, and those distributions are taxed at the normal state income tax rates.Ohio tax credits available to retireesRobert Powell: Are there any credits available for seniors with respect to retirement income?Chuck Grimm: Yes, there are a couple of credits available. One is a retirement income credit. If you receive $8,000 or more in retirement income, you can qualify for a $200 credit. It’s not large, but it is a credit available to retirees.There are also other credits, depending on income levels.Another important one is the homestead exemption for people age 65 or older. Ohio property taxes are based on the county and municipality where you live, but the homestead exemption can reduce the value of your property for tax purposes, which lowers property taxes. To qualify, you must meet certain income requirements.Ohio’s cost of living and housing affordabilityRobert Powell: I believe the price of a home in Ohio is significantly lower than the national median. Chuck Grimm: Yes, it’s very attractive to live here. According to Forbes estimates, the cost of living is about 8% to 12% below the national average.Home prices are generally very affordable. Along the lakefront, prices have increased over the years. That can make it more challenging for longtime residents because as property values increase, property taxes also rise.But the lakefront is very attractive, so it remains a popular place to live.Estate and inheritance taxesRobert Powell: What about estate or inheritance taxes? Should retirees be concerned about those?Chuck Grimm: Ohio abolished its state estate tax in 2013.Before that, in 2012, the lifetime exclusion was $338,000, which was relatively low. Estates above that threshold were taxed roughly between 6% and 7%.Also worth readingRetirees may want to rebalance as markets broaden, volatility risesWhy “breaking even” on Social Security is the wrong goalThe $83,250 secret every solo entrepreneur needs to know for 2026Medicare mistakes seniors wish they’d known soonerElon Musk says stop retirement saving: Experts call it ‘nonsense’Social Security benefits could drop 7% in 2032That could be significant depending on the size of the estate, especially on top of the federal estate tax at the time. Eliminating the estate tax removed a major concern for many residents.Sales taxes in OhioRobert Powell: What about the state sales tax?Chuck Grimm: The statewide sales tax is currently 5.75%. Depending on where you live, additional county or city taxes may apply.Some school districts also impose a school district income tax. But generally speaking, the statewide rate starts at 5.75%.529 plan tax benefitsRobert Powell: Many retirees want to set aside money for grandchildren in 529 plans. Are there any state tax benefits for doing that?Chuck Grimm: Yes. Contributions to 529 plans in Ohio are deductible up to $4,000 per beneficiary.We also have a scholarship credit that was introduced a couple of years ago. It’s a dollar-for-dollar credit against your Ohio state taxes.Initially, you had to make that contribution during the calendar year, which made planning difficult because you had to estimate your tax liability. Now the state allows contributions up to April 15.Single filers can claim up to a $750 credit, and married couples filing jointly can claim up to $1,500.That credit has become very attractive, especially as private-school tuition has increased.Why retirees should evaluate the full financial pictureRobert Powell: For someone currently living in Ohio or considering moving there in retirement, it seems worthwhile to consult a financial professional to run “what-if” scenarios.Chuck Grimm: Absolutely. You have to look at the big picture.For example, Ohio ranks around 14th highest for state income taxes. But nine states have no income tax at all.If you look at a state like Florida, which has no income tax, the overall cost equation may still be different. Property taxes and homeowners insurance costs, especially after recent hurricanes, can be significantly higher.So while you may save on income tax, you could pay more in other areas. Looking at the entire financial picture is essential when deciding where to live in retirement.The role of financial planning professionalsRobert Powell: You’re also a member of the Personal Financial Planning Champions Task Force at the American Institute of CPAs. People watching this conversation can go to the AICPA website to find a professional who can help with these scenarios.Chuck Grimm: Yes. The Personal Financial Planning program is designed to expand our reach and help the public understand that CPAs can offer planning advice along with tax preparation.Members in that program hold the PFS credential through the AICPA. We’re trying to encourage people to reach out and discuss their planning needs with professionals who have experience in these areas.Related: Jean Chatzky sends blunt message to Americans on 401(k)s, IRAs

Yesterday amid a flurry of enterprise AI product updates, Google announced arguably its most significant one for enterprise customers: the public preview availability of Gemini Embedding 2, its new embeddings model — a significant evolution in how machines represent and retrieve information across different media types. While previous embedding models were largely restricted to text, this new model natively integrates text, images, video, audio, and documents into a single numerical space — reducing latency by as much as 70% for some customers and reducing total cost for enterprises who use AI models powered by their own data to complete business tasks.Who needs and uses an embedding model?For those who have encountered the term “embeddings” in AI discussions but find it abstract, a useful analogy is that of a universal library. In a traditional library, books are organized by metadata: author, title, or genre. In the “embedding space” of an AI, information is organized by ideas.Imagine a library where books aren’t organized by the Dewey Decimal System, but by their “vibe” or “essence”. In this library, a biography of Steve Jobs would physically fly across the room to sit next to a technical manual for a Macintosh. A poem about a sunset would drift toward a photography book of the Pacific Coast, with all thematically similar content organized in beautiful hovering “clouds” of books. This is basically what an embedding model does.An embedding model takes complex data—like a sentence, a photo of a sunset, or a snippet of a podcast—and converts it into a long list of numbers called a vector. These numbers represent coordinates in a high-dimensional map. If two items are “semantically” similar (e.g., a photo of a golden retriever and the text “man’s best friend”), the model places their coordinates very close to each other in this map. Today, these models are the invisible engine behind:Search Engines: Finding results based on what you mean, not just the specific words you typed.Recommendation Systems: Netflix or Spotify suggesting content because its “coordinates” are near things you already like.Enterprise AI: Large companies use them for Retrieval-Augmented Generation (RAG), where an AI assistant “looks up” a company’s internal PDFs to answer an employee’s question accurately.The concept of mapping words to vectors dates back to the 1950s with linguists like John Rupert Firth, but the modern “vector revolution” began in the early 2000s when Yoshua Bengio’s team first used the term “word embeddings”. The real breakthrough for the industry was Word2Vec, released by a team at Google led by Tomas Mikolov in 2013. Today, the market is led by a handful of major players:OpenAI: Known for its widely-used text-embedding-3 series.Google: With the new Gemini and previous Gecko models.Anthropic and Cohere: Providing specialized models for enterprise search and developer workflows.By moving beyond text to a natively multimodal architecture, Google is attempting to create a singular, unified map for the sum of human digital expression—text, images, video, audio, and documents—all residing in the same mathematical neighborhood.Why Gemini Embedding 2 is such a big dealMost leading models are still “text-first.” If you want to search a video library, the AI usually has to transcribe the video into text first, then embed that text. Google’s Gemini Embedding 2 is natively multimodal. As Logan Kilpatrick of Google DeepMind posted on X, the model allows developers to “bring text, images, video, audio, and docs into the same embedding space”.It understands audio as sound waves and video as motion directly, without needing to turn them into text first. This reduces “translation” errors and captures nuances that text alone might miss.For developers and enterprises, the “natively multimodal” nature of Gemini Embedding 2 represents a shift toward more efficient AI pipelines. By mapping all media into a single 3,072-dimensional space, developers no longer need separate systems for image search and text search; they can perform “cross-modal” retrieval—using a text query to find a specific moment in a video or an image that matches a specific sound.And unlike its predecessors, Gemini Embedding 2 can process requests that mix modalities. A developer can send a request containing both an image of a vintage car and the text “What is the engine type?”. The model doesn’t process them separately; it treats them as a single, nuanced concept. This allows for a much deeper understanding of real-world data where the “meaning” is often found in the intersection of what we see and what we say.One of the model’s more technical features is Matryoshka Representation Learning. Named after Russian nesting dolls, this technique allows the model to “nest” the most important information in the first few numbers of the vector.An enterprise can choose to use the full 3072 dimensions for maximum precision, or “truncate” them down to 768 or 1536 dimensions to save on database storage costs with minimal loss in accuracy.Benchmarking the performance gains of moving to multimodalGemini Embedding 2 establishes a new performance ceiling for multimodal depth, specifically outperforming previous industry leaders across text, image, and video evaluation tasks. The model’s most significant lead is found in video and audio retrieval, where its native architecture allows it to bypass the performance degradation typically associated with text-based transcription pipelines. Specifically, in video-to-text and text-to-video retrieval tasks, the model demonstrates a measurable performance gap over existing industry leaders, accurately mapping motion and temporal data into a unified semantic space.The technical results show a distinct advantage in the following standardized categories:Multimodal Retrieval: Gemini Embedding 2 consistently outperforms leading text and vision models in complex retrieval tasks that require understanding the relationship between visual elements and textual queries.Speech and Audio Depth: The model introduces a new standard for native audio embeddings, achieving higher accuracy in capturing phonetic and tonal intent compared to models that rely on intermediate text-transcription.Contextual Scaling: In text-based benchmarks, the model maintains high precision while utilizing its expansive 8,192 token context window, ensuring that long-form documents are embedded with the same semantic density as shorter snippets.Dimension Flexibility: Testing across the Matryoshka Representation Learning (MRL) layers reveals that even when truncated to 768 dimensions, the model retains a significant majority of its 3,072-dimension performance, outperforming fixed-dimension models of similar size.What it means for enterprise databasesFor the modern enterprise, information is often a fragmented mess. A single customer issue might involve a recorded support call (audio), a screenshot of an error (image), a PDF of a contract (document), and a series of emails (text).In previous years, searching across these formats required four different pipelines. With Gemini Embedding 2, an enterprise can create a Unified Knowledge Base. This enables a more advanced form of RAG, wherein a company’s internal AI doesn’t just look up facts, but understands the relationship between them regardless of format.Early partners are already reporting drastic efficiency gains:Sparkonomy, a creator economy platform, reported that the model’s native multimodality slashed their latency by up to 70%. By removing the need for intermediate LLM “inference” (the step where one model explains a video to another), they nearly doubled their semantic similarity scores for matching creators with brands.Everlaw, a legal tech firm, is using the model to navigate the “high-stakes setting” of litigation discovery. In legal cases where millions of records must be parsed, Gemini’s ability to index images and videos alongside text allows legal professionals to find “smoking gun” evidence that traditional text-search would miss.Understanding the limitsIn its announcement, Google was upfront about some of the current limitations of Gemini Embedding 2. The new model can accommodate vectorization of individual files that comprise of as many as 8,192 text tokens, 6 images (in as single batch), 128 seconds of video (2 minutes, 8 seconds long), 80 seconds of native audio (1.34 minutes), and a 6-page PDF.It is vital to clarify that these are input limits per request, not a cap on what the system can remember or store.Think of it like a scanner. If a scanner has a limit of “one page at a time,” it doesn’t mean you can only ever scan one page. it means you have to feed the pages in one by one.Individual File Size: You cannot “embed” a 100-page PDF in a single call. You must “chunk” the document—splitting it into segments of 6 pages or fewer—and send each segment to the model individually.Cumulative Knowledge: Once those chunks are converted into vectors, they can all live together in your database. You can have a database containing ten million 6-page PDFs, and the model will be able to search across all of them simultaneously.Video and Audio: Similarly, if you have a 10-minute video, you would break it into 128-second segments to create a searchable “timeline” of embeddings.Licensing, pricing, and availabilityAs of March 10, 2026, Gemini Embedding 2 is officially in Public Preview. For developers and enterprise leaders, this means the model is accessible for immediate testing and production integration, though it is still subject to the iterative refinements typical of “preview” software before it reaches General Availability (GA).The model is deployed across Google’s two primary AI gateways, each catering to a different scale of operation:Gemini API: Targeted at rapid prototyping and individual developers, this path offers a simplified pricing structure.Vertex AI (Google Cloud): The enterprise-grade environment designed for massive scale, offering advanced security controls and integration with the broader Google Cloud ecosystem.It’s also already integrated with the heavy hitters of AI infrastructure: LangChain, LlamaIndex, Haystack, Weaviate, Qdrant, and ChromaDB.In the Gemini API, Google has introduced a tiered pricing model that distinguishes between “standard” data (text, images, and video) and “native” audio.The Free Tier: Developers can experiment with the model at no cost, though this tier comes with rate limits (typically 60 requests per minute) and uses data to improve Google’s products.The Paid Tier: For production-level volume, the cost is calculated per million tokens. For text, image, and video inputs, the rate is $0.25 per 1 million tokens.The “Audio Premium”: Because the model natively ingests audio data without intermediate transcription—a more computationally intensive task—the rate for audio inputs is doubled to $0.50 per 1 million tokens.For large-scale deployments on Vertex AI, the pricing follows an enterprise-centric “Pay-as-you-go” (PayGo) model. This allows organizations to pay for exactly what they use across different processing modes:Flex PayGo: Best for unpredictable, bursty workloads.Provisioned Throughput: Designed for enterprises that require guaranteed capacity and consistent latency for high-traffic applications.Batch Prediction: Ideal for re-indexing massive historical archives, where time-sensitivity is lower but volume is extremely high.By making the model available through these diverse channels and integrating it natively with libraries like LangChain, LlamaIndex, and Weaviate, Google has ensured that the “switching cost” for businesses isn’t just a matter of price, but of operational ease. Whether a startup is building its first RAG-based assistant or a multinational is unifying decades of disparate media archives, the infrastructure is now live and globally accessible.In addition, the official Gemini API and Vertex AI Colab notebooks, which contain the Python code necessary to implement these features, are licensed under the Apache License, Version 2.0.The Apache 2.0 license is highly regarded in the tech community because it is “permissive.” It allows developers to take Google’s implementation code, modify it, and use it in their own commercial products without having to pay royalties or “open source” their own proprietary code in return.How enterprises should respond: migrate to Gemini 2 Embedding or not?For Chief Data Officers and technical leads, the decision to migrate to Gemini Embedding 2 hinges on the transition from a “text-plus” strategy to a “natively multimodal” one. If your organization currently relies on fragmented pipelines — where images and videos are first transcribed or tagged by separate models before being indexed — the upgrade is likely a strategic necessity. This model eliminates the “translation tax” of using intermediate LLMs to describe visual or auditory data, a move that partners like Sparkonomy found reduced latency by up to 70% while doubling semantic similarity scores. For businesses managing massive, diverse datasets, this isn’t just a performance boost; it is a structural simplification that reduces the number of points where “meaning” can be lost or distorted.The effort to switch from a text-only foundation is lower than one might expect due to what early users describe as excellent “API continuity”. Because the model integrates with industry-standard frameworks like LangChain, LlamaIndex, and Vector Search, it can often be “dropped into” existing workflows with minimal code changes. However, the real cost and energy investment lies in re-indexing. Moving to this model requires re-embedding your existing corpus to ensure all data points exist in the same 3,072-dimensional space. While this is a one-time computational hurdle, it is the prerequisite for unlocking cross-modal search—where a simple text query can suddenly “see” into your video archives or “hear” specific customer sentiment in call recordings.The primary trade-off for data leaders to weigh is the balance between high-fidelity retrieval and long-term storage economics. Gemini Embedding 2 addresses this directly through Matryoshka Representation Learning (MRL), which allows you to truncate vectors from 3072 dimensions down to 768 without a linear drop in quality.This gives CDOs a tactical lever: you can choose maximum precision for high-stakes legal or medical discovery—as seen in Everlaw’s 20% lift in recall—while utilizing smaller, more efficient vectors for lower-priority recommendation engines to keep cloud storage costs in check. Ultimately, the ROI is found in the “lift” of accuracy; in a landscape where an AI’s value is defined by its context, the ability to natively index a 6-page PDF or 128 seconds of video directly into a knowledge base provides a depth of insight that text-only models simply cannot replicate.

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