AI創作，一門價值萬億美元的大生意？

虎嗅注：最近一段時間，AI作畫的話題在整個人工智慧圈子很是熱門。當然，作畫只是其中一方面，人工智慧在生成內容這一塊還有很多的應用場景，除了作畫，還可以生成文字、生成音影片等。不久前，紅杉美國特地將這個話題拎出來討論了一番，非常有趣的是，這篇文章除了Sonya Huang和Pat Grady兩位署名作者外，還有一個作者是GPT-3。本文前半部分是紅杉的英文原文，後半部分為中文編譯，希望對你有所啟發。

本文來自： 紅杉 ，編譯： 深思圈 ，原文標題：Generative AI: A Creative New World，題圖來自：《愛，死亡和機器人》

Humans are good at analyzing things. Machines are even better. Machines can analyze a set of data and find patterns in it for a multitude of use cases, whether it’s fraud or spam detection, forecasting the ETA of your delivery or predicting which TikTok video to show you next. They are getting smarter at these tasks. This is called “Analytical AI,” or traditional AI.

But humans are not only good at analyzing things—we are also good at creating. We write poetry, design products, make games and crank out code. Up until recently, machines had no chance of competing with humans at creative work—they were relegated to analysis and rote cognitive labor. But machines are just starting to get good at creating sensical and beautiful things. This new category is called “Generative AI,” meaning the machine is generating something new rather than analyzing something that already exists.

Generative AI is well on the way to becoming not just faster and cheaper, but better in some cases than what humans create by hand. Every industry that requires humans to create original work—from social media to gaming, advertising to architecture, coding to graphic design, product design to law, marketing to sales—is up for reinvention. Certain functions may be completely replaced by generative AI, while others are more likely to thrive from a tight iterative creative cycle between human and machine—but generative AI should unlock better, faster and cheaper creation across a wide range of end markets. The dream is that generative AI brings the marginal cost of creation and knowledge work down towards zero, generating vast labor productivity and economic value—and commensurate market cap.

The fields that generative AI addresses—knowledge work and creative work—comprise billions of workers. Generative AI can make these workers at least 10% more efficient and/or creative: they become not only faster and more efficient, but more capable than before. Therefore, Generative AI has the potential to generate trillions of dollars of economic value.

Why Now?

Generative AI has the same “why now” as AI more broadly: better models, more data, more compute. The category is changing faster than we can capture, but it’s worth recounting recent history in broad strokes to put the current moment in context.

Wave 1: Small models reign supreme (Pre-2015) 5+ years ago , small models are considered “state of the art” for understanding language. These small models excel at analytical tasks and become deployed for jobs from delivery time prediction to fraud classification. However, they are not expressive enough for general-purpose generative tasks. Generating human-level writing or code remains a pipe dream. 

Wave 2: The race to scale (2015-Today) A landmark paper by Google Research (Attention is All You Need) describes a new neural network architecture for natural language understanding called transformers that can generate superior quality language models while being more parallelizable and requiring significantly less time to train. These models are few-shot learners and can be customized to specific domains relatively easily.

AS AI MODELS HAVE GOTTEN PROGRESSIVELY LARGER THEY HAVE BEGUN TO SURPASS MAJOR HUMAN PERFORMANCE BENCHMARKS. SOURCES: © THE ECONOMIST NEWSPAPER LIMITED, LONDON, JUNE 11TH 2022. ALL RIGHTS RESERVED; SCIENCE.ORG/CONTENT/ARTICLE/COMPUTERS-ACE-IQ-TESTS-STILL-MAKE-DUMB-MISTAKES-CAN-DIFFERENT-TESTS-HELP

Sure enough, as the models get bigger and bigger, they begin to deliver human-level, and then superhuman results. Between 2015 and 2020, the compute used to train these models increases by 6 orders of magnitude and their results surpass human performance benchmarks in handwriting, speech and image recognition, reading comprehension and language understanding. OpenAI’s GPT-3 stands out: the model’s performance is a giant leap over GPT-2 and delivers tantalizing Twitter demos on tasks from code generation to snarky joke writing.

Despite all the fundamental research progress, these models are not widespread. They are large and difficult to run (requiring GPU orchestration) , not broadly accessible (unavailable or closed beta only) , and expensive to use as a cloud service. Despite these limitations, the earliest Generative AI applications begin to enter the fray.  

Wave 3: Better, faster, cheaper (2022+) Compute gets cheaper. New techniques, like diffusion models, shrink down the costs required to train and run inference. The research community continues to develop better algorithms and larger models. Developer access expands from closed beta to open beta, or in some cases, open source.

For developers who had been starved of access to LLMs, the floodgates are now open for exploration and application development. Applications begin to bloom.

ILLUSTRATION GENERATED WITH MIDJOURNEY

Wave 4: Killer apps emerge (Now) With the platform layer solidifying, models continuing to get better/faster/cheaper, and model access trending to free and open source, the application layer is ripe for an explosion of creativity.

Just as mobile unleashed new types of applications through new capabilities like GPS, cameras and on-the-go connectivity, we expect these large models to motivate a new wave of generative AI applications. And just as the inflection point of mobile created a market opening for a handful of killer apps a decade ago, we expect killer apps to emerge for Generative AI. The race is on.

Market Landscape

Below is a schematic that describes the platform layer that will power each category and the potential types of applications that will be built on top.

Models

Textis the most advanced domain. However, natural language is hard to get right, and quality matters. Today, the models are decently good at generic short/medium-form writing (but even so, they are typically used for iteration or first drafts). Over time, as the models get better, we should expect to see higher quality outputs, longer-form content, and better vertical-specific tuning.

Code generationis likely to have a big impact on developer productivity in the near term as shown by GitHub CoPilot. It will also make the creative use of code more accessible to non developers.

Imagesare a more recent phenomenon, but they have gone viral: it’s much more fun to share generated images on Twitter than text! We are seeing the advent of image models with different aesthetic styles, and different techniques for editing and modifying generated images.

Speechsynthesis has been around for a while (hello Siri!) but consumer and enterprise applications are just getting good. For high-end applications like film and podcasts the bar is quite high for one-shot human quality speech that doesn’t sound mechanical. But just like with images, today’s models provide a starting point for further refinement or final output for utilitarian applications.

Video and 3D models are further behind.People are excited about these models’ potential to unlock large creative markets like cinema, gaming, VR, architecture and physical product design. We should expect to see foundational 3D and video models in the next 1-2 years. 

Other domains: There is fundamental model R&D happening in many fields, from audio and music to biology and chemistry (generative proteins and molecules, anyone?).  

The below chart illustrates a timeline for how we might expect to see fundamental models progress and the associated applications that become possible. 2025 and beyond is just a guess.

Applications

Here are some of the applications we are excited about. There are far more than we have captured on this page, and we are enthralled by the creative applications that founders and developers are dreaming up.

Copywriting: The growing need for personalized web and email content to fuel sales and marketing strategies as well as customer support are perfect applications for language models. The short form and stylized nature of the verbiage combined with the time and cost pressures on these teams should drive demand for automated and augmented solutions.

Vertical specific writing assistants: Most writing assistants today are horizontal; we believe there is an opportunity to build much better generative applications for specific end markets, from legal contract writing to screenwriting. Product differentiation here is in the fine-tuning of the models and UX patterns for particular workflows. 

Code generation:Current applications turbocharge developers and make them much more productive: GitHub Copilot is now generating nearly 40% of code in the projects where it is installed. But the even bigger opportunity may be opening up access to coding for consumers. Learning to prompt may become the ultimate high-level programming language.

Art generation: The entire world of art history and pop cultures is now encoded in these large models, allowing anyone to explore themes and styles at will that previously would have taken a lifetime to master.

Gaming: The dream is using natural language to create complex scenes or models that are riggable; that end state is probably a long way off, but there are more immediate options that are more actionable in the near term such as generating textures and skybox art.  

Media/Advertising: Imagine the potential to automate agency work and optimize ad copy and creative on the fly for consumers. Great opportunities here for multi-modal generation that pairs sell messages with complementary visuals.

Design:Prototyping digital and physical products is a labor-intensive and iterative process. High-fidelity renderings from rough sketches and prompts are already a reality. As 3-D models become available the generative design process will extend up through manufacturing and production—text to object. Your next iPhone app or sneakers may be designed by a machine.

Social media and digital communities:Are there new ways of expressing ourselves using generative tools? New applications like Midjourney are creating new social experiences as consumers learn to create in public.

ILLUSTRATION GENERATED WITH MIDJOURNEY

Anatomy of a Generative AI Application

What will a generative AI application look like? Here are some predictions.

Intelligence and model fine-tuning 

Generative AI apps are built on top of large models like GPT-3 or Stable Diffusion. As these applications get more user data, they can fine-tune their models to: 1) improve model quality/performance for their specific problem space and; 2) decrease model size/costs.  

We can think of Generative AI apps as a UI layer and “little brain” that sits on top of the “big brain” that is the large general-purpose models.

Form Factor 

Today, Generative AI apps largely exist as plugins in existing software ecosystems. Code completions happen in your IDE; image generations happen in Figma or Photoshop; even Discord bots are the vessel to inject generative AI into digital/social communities.  

There are also a smaller number of standalone Generative AI web apps, such as Jasper and Copy.ai for copywriting, Runway for video editing, and Mem for note taking.

A plugin may be an effective wedge into bootstrapping your own application, and it may be a savvy way to surmount the chicken-and-egg problem of user data and model quality (you need distribution to get enough usage to improve your models; you need good models to attract users). We have seen this distribution strategy pay off in other market categories, like consumer/social.

Paradigm of Interaction 

Today, most Generative AI demos are “one-and-done”: you offer an input, the machine spits out an output, and you can keep it or throw it away and try again. Increasingly, the models are becoming more iterative, where you can work with the outputs to modify, finesse, uplevel and generate variations.  

Today, Generative AI outputs are being used as prototypes or first drafts. Applications are great at spitting out multiple different ideas to get the creative process going (e.g. different options for a logo or architectural design) , and they are great at suggesting first drafts that need to be finessed by a user to reach the final state (e.g. blog posts or code autocompletions) . As the models get smarter, partially off the back of user data, we should expect these drafts to get better and better and better, until they are good enough to use as the final product.  

Sustained Category Leadership

The best Generative AI companies can generate a sustainable competitive advantage by executing relentlessly on the flywheel between user engagement/data and model performance. 

To win, teams have to get this flywheel going by 1) having exceptional user engagement → 2) turning more user engagement into better model performance (prompt improvements, model fine-tuning, user choices as labeled training data) → 3) using great model performance to drive more user growth and engagement. 

They will likely go into specific problem spaces (e.g., code, design, gaming) rather than trying to be everything to everyone. They will likely first integrate deeply into applications for leverage and distribution and later attempt to replace the incumbent applications with AI-native workflows. It will take time to build these applications the right way to accumulate users and data, but we believe the best ones will be durable and have a chance to become massive. 

Hurdles and Risks

Despite Generative AI’s potential, there are plenty of kinks around business models and technology to iron out. Questions over important issues like copyright, trust & safety and costs are far from resolved.

Eyes Wide Open

Generative AI is still very early. The platform layer is just getting good, and the application space has barely gotten going.

To be clear, we don’t need large language models to write a Tolstoy novel to make good use of Generative AI. These models are good enough today to write first drafts of blog posts and generate prototypes of logos and product interfaces. There is a wealth of value creation that will happen in the near-to-medium-term.

This first wave of Generative AI applications resembles the mobile application landscape when the iPhone first came out—somewhat gimmicky and thin, with unclear competitive differentiation and business models. However, some of these applications provide an interesting glimpse into what the future may hold. Once you see a machine produce complex functioning code or brilliant images, it’s hard to imagine a future where machines don’t play a fundamental role in how we work and create.

If we allow ourselves to dream multiple decades out, then it’s easy to imagine a future where Generative AI is deeply embedded in how we work, create and play: memos that write themselves; 3D print anything you can imagine; go from text to Pixar film; Roblox-like gaming experiences that generate rich worlds as quickly as we can dream them up.

While these experiences may seem like science fiction today, the rate of progress is incredibly high—we have gone from narrow language models to code auto-complete in several years—and if we continue along this rate of change and follow a “Large Model Moore’s Law,” then these far-fetched scenarios may just enter the realm of the possible.

PS: This piece was co-written with GPT-3. GPT-3 did not spit out the entire article, but it was responsible for combating writer’s block, generating entire sentences and paragraphs of text, and brainstorming different use cases for generative AI. Writing this piece with GPT-3 was a nice taste of the human-computer co-creation interactions that may form the new normal. We also generated illustrations for this post with Midjourney, which was SO MUCH FUN!

以下為中文版：

人類擅長分析事物，而機器在這方面甚至做得就更好了。機器可以分析一組資料，並在其中找到許多用例 （use case） 的模式，無論是欺詐還是垃圾郵件檢測，預測你的發貨時間或預測該給你看哪個TikTok影片，它們在這些任務中變得越來越聰明。這被稱為“分析型AI （Analytical AI） ”，或傳統AI。

但是人類不僅擅長分析事物，我們也擅長創造。我們寫詩，設計產品，製作遊戲，編寫程式碼。直到最近，機器還沒有機會在創造性工作上與人類競爭——它們被降格為只做分析和機械性的認知工作。但最近，機器開始嘗試創造有意義和美麗的東西，這個新類別被稱為“生成式AI （Generative AI） ”，這意味著機器正在生成新的東西，而不是分析已經存在的東西。

生成式AI正在變得不僅更快、更便宜，而且在某些情況下比人類創造的更好。從社交媒體到遊戲，從廣告到建築，從程式設計到平面設計，從產品設計到法律，從市場營銷到銷售，每一個原來需要人類創作的行業都等待著被機器重新創造。某些功能可能完全被生成式AI取代，而其他功能則更有可能在人與機器之間緊密迭代的創作週期中蓬勃發展。但生成式AI應該在廣泛的終端市場上解鎖更好、更快、更便宜的創作。 人們期待的夢想是：生成式AI將創造和知識工作的邊際成本降至零，產生巨大的勞動生產率和經濟價值，以及相應的市值。

生成式AI可以處理的領域包括了知識工作和創造性工作，而這涉及到數十億的人工勞動力。生成式AI可以使這些人工的效率和創造力至少提高10%，它們不僅變得更快和更高效，而且比以前更有能力。因此，生成式AI有潛力產生數萬億美元的經濟價值。

為什麼是現在？

生成式AI與更廣泛的AI有著相同的“為什麼是現在 （Why now） ”的原因：更好的模型，更多的資料，更多的算力。這個類別的變化速度比我們所能捕捉到的要快，但我們有必要在大背景下回顧一下最近的歷史。

第1波浪潮：小模型 （small models） 占主導地位 （2015年前） ，小模型在理解語言方面被認為是“最先進的”。這些小模型擅長於分析任務，可以用於從交貨時間預測到欺詐分類等工作。但是，對於通用生成任務，它們的表達能力不夠。生成人類級別的寫作或程式碼仍然是一個白日夢。

第2波浪潮：規模競賽 （2015年-至今） ，Google Research的一篇里程碑式的論文 （Attention is All You Need http://arxiv.org/abs/1706.03762） 描述了一種用於自然語言理解的新的神經網路架構，稱為transformer，它可以生成高質量的語言模型，同時具有更強的並行性，需要的訓練時間更少。這些模型是簡單的學習者，可以相對容易地針對特定領域進行定製。

果不其然，隨著模型越來越大，它們開始可以輸出達到人類水平的結果，然後是超人的結果。從2015年到2020年，用於訓練這些模型的計算量增加了6個數量級，其結果在書寫、語音、影象識別、閱讀和語言理解方面超過了人類的表現水平。OpenAI的GPT-3表現尤其突出：該模型的效能比GPT-2有了巨大的飛躍，並且從程式碼生成到笑話編寫的任務中都提供了出色的Twitter demo來證明。

儘管所有的基礎研究都取得了進展，但這些模型並不普遍。它們龐大且難以執行 (需要特別的GPU配置) ，不能被更多人廣泛觸達使用 (不可用或只進行封閉測試) ，而且作為雲服務使用成本昂貴。儘管存在這些限制，最早的生成式AI應用程式也已經開始進入競爭。

第3波浪潮：更好、更快和更便宜 （2022+） ，算力變得更便宜，新技術，如擴散模型 （diffusion models） ，降低了訓練和執行所需的成本。研究人員繼續開發更好的演算法和更大的模型。開發人員的訪問許可權從封閉測試擴充套件到開放測試，或者在某些情況下擴充套件到開源。

對於那些渴望接觸LLMs （Large Language Model 大語言模型） 的開發人員來說，探索和應用開發的閘門現在已經開啟，應用開始大量湧現。

第4波浪潮：殺手級應用出現 （現在） ，隨著平臺層的穩固，模型繼續變得更好、更快和更便宜，模型的獲取趨於免費和開源，應用層的創造力已經成熟。

正如移動裝置通過GPS、攝像頭和網路連線等新功能釋放了新型別的應用程式一樣，我們預計這些大型模型將激發生成式AI應用程式的新浪潮。就像十年前移動網際網路的拐點被一些殺手級應用打開了市場一樣，我們預計生成式AI的殺手級應用程式也會出現，比賽開始了。

市場格局

下面是一個示意圖，說明了為每個類別提供動力的平臺層，以及將在其上構建的潛在應用程式型別。

模型

文字 （Text） 是最先進的領域，然而，自然語言很難被正確使用並且質量很重要。如今，這些模型在一般的中短篇形式的寫作中相當出色(但即便如此，它們通常用於迭代或初稿)。隨著時間的推移，模型變得越來越好，我們應該期望看到更高質量的輸出、更長形式的內容和更好的垂直領域深度。

程式碼生成 （Code generation） 可能會在短期內對開發人員的生產力產生很大的影響，正如GitHub CoPilot所表現的那樣。此外，程式碼生成還將使非開發人員更容易創造性地使用程式碼。

圖片 （Images） 是最近才出現的現象，但它們已經像病毒一樣傳播開來。在Twitter上分享生成的圖片比文字有趣得多！我們正在看到具有不同美學風格的影象模型和用於編輯和修改生成影象的不同技術在陸續出現。

語音合成 （Speech synthesis） 已經出現一段時間了，但消費者和企業應用才剛剛起步。對於像電影和播客這樣的高階應用程式來說，聽起來不機械的，具有人類質量的語音是相當高的門檻。但就像影象一樣，今天的模型為進一步優化或實現應用的最終輸出提供了一個起點。

影片和3D模型則遠遠落後，人們對這些模型的潛力感到興奮，因為它們可以開啟電影、遊戲、虛擬現實、建築和實物產品設計等大型創意市場。我們應該期待在未來1-2年內看到基礎的3D和影片模型的出現。

還有很多其他領域，比如從音訊和音樂到生物和化學等等，都在進行基礎模型的研發。下面這張圖是基本模型進展和相關應用程式成為可能的時間表，其中2025年及以後的部分只是一個猜測。

應用程式

以下是一些讓我們感到興奮的應用，這僅僅只是一部分，實際上的應用要比我們所捕捉到的多得多，我們被創始人和開發人員所夢想的創造性應用程式所吸引。

文案 （Copywriting） ：越來越多的人需要個性化的網頁和電子郵件內容來推動銷售和營銷策略以及客戶支援，這是語言模型的完美應用。這些文案往往形式簡單，並且都有固定的模版，加上這些團隊的時間和成本壓力，應該會大大推動對自動化和增強解決方案的需求。

垂直行業的寫作助手 （Vertical specific writing assistants） ：現在大多數寫作助手都是通用型的，我們相信為特定的終端市場構建更好的生成式應用程式有著巨大機會，比如從法律合同編寫到劇本編寫。這裡的產品差異化體現在針對特定工作流的模型和UX互動的微調。

程式碼生成 （Code generation） ：當前的應用程式推動了開發人員的發展，使他們的工作效率大大提高。在安裝了Copilot的專案中，它生成了近40%的程式碼。但更大的機會可能是為C端消費者賦能程式設計開發能力，學習提示 （learning to prompt） 可能會成為最終的高階程式語言。

藝術生成 （Art generation） ：整個藝術史和流行文化的世界現在都被編碼進了這些大型模型中，這將允許任何人隨意探索在以前可能需要花人一輩子的時間才能掌握的主題和風格。

遊戲 （Gaming） ：在這方面的夢想是使用自然語言建立複雜的場景或可操縱的模型，這個最終狀態可能還有很長一段路要走，但在短期內有更直接的選擇，如生成紋理和天空盒藝術 （skybox art） 。

媒體/廣告 （Media/Advertising） ： 想象一下自動化代理工作的潛力，為消費者實時優化廣告文案和創意。多模態生成的絕佳機會是將銷售資訊與互補的視覺效果結合起來。

設計 （Design） ：設計數字和實物產品的原型是一個勞動密集型的迭代過程，AI根據粗略的草圖和提示來製作高保真的效果圖已經成為現實。隨著3D模型的出現，生成設計的過程將從製造和生產延伸到實物，你的下一個iPhone APP或運動鞋可能是由機器設計的。

社交媒體和數字社群 （Social media and digital communities） ：是否存在使用生成工具表達自我的新方式？隨著Midjourney等新應用學會了像人類一樣在社交網路上創作，這將創造新的社交體驗。

生成式AI應用的解析

生成式AI應用程式會是什麼樣子？以下是一些預測：

智慧和模型微調

生成式AI應用是建立在GPT-3或Stable Diffusion等大型模型之上的，隨著這些應用獲得更多的使用者資料，它們可以對模型進行微調，一方面針對特定的問題空間改進模型質量和效能，另外一方面減少模型的大小和成本。

我們可以把生成式AI應用看作一個UI層和位於大型通用模型“大大腦 （big brain） ”之上的“小大腦 （little brain） ”。

形成的因素

如今，生成式AI應用在很大程度上以外掛的形式存在於現有的軟體生態系統中。比如程式碼生成在你的IDE中，影象生成在Figma或Photoshop中，甚至Discord機器人也是將生成AI放在數字社交社群裡的工具。

還有少量獨立的生成式AI Web應用，如在文案方面有Jasper和Copy.ai，在影片剪輯方面有Runway，在做筆記方面有Mem。

外掛的形式可能是生成式AI應用在早期比較好的切入點，它可以克服使用者資料和模型質量方面面臨的“先有雞還是先有蛋”的問題 (這裡具體指的是：一方面需要分發來獲得足夠多的使用資料，從而來改進模型，另外一方面又需要好的模型來吸引使用者) 。我們已經看到這種策略在其他市場類別中取得了成功，如消費者和社交市場。

互動正規化

如今，大多數生成式AI演示都是“一次性”的：你提供一個輸入，機器吐出一個輸出，你可以保留它或扔掉它，然後再試一次。未來，模型將會支援迭代，你可以使用輸出來修改、調整、升級和生成變化。

如今，生成式AI輸出被用作原型或初稿。應用程式非常擅長丟擲多個不同的想法，以使創作過程繼續 (比如一個logo或建築設計的不同選項) ，它們也非常擅長給出初稿，但需要使用者最終潤色來定稿(比如部落格帖子或程式碼自動完成)。隨著模型變得越來越智慧，同時部分藉助於使用者資料，我們應該期待這些草稿會變得越來越好，直到它們足夠好，可以用作最終產品。

持續的行業領導力

最好的生成式AI公司可以通過在使用者粘性、資料和模型效能之間形成的飛輪來產生可持續的競爭優勢。為了取得勝利，團隊必須通過以下方法來實現這個飛輪:

擁有出色的使用者粘性→將更多的使用者粘性轉化為更好的模型效能 (及時改進、模型微調、把使用者選擇作為標記訓練資料) →使用出色的模型效能來推動更多的使用者增長和留存。

他們可能會專注於特定的領域 (如程式碼、設計和遊戲) ，而不是試圖解決所有人的問題。他們可能首先將深度整合到現有的應用程式中，以便在此基礎上利用和分發自己的程式，然後嘗試用AI原生工作流替換現有的應用程式。用正確的方式構建這些應用來積累使用者和資料是需要時間的，但我們相信最好的應用將會是持久的，並有機會變得龐大。

困難和風險

儘管生成式AI具有巨大的潛力，但在商業模式和技術方面仍有許多問題需要解決。比如版權、信任、安全和成本等重要問題還亟待解決。

放開視野

生成式AI仍然非常早期。平臺層剛剛有起色，而應用層領域才剛剛起步。

需要明確的是，我們不需要利用大型語言模型的生成式AI來編寫托爾斯泰小說。這些模型現在已經足夠好了，可以用來寫部落格文章的初稿，以及生成logo和產品介面的原型，這在中短期內將會創造大量的價值。

生成式AI應用的第一波浪潮，類似於iPhone剛出現時的移動應用場景——有些噱頭但比較單薄，競爭差異化和商業模式不明確。然而，其中一些應用程式提供了一個有趣的視角，讓我們可以一窺未來可能會發生什麼。一旦你看到了機器可以產生複雜的功能程式碼或精彩的圖片，你就很難想象未來機器在我們的工作和創造中不再發揮作用。

如果我們允許自己夢想幾十年後，那麼很容易想象一個未來，生成式AI將深深融入我們的工作、創作和娛樂方式：備忘錄可以自己寫，3D列印任何你能想象的東西，從文字到皮克斯電影，像Roblox類似的遊戲體驗來快速創造出豐富的世界。

雖然這些在今天看起來像是科幻小說，但科技進步的速度是驚人的。從微小 （narrow） 的語言模型到程式碼自動生成只用了幾年時間，如果我們繼續沿著這個變化的速度，並遵循“大模型摩爾定律 （Large Model Moore's Law） ”，那麼這些遙不可及的場景就會變得觸手可及。

本文來自： 紅杉 ，編譯： 深思圈