Will "No Code" Software Development Eliminate Jobs?

Workers always worry that new automation capabilities will eliminate their jobs, and the danger is not imaginary. Robotics, self-driving vehicles, chatbots and algorithmic trading have clear potential to--or a history of--eliminating at least some jobs in many fields. 

But other important changes also tend to happen when any form of automation is applied. 

Almost always, routine tasks are replaced by higher-order tasks. Also, specialist capabilities tend to filter out to the edges of organizations and away from specialist support groups. 

In other words, automation often enables people to accomplish tasks themselves that once were performed by other specialist units within an organization. 

Automation often can take over repetitive, manual, or time-consuming processes, allowing workers to focus on more strategic and creative work. 

Such effects often are overlooked when looking only at job loss. To be sure, self-checkout kiosks in retail or computer-generated graphics clearly allow stores and content producers to reduce the number of humans required to produce a given level of output. 

But tasks also can shift to higher-order activities. In accounting, software automates routine bookkeeping tasks, enabling accountants to focus on financial analysis, forecasting, and advising clients on growth strategies.

Industry	Jobs Eliminated	Jobs Created	Automation Technology
Manufacturing	Assembly line workers, factory floor operators	Robotics engineers, automation technicians, AI/machine learning specialists	Industrial robots, automated guided vehicles (AGVs), 3D printing
Transportation	Truck drivers, taxi drivers	Self-driving vehicle engineers, AI/machine learning specialists for autonomous systems, logistics coordinators	Self-driving cars, drones for delivery, autonomous vehicles
Customer Service	Call center agents, basic customer support roles	Chatbot developers, AI/machine learning specialists for customer service, data analysts	Chatbots, AI-powered customer service platforms, virtual assistants
Healthcare	Some medical technicians, basic lab assistants	Medical imaging specialists, AI/machine learning specialists for disease diagnosis, robotic surgeons	Robotic surgery systems, AI-powered diagnostic tools, telemedicine platforms
Finance	Data entry clerks, some loan officers	Financial analysts, data scientists, cybersecurity experts	Algorithmic trading, automated loan processing, fraud detection systems

Automation tends to widen the number of people able to create output that formerly might have been the province of specialists. Those of you with long memories might remember mainframe computing; content creation before desktop publishing; secretaries; travel agents or bank tellers. Lots of tasks you would formerly have had to ask another department to execute we now do ourselves. 

No-code software development, a way to create software applications without writing any code, is probably going to bring that sort of change.  

Using a “drag and drop” visual approach (instead of typing lines of code), users assemble products using pre-built components such as buttons, forms, and data fields. Some might liken the process to building something with Legos, but the objective is to enable non-coders to create websites, mobile apps, business process functions or databases. 

As always, the hype will be greater than the typical results will validate. Simpler use cases developed by non-coders will work better, but more-complex apps will likely remain the province of specialists. Word processing means we all can “write,” but word processing doesn’t make a poor writer a good writer. 

Domain	Specialist Tool	Automated Tool	Impact
Graphic Design	Photoshop, Illustrator	Canva, Adobe Express	Non-designers can create professional-quality graphics with templates and AI suggestions.
Programming	Coding languages like Python, Java	No-code/low-code platforms (e.g., Bubble, Webflow)	Entrepreneurs and non-developers can build apps or websites without extensive coding skills.
Data Analysis	SPSS, MATLAB	Excel with AI tools, Google Data Studio, Power BI	Business users can perform complex data analysis without deep statistical knowledge.
Video Editing	Adobe Premiere Pro, Final Cut Pro	CapCut, Descript	Automated editing and AI-powered tools allow novices to produce polished videos.
Music Production	DAWs like Pro Tools, Logic Pro	AI music tools (e.g., AIVA, Soundtrap)	Musicians and hobbyists can compose and produce music without formal training.
Healthcare	Medical imaging tools, diagnostic software	AI-powered apps (e.g., skin cancer detection apps)	Patients and general practitioners can leverage diagnostic tools previously limited to specialists.
Photography	DSLRs with manual settings	Smartphone cameras with AI enhancements	Casual users can capture high-quality photos with professional-level adjustments.
Education	Custom curriculum design	AI tutoring tools (e.g., Khan Academy, ChatGPT)	Teachers and students can create personalized learning paths without advanced pedagogical training.
3D Design	CAD software	User-friendly 3D tools (e.g., Tinkercad, SketchUp)	Beginners can design 3D models for printing or visualization with little technical expertise.
Marketing	A/B testing tools, campaign management	AI marketing platforms (e.g., HubSpot, Jasper AI)	Small business owners can execute data-driven campaigns without a marketing team.

“No code” software development might wind up enabling something like word processing or spreadsheet or desktop publishing outcomes. Non-specialists will be able to create outputs that were provided by specialists in the past, at least at a simpler level. Many forms of analysis and problem solving can be pushed forward towards the edge of organizations; closer to customers. 

In principle, innovation should happen faster, as work groups can create faster than when relying on support from other parts of the organization. 

Meta Byte Latent Transformer is Another Way Inference Costs Will Keep Dropping

Large Language Model costs are going to keep dropping. DeepSeek was only one example. Now Meta developers propose to use Byte Latent Transformer (BLT) as a new alternative to using tokens for language models.

Meta has introduced the Byte Latent Transformer (BLT) as a new alternative to using tokens for language models.   

Instead of breaking down text into tokens, BLT processes data directly at the byte level. This allows the model to handle any language or data format without needing predefined vocabularies.   

Some potential benefits include lower inference cost, as noisy or non-standard text (text with typos, mixed languages, or special characters) can be processed more efficiently. 

BLT also is said to dynamically group bytes into "patches," potentially reducing computational effort (and hence cost of inferences). 

BLT's tokenizer-free approach could make it easier to develop models for languages with limited data.

The point is that AI language model costs are going to keep dropping. As that happens, we will see greater usage across a wider range of applications and processes. 

High Stakes for AI, Expected Behaviors by Participants

Amazon will make an estimated $100 billion investment in capital investments in 2025, with a “vast majority” of that going to artificial intelligence. Alphabet has indicated it will invest about $75 billion; Microsoft plans to spend $80 billion while Meta intends to invest $60 billion to $65 billion in 2025. 

Combined, the four companies have reported capex of almost $250 billion in 2024 and forecast this to rise beyond $300 billion this year. All of which will make some observers--who are uncertain about the payback--quite nervous.

Indeed, as Jassy noted on Amazon’s most-recent earnings call,”there aren't that many generative AI applications at large scale yet.” 

The gamble is all a matter of perspective. Skeptics tend to want near-term proof of profit potential. Optimists see a generational opportunity. The former will want to see near-term financial results; the latter believe the opportunity for disrupting computing and business models is so profound they cannot allow the opportunity to be forfeited by inaction or inadequate vigor. 

“AI represents, for sure, the biggest opportunity since cloud and probably the biggest technology shift and opportunity in business since the Internet,” said Amazon CEO Andy Jassy. Who siad AI is a “once-in-a-lifetime” type of opportunity. 

Sundar Pichai, Alphabet CEO, said the AI opportunity was “as big as it comes.” 

Still, the arrival of DeepSeek does offer the promise of lower model costs, Jassy indicated. “I think like many others, we were impressed with what DeepSeek has done.”

And Amazon seemed notably “impressed with some of the training techniques, primarily in flipping the sequencing of reinforcement training, reinforcement learning being earlier and without the human in the loop.” 

As typically is the case for all of computing, allowing machines to work without the constraints of human processing speed tends to speed up progress. 

Jassy also addressed the fear some have that lower model costs (inference and training) will lead to smaller markets for AI products and infrastructure. 

“Sometimes people make the assumptions that if you're able to decrease the cost of any type of technology component, in this case, we're really talking about inference, that somehow it's going to lead to less total spend in technology,” Jassy noted. “And we just, we have never seen that to be the case.”

Indeed, computing economics suggest that vastly-lower prices per compute cycle or storage costs lead to people and businesses creating new use cases that formerly were cost prohibitive, with the result that overall technology spend grows over time, Jassy argued. 

Customers “get excited about what else they could build that they always thought was cost-prohibitive before and they usually end up spending a lot more in total on technology once you make the per unit cost less,” he added. 

“I think that is very much what's going to happen here in AI, which is the cost of inference will substantially come down,” Jassy said. 

So, as often is the case, financial analysts and firm leaders are asking different questions. Financial analysts always are focused on the next quarter. Firm leaders necessarily are focused on the longer term, especially when a disruptive new industry possibly is being born. 

It’s an old story. Entrepreneurs, investors and product developers must accept risk as the price of winning. Lawyers, accountants and financial analysts all want to “de-risk” as much as possible. 

And we are some ways from proving the visionaries or the risk managers correct. 

If the former are correct, then the new equivalents of market leaders in search, social media, e-commerce and entertainment will emerge. And those potential leaders will have to invest heavily now. 

If the latter prove correct we will see possibly-massive investment losses and bankruptcies for many contestants who overspent too early, even if the market does eventually emerge as expected. 

On the other hand, in the computing field we have often seen legacy leaders displaced because they failed to invest in the emerging substitute technologies. 

Early Market Share Leader	Industry	Established Market Leader That Displaced Them
Kodak	Cameras & Film	Sony, Canon
Blockbuster	Video Rental	Netflix
Nokia	Smartphones	Apple, Samsung
MySpace	Social Media	Facebook
Yahoo!	Search Engines	Google

Internet Changed Distribution; Generative AI Changes Content Creation

While digital transformation (internet plus digital content) mostly changed distribution and consumption patterns, generative AI is transforming the actual creation process as well. Where the shift to digital media eliminated geographic barriers and distribution costs, AI might have more impact in automating content curation and creation. 

Where digital media and the internet allowed more people to become content creators, AI could start to displace some of the value of such roles. 

What remains to be seen are the various ways business models and consumer behavior will change. Digital media and the internet meant “regular people” could create and publish content. And while that might not have directly disrupted “legacy media,” is has created social media, “influencers” and user-generated content as platforms for business models. 

Functions	Digital/Internet Disruption	AI Disruption
Content Creation	More content creation tools	Autonomous content generation with minimal human input
Distribution	Eliminated physical distribution constraints; enabled direct-to-consumer publishing	Could integrate creation and distribution (personalized content on demand)
Business Models	Shifted from ownership to renting; reduced unit costs; created ad-supported models	May enable "personal content engines" with ad or subscription revenue models
Professional Impact	Disrupted traditional gatekeepers (publishers, labels); created new creator economy	May automate certain creative roles
Quality Control	Variable quality	Similar challenges
Consumer Behavior	Shifted to on-demand consumption; increased content variety; shorter attention spans	Even more personalization
Creative Process	Digitized workflow; enabled remote collaboration	Could fundamentally change human-machine creative collaboration; may automate routine creative tasks
Economic Impact	Redistributed revenue from traditional to digital players	May further reduce content production costs
Market Structure	Led to new platforms	Could happen again

LLMs are Used in Ways Different from Traditional Search

Users of generative artificial intelligence chatbots seem to be looking for different types of information than they do when using traditional search, according to an analysis by Semrush.

 

Traditional search engines such as Google typically serve four types of intent: 

• Navigational (finding specific sites)

• Informational (learning about topics)

• Commercial (researching products) 

• Transactional (making purchases). 

But only 30 percent of ChatGPT prompts analyzed by bSemrush could be identified as one of these traditional categories. The other 70 percent of queries were rarely seen in standard search engine requests. 

source: Semrush

“Many ChatGPT queries represent entirely new types of intent, potentially related to problem-solving, brainstorming, or exploratory inquiries,m” says Semrush. 

Large language models seem to be used to create images, develop plans, summarize text, ask for advice, get writing help or brainstorm. As befits a tool designed to create content, that seems to be what many users are doing.