Zero Trust Security: Why “Never Trust, Always Verify” Is Winning the Cybersecurity WarCybersecurity
Zero Trust Security: Why “Never Trust, Always Verify” Is Winning the Cybersecurity War
NeuralWired Staff·May 24, 2026·12 min read
$40B+Global ZT market size in 2025
30%Organizations that have actually implemented ZT
$1.76MAverage breach cost saved with mature ZT (IBM 2024)
In 2020, hackers slipped into SolarWinds’ build pipeline and pushed poisoned software updates to 18,000 organizations, including the U.S. Treasury, Homeland Security, and the Pentagon. They moved through networks undetected for months. The perimeter had held. The castle walls were intact. The attackers were already inside, trusted by every system they touched.
That’s the problem zero trust security was designed to solve. And after two decades of being dismissed as too complex, too expensive, or too theoretical, it has become the dominant cybersecurity framework for enterprises, governments, and anyone who can’t afford to assume the person inside the network is actually who they say they are.
The zero trust security market hit $40.01 billion in 2025. It’s projected to reach $182.59 billion by 2035. Every major federal agency in the United States is under a legal mandate to adopt it. Yet only 30% of organizations have actually done it. That gap, between the promise and the practice, is the real story.
What Zero Trust Security Actually Means
Zero trust is not a product. It’s not software you buy. It’s a philosophy, and that distinction matters enormously, because hundreds of vendors are selling “zero trust solutions” while the framework’s own creator is calling them out on it.
“Zero Trust is first and foremost a strategy. It’s something that you do, not something you buy.”
— John Kindervag, Chief Evangelist, Illumio; Creator of the Zero Trust model; speaking at RSAC 2025. Source
Kindervag created zero trust around 2009–2010 while a VP and Principal Analyst at Forrester Research. His foundational paper proposed a framework in which companies abandon the assumption that any device or user, inside or outside the corporate network, can be trusted by default. The phrase he coined: never trust, always verify.
The authoritative technical definition comes from NIST (Special Publication 800-207, published August 2020): zero trust “provides a collection of concepts and ideas designed to minimize uncertainty in enforcing accurate, least privilege per-request access decisions in information systems and services in the face of a network viewed as compromised.”
In plain English: assume the network is already breached. Verify every user, every device, every access request, every time. Grant only the minimum access required for that specific task. And continuously monitor, because a device that was clean at 9 a.m. might be compromised by 11 a.m.
The Core Shift
Traditional security asks: Are you inside the network? If yes, you’re trusted. Zero trust asks: Who are you, what device are you on, what do you need, and does this request make sense right now?, every single time.
How It Works: The Five Pillars
CISA’s Zero Trust Maturity Model organizes the architecture across five pillars. If you’re building or assessing a zero trust program, this is your map.
The average enterprise uses 130 SaaS apps. Each is a potential attack vector.
Data
Data classification, DLP, encryption at rest and in transit
Ultimately, data is what attackers want. This pillar protects the final target.
Each pillar progresses through maturity stages, Traditional, Initial, Advanced, and Optimal. Cross-cutting capabilities including visibility, analytics, automation, and orchestration apply across all five. The point isn’t to buy a tool for each pillar. It’s to map your existing security investments to this framework and identify what’s genuinely missing.
The VPN vs. ZTNA Distinction
The most misunderstood comparison in enterprise security: a VPN and Zero Trust Network Access (ZTNA) are not the same thing. A VPN grants broad network access once a user authenticates, you’re in, and you can reach most of what’s on the network. ZTNA grants access only to specific resources, verified continuously for every session. It’s the difference between handing someone a master key and escorting them directly to the one room they need. Gartner predicted that by 2025, 60% of companies would replace VPNs with ZTNA solutions, and that transition is still very much underway.
Why Zero Trust Is Winning Now
Three forces converged to make zero trust urgent rather than optional.
The Perimeter Collapsed
The traditional “castle and moat” security model assumed that everything inside the corporate network could be trusted. That assumption died slowly, then all at once. SolarWinds (2020), Colonial Pipeline (2021), and the MOVEit breach (2023) each involved extensive lateral movement that perimeter defenses couldn’t detect. The attackers weren’t breaking through the walls, they were walking through the gate with stolen credentials.
Remote Work Killed the Network Edge
When 2020 sent millions of employees home overnight, it didn’t just complicate security, it obliterated the physical boundary the perimeter model depended on. Workers logging in from home networks, personal devices, coffee shops, and foreign countries made the “inside vs. outside” distinction meaningless. Zero trust, which had been growing steadily, became unavoidable.
The U.S. Government Made It Mandatory
In May 2021, President Biden’s Executive Order 14028 formally required federal civilian agencies to develop plans for Zero Trust Architecture. The OMB memorandum M-22-09 (January 2022) went further, requiring all federal agencies to meet specific ZT objectives by the end of FY 2024. When the U.S. government mandates a cybersecurity framework across every civilian agency, the private sector follows, not because it has to, but because the vendor ecosystem, talent pool, and enterprise procurement processes all orient toward it.
A CISA progress report published January 2025 assessed federal agency implementation through FY 2024. It was candid about failures and outlined next steps, which is itself a signal that the mandate has teeth, even if delivery is uneven.
The Implementation Gap: 72% Planning, 30% Doing
Here’s the single most important number in zero trust right now: according to Forrester, 72% of security decision-makers at large organizations plan to pursue zero trust or are already doing so. According to CyberRisk Alliance’s 2024 survey, only 30% of organizations have actually implemented zero trust practices.
That’s a 42-point execution gap. And it has a name: the implementation problem.
“Anything that helps me get visibility and reduces risk is a win, but Zero Trust has to start with a mindset and a strategy aligned to business outcomes.”
— Jared Nussbaum, CISO, Ares Management; speaking at RSAC 2025. Source
What’s stopping organizations? The data from a StrongDM survey of 600 U.S.-based cybersecurity workers is blunt: 48% cite cost and resource constraints as their primary barrier. Another 22% report internal resistance. The obstacles aren’t technical, they’re organizational and financial.
Gartner’s estimate cuts even deeper: by the end of 2026, only 10% of large enterprises will have a mature and measurable zero trust program, up from less than 1% in 2023. Even among organizations that have started, most are mid-journey. Approximately 52% of organizations have completed full ZTNA deployment; 38% remain in partial implementation phases.
The ROI Case CISOs Should Be Making to Their Boards
The IBM Cost of a Data Breach Report 2024 found that the average breach costs $4.88 million, a record high, up 10% from 2023. Organizations with mature zero trust deployments save an average of $1.76 million per breach compared to those without. A mid-market zero trust program can pay for itself from a single avoided breach.
For CISOs navigating this, the practical guidance is consistent: don’t buy new platforms before mapping existing investments. If you have MFA, EDR, and IAM tools already deployed, map them to the five pillars first. Identity is almost always where the highest-ROI work begins, because it’s where most breaches start.
The Hard Truth: What Zero Trust Can’t Do
No serious coverage of zero trust is complete without this part. Three categories of criticism deserve attention from anyone making real decisions about it.
The Vendor Exploitation Problem
The 2023 Okta breach is the cautionary tale. A threat actor accessed a stolen credential from the identity and access management firm, a company whose entire value proposition is verifying identity, and used it to access customer systems across Okta’s client base. As Jason Steer, CISO of Recorded Future, noted in the aftermath:
“A lot of organizations are now all in on companies like Okta, who offer zero trust, and that means threat actors understand that as well.”
— Jason Steer, CISO, Recorded Future. Infosecurity Magazine, March 2026
Steer’s point is precise: zero trust can consolidate organizational risk into single-vendor dependencies. The identity pillar, when it relies on one provider, becomes a single point of failure with a much larger blast radius than the perimeter it replaced.
Kindervag himself has addressed the product misconception directly: “Any business or vendor that claims to have a zero trust product is either lying or doesn’t understand the concept at all.”
MFA Is Not Impenetrable
Identity is zero trust’s highest-ROI pillar and its most exploited weakness simultaneously. Attackers have developed reliable techniques to circumvent MFA: man-in-the-middle attacks that intercept one-time codes, SIM swapping to take over a user’s phone number, and push notification fatigue attacks that bombard users with authentication requests until they approve one out of frustration. Zero trust doesn’t prevent these. It raises the cost of exploitation, it doesn’t eliminate it.
The Academic Challenge: Is True Zero Trust Even Achievable?
This one is uncomfortable, and it mostly hasn’t penetrated vendor marketing materials or government mandates. Professor Virgil D. Gligor of Carnegie Mellon University, a 2019 inductee into the National Cyber Security Hall of Fame and recipient of NIST’s National Information Systems Security Award, published a formal technical challenge to zero trust’s theoretical foundations.
His argument: enterprise networks rely on “black box” devices whose security properties cannot be proven unconditionally. Because of this, the name “zero trust” is technically incoherent. What practitioners are building is trust minimization, which is valuable, but different. As Gligor concluded in his CMU CyLab Technical Report (22-002): “Zero trust is impossible in any enterprise network and has meaning only as an unreachable limit of trust establishment.”
What This Means Practically
Gligor’s argument isn’t that zero trust programs are worthless, it’s that teams which believe they have achieved complete trust elimination may operate with false confidence that itself becomes a vulnerability. The goal should be trust minimization, not trust elimination. If your security culture assumes zero trust means zero risk, that’s the threat.
The Friction-Shadow IT Paradox
Ironically, aggressive zero trust implementation can recreate the exact vulnerabilities it’s designed to prevent. When continuous verification creates too much friction, too many authentication prompts, too many blocked workflows, users find workarounds. Shadow IT proliferates. Unmonitored channels open. Organizations attempting comprehensive overnight transitions typically face implementation failures and user resistance that undermine the program entirely. Incremental deployment by pillar, starting with identity, consistently outperforms big-bang rollouts.
What’s Changing in 2025–2026
Two developments define the frontier of zero trust right now.
AI Integration
The integration of AI and machine learning within zero trust architectures is producing real capability improvements, particularly in behavioral analytics and anomaly detection. The canonical early example: in August 2025, Cloudflare launched new capabilities within its Cloudflare One platform designed to help organizations monitor AI usage and protect against Shadow AI, which it describes as the unsanctioned use of generative AI tools that bypass corporate security controls. Our read: this signals that zero trust is evolving to treat AI models themselves as entities that require access verification, not just the humans using them.
Post-Quantum Cryptography
In March 2025, Cloudflare announced end-to-end support for post-quantum cryptography within its ZTNA solution, enabling quantum-safe connectivity from web browsers to corporate applications without requiring organizations to individually upgrade each system. This matters because the encryption underpinning zero trust’s secure communications, the channel through which continuous verification happens, needs to be quantum-resistant before quantum computing makes current encryption breakable. The organizations that don’t start this transition now will face a retroactive security crisis when the threat matures.
NIST released the final version of SP 1800-35 (Implementing a Zero Trust Architecture) in June 2025, documenting end-to-end implementations built with 24 commercial vendors in a government lab environment. It’s the most comprehensive practical build guide available for organizations starting from scratch.
Frequently Asked Questions
What is zero trust security in simple terms?
Zero trust security is a cybersecurity approach that eliminates automatic trust for any user, device, or network connection, including those already inside a corporate network. Instead of trusting based on location, every access request is verified continuously. The core principle: “never trust, always verify.” NIST defined the framework in SP 800-207 in 2020.
What are the five pillars of zero trust?
The CISA Zero Trust Maturity Model defines five pillars: Identity, Devices, Networks, Applications & Workloads, and Data. Each pillar progresses through maturity stages, Traditional, Initial, Advanced, and Optimal. Cross-cutting capabilities including visibility, analytics, automation, and orchestration apply across all five pillars.
Is zero trust the same as a VPN?
No. A VPN grants broad network access once a user authenticates. ZTNA (Zero Trust Network Access) grants access only to specific resources, verified continuously for every session. It’s the direct VPN replacement technology. Gartner predicted that by 2025, 60% of companies would replace VPNs with ZTNA solutions, a transition still underway for most organizations.
Who created zero trust security?
Zero trust was created by John Kindervag while a VP and Principal Analyst at Forrester Research around 2009–2010. He published the foundational paper introducing the model and the phrase “never trust, always verify.” Kindervag is now Chief Evangelist at cybersecurity company Illumio and served as a primary author of the NSTAC report to the President on zero trust.
Does zero trust prevent ransomware?
Zero trust significantly reduces ransomware risk by limiting lateral movement, the ability of attackers to spread through a network after initial compromise. Micro-segmentation, a core zero trust control, contains breaches to smaller network zones. However, zero trust doesn’t prevent the initial point of entry, and identity controls remain vulnerable to MFA bypass techniques.
How much does it cost to implement zero trust?
Costs vary widely by organization size, existing infrastructure, and vendor choices. The financial case rests on IBM’s 2024 data: the average breach costs $4.88 million, while organizations with mature zero trust programs save an average of $1.76 million per breach. Most practitioners recommend starting with existing MFA and IAM tools mapped to the five pillars before purchasing new platforms.
What You Now Know That Most Organizations Don’t Act On
Zero trust security isn’t a product, a perimeter replacement, or a checkbox. It’s a strategic reorientation, from “trust by location” to “verify always, grant least privilege, monitor continuously.” The concept is 15 years old. The mandate, the market, and the threat landscape have finally caught up.
The implementation gap, 72% intent, 30% execution, is the central story of cybersecurity in 2025. The organizations closing that gap are not the ones that bought a “zero trust platform.” They’re the ones that mapped identity as pillar one, built maturity incrementally, and didn’t mistake a vendor’s marketing claim for a security guarantee.
Watch three things over the next 12–18 months:
AI as a zero trust entity: As enterprises adopt generative AI tools, the frameworks for verifying AI model access, not just human access, will become a new frontier of zero trust architecture.
Post-quantum cryptography adoption: Organizations that don’t begin transitioning the cryptographic layer of their zero trust implementations will face a retroactive security crisis when quantum computing matures.
Regulatory enforcement sharpens: GDPR, NIS2, and U.S. federal compliance requirements are tightening. A breach without a documented zero trust program is increasingly being treated as negligence by regulators and cyber liability insurers alike.
If you’re building this, start with identity. Resist the “zero trust in a box” pitch. And read Gligor’s paper, not because he’s right that zero trust is theoretically impossible, but because the organizations that understand its limits are the ones that won’t be surprised when it doesn’t live up to its name.
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Artificial IntelligenceCareer Guide • May 23, 2026
How to Become a Prompt Engineer in 2026: The Honest Guide
The standalone job title is collapsing. The underlying skill is becoming mandatory across every technical role. Here’s the real path, skills, salaries, courses, and the warnings nobody else will tell you.
N
NeuralWired Editorial
12 min read • Updated May 23, 2026
⏱ 12 min
In 2023, Anthropic posted a job listing that broke the internet. The role: Prompt Engineer and Librarian. The salary ceiling: $335,000. The requirement that caused the real frenzy: no PhD, minimal coding experience. For a brief moment, the world believed you could earn a doctor’s salary just for being very, very good at talking to chatbots.
That moment is over.
Searches for “prompt engineer” on Indeed have dropped 86% from their April 2023 peak. Microsoft surveyed 31,000 workers across 31 countries and found that Prompt Engineer ranked second-to-last among roles companies plan to hire in the next 18 months. The standalone title, for most organizations, never really materialized.
And yet, here you are, reading a guide on how to become a prompt engineer. And the search volume for that exact phrase has surged 5,000%+ in the past 12 months. Both things are true at once, and the tension between them is exactly what this guide is about.
Our Read
The job title is dying. The skill is becoming mandatory. If you’re learning how to become a prompt engineer in 2026, you’re not chasing a job title, you’re building a capability layer that will sit underneath every technical role in the next decade. That reframe changes everything about how you should approach this.
The Paradox Nobody Is Talking About
Two credible, opposing forces are pulling at this field simultaneously. Understanding both is the foundation of making any smart career decision here.
The optimistic case is real: Grand View Research puts the global prompt engineering market at $222 million in 2023, projecting it to hit $2.06 billion by 2030, a CAGR of 32.8%. McKinsey reports that 71% of organizations now use generative AI in at least one business function. Every one of those deployments requires someone who knows how to work with language models systematically. That’s real demand.
The skeptical case is equally real. Fortune reported in May 2025 that Allison Shrivastava, economist at Indeed, put it plainly:
Prompt engineering as a skill is still definitely a good thing to have, but it’s not an entire title.
Allison Shrivastava, Economist, Indeed (Fortune, May 2025)
Jared Spataro, Microsoft’s Chief Marketing Officer for AI at Work, was even more direct. After his team’s survey of 31,000 workers across 31 countries:
Two years ago, everybody said, ‘Oh, I think prompt engineer is going to be the hot job.’ It’s not turning out to be true at all.
Jared Spataro, CMO AI at Work, Microsoft (Wall Street Journal, 2025)
His argument: modern AI models now ask clarifying questions, acknowledge uncertainty, and self-iterate. The human middleman who translated vague instructions into precise prompts is being absorbed into the model itself.
So which camp is right? Both. The reconciliation is simple: the discipline is real; the job description isn’t. Prompt engineering is becoming what spreadsheet literacy became in the 1990s, not a career, but a baseline competency that elevates every career it touches. Andrew Ng made this comparison explicitly, and it’s the clearest mental model available.
32.8%
Projected annual market growth (CAGR) through 2030
71%
Organizations now using generative AI in at least one function
−86%
Drop in “prompt engineer” job searches on Indeed since peak (April 2023)
What a Prompt Engineer Actually Does
Strip the hype and the definition is precise. Prompt engineering is the systematic practice of designing, structuring, and optimizing text instructions, prompts, to guide large language models like OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini toward accurate, relevant, and consistent outputs. It combines natural language processing, cognitive science, linguistics, and iterative systems design.
That last part matters: iterative systems design. The most important thing Isa Fulford’s widely-used curriculum at DeepLearning.AI establishes is that effective prompting is not about finding “magic words.” It’s about systematic evaluation, measurement, and structural thinking. The people who treat it that way build things that work in production. The people who treat it as a creative guessing game produce inconsistency at scale.
The Core Techniques You Actually Need to Know
Technique
What It Is
When to Use It
Zero-shot prompting
No examples given; model uses training knowledge alone
Simple, well-defined tasks; quick prototyping
Few-shot prompting
1–5 examples embedded in the prompt to guide output format
Consistent formatting, classification tasks, tone matching
Chain-of-thought (CoT)
Instructs model to reason step by step before answering
Logic, math, multi-step problem solving
Retrieval-Augmented Generation (RAG)
Combines LLM with external knowledge base to reduce hallucination
Background instructions defining model persona, scope, and constraints
Product deployments, customer-facing AI tools
Prompt chaining
Linking multiple prompts sequentially; each output feeds the next
Complex multi-step workflows, agent pipelines
The Skills That Actually Matter in 2026
Here’s where most guides go wrong: they describe the skills that got people hired in 2023. The market has moved. Based on aggregated requirements from active listings at Google, Microsoft, Amazon, JPMorgan Chase, Booz Allen Hamilton, and leading AI-native startups, here’s what employers are actually looking for right now.
LLM API proficiency, At minimum one of: OpenAI, Anthropic Claude, Google Gemini, or Microsoft Copilot. Not just using the chat interface, working with the API programmatically.
Prompt technique mastery, Zero-shot, few-shot, chain-of-thought, RAG. These aren’t optional vocabulary; they’re the toolkit every practitioner is expected to have.
Python programming, Strongly preferred for senior roles; not always required for entry-level marketing or content positions. If you want engineering-tier compensation, this is non-negotiable.
Token economics and context window management, Understanding how models handle input length, what falls out of context, and how to structure information for reliability.
Evaluation and benchmarking, The ability to design A/B tests for prompts, measure output quality systematically, and build evals that catch prompt drift when models update. This is where most entry-level practitioners fall short.
Responsible AI and bias detection, Not a box-check skill. Organizations deploying AI at scale have legal and reputational exposure; people who can identify and mitigate bias in LLM outputs are genuinely scarce.
Domain expertise, The highest-value prompt engineers are domain experts first. A healthcare analyst who can engineer clinical documentation prompts is worth more than a generic prompt specialist. The skill multiplies domain knowledge; it doesn’t replace it.
⚠ Career Risk
The “no coding required” framing from 2023 is obsolete for any role paying over $90K. Entry-level positions at non-technical companies still exist without code, but AI lab and enterprise engineering roles almost universally require Python and API experience. Plan accordingly.
Salaries: The Honest Numbers
The $335,000 Anthropic listing was real. It was also an outlier at an elite AI safety lab during a period of acute talent scarcity, for a senior specialized role. Using it as a benchmark is like using NBA contracts to estimate what competitive basketball players earn. Here’s the actual range.
Source
Salary Range
Context
ZipRecruiter (June 2025)
$33K – $95K (avg $63K)
Includes contract and part-time; skews low
Glassdoor (via Coursera, Dec 2025)
$90K – $160K (avg $123K)
Full-time tech roles; more representative for career changers
Big Tech (Google, Microsoft, Amazon, Meta)
$110K – $250K
Senior IC and staff-level roles; equity separate
AI Labs (OpenAI, Anthropic, Cohere)
$150K – $335K+
Equity-heavy; total comp often exceeds base significantly
Government / Consulting (Booz Allen)
Up to $212K
Cleared roles; lower equity but high stability
The signal worth watching: Forward Deployed Engineers (FDEs) are where the highest-demand adjacent hiring is concentrating right now. OpenAI formalized its FDE program at scale on May 11, 2026, these are hybrid engineering and client-facing practitioners who embed with enterprise customers to deploy AI in production. Job postings for FDEs reportedly grew 800%+ in 2025. If you’re building prompt engineering skills and want a clear career target, FDE is the most concrete emerging track.
Best Courses and Certifications in 2026
No industry-standard certification equivalent to AWS or PMP exists in this field yet. Expert consensus is consistent: a portfolio of real AI applications outweighs any certificate. That said, one recognized credential on a resume does open doors, it signals fluency to hiring managers who don’t know how else to screen for it.
Course
Provider
Cost
Credibility Signal
ChatGPT Prompt Engineering for Developers
DeepLearning.AI (Andrew Ng + Isa Fulford)
Free, ~90 min
Highest technical credibility among engineering hiring managers
Prompting Essentials
Google Cloud Skills Boost
Paid (Credly badge issued)
HR-recognizable; Google brand carries weight in enterprise
Prompt Engineering for ChatGPT
Vanderbilt / Coursera
~$49 certificate, ~18 hours
University-backed; more respected by non-technical HR
AI Prompt Engineering Series
IBM
Varies
Enterprise-credible brand; useful for Fortune 500 applications
Azure OpenAI Prompt Engineering
Microsoft Learn
Free
Best for roles targeting Microsoft Copilot ecosystem
Best strategy: Complete one certificate from a recognized platform (DeepLearning.AI for technical roles; Google for enterprise roles). Then build a GitHub repository with three to five real LLM application examples, prompt chains, evaluation scripts, RAG pipelines. The portfolio is what gets you the interview. The certificate is what gets you past the keyword filter.
Step-by-Step Career Roadmap
This is for three distinct readers: developers who want to integrate AI into existing work, career switchers approaching this from a non-technical background, and engineering leaders building team capabilities. The path diverges early.
For Developers
Start with the DeepLearning.AI course, 90 minutes, free, co-taught by Andrew Ng and Isa Fulford. It’s the closest thing to canonical teaching the field has, and engineering hiring managers recognize it. Do it this week.
Build with the APIs directly, Sign up for OpenAI and Anthropic developer accounts. Write scripts. Chain prompts. Build a small RAG prototype using your own documents. The tactile experience is irreplaceable.
Learn to evaluate, not just generate, The hardest part of prompt engineering at production scale isn’t writing good prompts; it’s detecting when they fail. Build an eval suite for your prompts. Measure output quality. This is what separates junior from senior practitioners.
Move toward context engineering, The field is converging on “context engineering”, managing what information enters the model’s input window at runtime. This is the next layer above basic prompting. Study LangChain, agent frameworks, and retrieval architecture.
Target FDE or LLM Engineer roles, These titles are where serious engineering-grade prompt work is actually happening and where compensation reflects the skill level.
For Career Switchers (Non-Technical)
The pure “prompt engineer” title pivot carries real risk. The correct framing is not “become a prompt engineer” but rather “add prompting capability to your domain expertise.” A healthcare writer who can engineer clinical documentation prompts is far more valuable than a generic prompt specialist with no domain background. The skill multiplies; it doesn’t substitute.
Identify your domain expertise first. That’s your differentiator.
Take the Google Prompting Essentials or Vanderbilt/Coursera certificate, HR-recognizable and accessible without technical prerequisites.
Build domain-specific examples: if you’re in finance, build a portfolio of prompts that automate financial reporting tasks. If you’re in healthcare, build clinical documentation workflows.
Target titles like AI Trainer, AI Integration Specialist, Applied AI Analyst, these are where standalone prompt-adjacent hiring is actually occurring in 2026, not under the “Prompt Engineer” label.
The Webmaster Analogy
In the mid-1990s, “Webmaster” was a defined, specialized, high-paying role. Within a decade, web skills were distributed across designers, developers, content managers, and marketers, the title disappeared but the skills proliferated. Prompt engineering is following an identical trajectory on a compressed timeline. This isn’t a reason to avoid the skill. It’s a reason to acquire it before it becomes a baseline expectation rather than a differentiator.
The Future: Context Engineering Is What Comes Next
The practitioners who are most valuable in 2026 aren’t optimizing individual prompts, they’re designing the full information pipeline that feeds AI systems at runtime. This is context engineering: the discipline of systematically managing what information gets included in a model’s input window, in what form, and in what order.
The progression looks like this: basic prompting → structured prompt design → RAG architecture → context engineering → LLM evaluation systems. The further right you sit on that spectrum, the more durable your value and the higher your compensation ceiling.
Two dynamics are compressing this timeline. First, models are improving fast, GPT-4 and its successors already self-refine outputs more capably than GPT-3.5. By 2027, routine prompt iteration for common tasks may be largely automated. What remains valuable is strategic prompt architecture: system design, evaluation framework design, and context pipeline engineering. Second, OpenAI’s formalization of its Forward Deployed Engineer program in May 2026 signals that the highest-leverage prompt-adjacent work is becoming institutionalized as a distinct engineering discipline, not a standalone role, but a specialization within software engineering.
Stanford’s 2025 AI Index, analyzing over 51,000 job posting websites, found that 1.8% of all U.S. job postings now require AI skills, up from 1.4% in 2023. That trajectory doesn’t stop. The question is whether you’re building the deeper skills before they become the expectation.
Frequently Asked Questions
What does a prompt engineer do?
A prompt engineer designs, tests, and refines text instructions given to AI language models like ChatGPT, Claude, and Gemini. They craft inputs that guide models toward accurate, useful, and consistent outputs across applications from customer service automation to code generation and content creation. The role combines linguistics, systems thinking, and iterative testing, not creative guessing.
Do you need to know how to code to become a prompt engineer?
Basic prompt engineering doesn’t require coding. However, senior roles increasingly require Python for API integration, evaluation scripting, and RAG pipeline design. Entry-level positions at non-technical companies rarely require code; AI lab and enterprise engineering roles almost always do. The “no coding required” framing from 2023 is effectively obsolete for roles paying above $90K.
How much does a prompt engineer earn?
U.S. salaries range from roughly $63,000 (ZipRecruiter national average, including contract roles) to $123,000 (Glassdoor average for full-time tech positions). Senior roles at major AI companies reach $250,000 and above in total compensation. Anthropic’s widely reported outlier listing reached $335,000, but that was a senior, specialized role at an elite AI lab during a period of acute talent scarcity. It is not a typical benchmark.
Is prompt engineering a good career in 2026?
The skill is highly valuable; the standalone job title has underperformed expectations. Prompt engineering is most powerful as a capability layer added to existing domain expertise, a software developer, healthcare analyst, or marketing strategist who prompts effectively commands a premium. As a standalone career pivot with no domain background, the path is significantly narrower than 2023 coverage suggested.
What are the best certifications for prompt engineering?
The most employer-recognized options are Google’s Prompting Essentials (issues a Credly badge, HR-recognizable), Vanderbilt/Coursera’s Prompt Engineering for ChatGPT (university-backed, roughly 18 hours), and DeepLearning.AI’s course with Andrew Ng and Isa Fulford (highest technical credibility among engineering hiring managers). No industry-standard certification equivalent to AWS or PMP exists yet. A portfolio of real projects matters more than any single certificate.
What is the future of prompt engineering?
The standalone job title will continue shrinking. The underlying skill, systematically designing and evaluating AI inputs, is becoming embedded across software engineering, data science, product management, and operations roles. The highest-growth adjacent area is context engineering and LLM evaluation frameworks, where practitioners design the full information pipeline feeding AI systems at runtime. That’s where the durable, high-value work is concentrating.
What You Now Know That Most People Don’t
The prompt engineering story isn’t boom or bust. It’s transformation. The job title peaked in April 2023 and didn’t recover. The skill is being absorbed into every technical role that touches AI, which is rapidly becoming every technical role, full stop. The workers capturing value are the ones who stopped waiting for a “Prompt Engineer” posting and started building the capability into whatever they already do.
Three things to watch and act on in the next 6–18 months:
The Forward Deployed Engineer track is formalizing fast, OpenAI’s May 2026 program announcement is the clearest signal of where prompt-adjacent work is going at scale
Context engineering is the next layer, start learning RAG architecture and LLM evaluation frameworks before they become baseline expectations
Model updates will devalue model-specific prompt knowledge, build technique fluency, not platform-specific tricks
Best Programming Languages to Learn in 2026: What the Data Actually Says
TypeScript just dethroned Python on GitHub for the first time in over a decade. AI tools cut junior developer job postings by 25%. The honest, data-backed ranking every developer needs before choosing their next language.
NeuralWired Research DeskMay 23, 202614 min read2026 DataCareer Guide
In August 2025, TypeScript did something no language had managed in over a decade: it knocked Python off the top spot on GitHub. The margin was slim, roughly 42,000 contributors, barely 1.6% of a 180-million-user platform, but the symbolism landed across every developer forum worth reading. The era of “just learn Python” as universal career advice had quietly ended.
What makes 2026 genuinely different is that the question itself is evolving. It used to be, “which language gets me hired?” Now it carries a harder layer, “which languages will still require a human to write them in 18 months?” That’s not alarmism. SWE-bench verification rates jumped from 33% to over 70% in just two years. Google’s Sundar Pichai confirmed AI now writes more than a quarter of code at the company. These are not talking points. They’re operational realities.
+66.6%TypeScript contributor growth on GitHub YoY — Aug 2025
+7ppPython’s Stack Overflow adoption jump, largest single-year gain in a decade
−25%Drop in entry-level developer job postings in 2024
+15%BLS projected growth in software developer roles through 2034
The Shift Nobody Saw Coming
The programming language landscape had a stable power structure for years. JavaScript dominated developer surveys for a decade. Python rose steadily through the AI boom. Then 2025 delivered a genuine plot twist in each direction, back to back.
The 2024 GitHub Octoverse was the first signal: Python dethroned JavaScript for the first time in over a decade, fueled by a 59% surge in contributions to generative AI projects and a 98% increase in total generative AI project count. Then, barely twelve months later, TypeScript dethroned Python. In a platform of 180 million users, TypeScript added over 1 million monthly active contributors year-over-year, a 66.6% jump, reaching 2,636,006 contributors just ahead of Python’s 2,594,000.
Why TypeScript? The short answer is AI-assisted development. A 2025 academic study found that 94% of LLM-generated compilation errors are type-check failures. When teams are shipping AI-generated code into production, and 80% of new GitHub developers are using GitHub Copilot in their first week, TypeScript’s static typing catches a category of bugs that JavaScript cannot. Typed code and AI code generation are natural allies.
“We might be six to twelve months away from when the model is doing most, maybe all of what software engineers do end-to-end. I have engineers within Anthropic who say I don’t write any code anymore. I just let the model write the code. I edit it.”
— Dario Amodei, CEO & Co-Founder, Anthropic — Davos, January 2026
Our read: Amodei isn’t predicting the death of programming. He’s predicting the death of a particular kind of programming, the mechanical, specification-driven implementation work that entry-level roles have always been built on. The languages that matter in this environment are those where human judgment adds irreplaceable value: architecture, performance, security, domain logic. That reframe points toward exactly the same top languages, for different reasons than before.
2026 Language Rankings at a Glance
This table synthesizes GitHub Octoverse 2025, Stack Overflow Developer Survey 2025, TIOBE Index (May 2026), U.S. Bureau of Labor Statistics projections, and current job market salary data. No single source tells the full picture; this combines all of them.
#
Language
Primary Signal
US Senior Salary
Best For
AI Threat Level
1
Python
TIOBE #1 (21.81%); SO +7pp YoY
$98k–$188k
AI/ML, data science, automation
Medium–Low (core AI infra)
2
TypeScript
GitHub #1 by contributors (Aug 2025)
$95k–$180k
Full-stack, AI-product builds
Low (type safety + AI)
3
JavaScript
Stack Overflow #1, 66% usage, 13 yrs
$85k–$160k
Web, frontend, rapid prototyping
Medium (frontend commoditizing)
4
Rust
Most admired 10 years straight (72%); +35% job posts YoY
$130k–$235k
Systems, infrastructure, security
Very Low (perf-critical)
5
Go
TIOBE #8; cloud-native & DevOps
$115k–$195k
Cloud backends, DevOps, AI infra
Low (concurrency nuanced)
6
Java
TIOBE #3; enterprise bedrock
$95k–$175k
Enterprise, Android
Medium
7
C#
TIOBE Language of the Year 2025
$90k–$165k
.NET, Unity, enterprise
Medium
8
SQL
Non-negotiable second skill; PostgreSQL most admired DB, 3 yrs
$85k–$155k
Data engineering, analytics, backend
Low (query logic = human judgment)
The Top 8 Languages: Deep Dives
01
PythonThe Undisputed AI-Era King — With Caveats
Python’s position in 2026 is paradoxical: it’s simultaneously the most important language to learn and the most over-hyped entry-level career path. The TIOBE Index gives Python a 21.81% market share, a dominant lead no other language comes close to. Stack Overflow’s 2025 survey recorded its largest single-year adoption jump in over a decade: +7 percentage points. AI/ML job postings on Indeed are up 134% since 2020, and Python powers the overwhelming majority of that work.
The caveat: TIOBE CEO Paul Jansen noted in early 2026 that Python’s share has declined from a peak of 26.98% in July 2025 as domain-specific languages gain ground. Python’s explosive growth may be plateauing, when every data scientist already uses it, further growth hits a ceiling.
What the job market actually wants in 2026: not basic Python. Hiring managers want FastAPI, LangChain, experience building on LLM APIs, and Python used as infrastructure glue for AI pipelines — not just scripted data manipulation. The “learn Python in 30 days” bootcamp path leads to the most competitive segment of an increasingly AI-saturated entry-level market.
“Our goal is for Python to be a great tool that helps the ever-growing developer community build the world they envision. We couldn’t be more pleased to learn about Python’s continued rise in popularity on GitHub, especially coupled with the increased use of Jupyter Notebooks, data analysis, AI, and open source technology.”
— Deb Nicholson, Executive Director, Python Software Foundation — GitHub Octoverse 2024
TypeScriptGitHub’s New #1 — and the AI-Native Default
TypeScript’s ascent to the top of GitHub isn’t just a popularity story, it reflects a structural change in how professional teams ship code in an AI-assisted era. When AI tools generate your code, type errors become the primary failure mode. TypeScript catches them at compile time. JavaScript doesn’t. For engineering teams shipping AI-generated code into production, which describes most of them, this is risk management, not preference.
A critical context note worth having: the 42,000-developer margin between TypeScript and Python on GitHub is just 1.6% of the platform. TypeScript also compiles to JavaScript, they share the same runtime ecosystem. The JavaScript + TypeScript combined total vastly outscales Python in total GitHub activity. The “TypeScript at #1” headline is accurate, but that framing completes it.
There is a legitimate dissenting view from senior developers, particularly at smaller companies: TypeScript’s type system is over-engineered for teams under ten people, and the velocity cost of strict typing outweighs its benefits outside enterprise-scale codebases. Smaller startups often retain plain JavaScript for speed. That’s not wrong, it’s a real tradeoff, not a failure to understand TypeScript.
GitHub #1 · Aug 2025+66.6% YoYAI-nativeReact/Next.jsNode.js
03
JavaScriptStill the Most-Used Language on Earth
JavaScript has been used by 66% of all professional developers for thirteen consecutive years in Stack Overflow surveys. You can’t build a frontend without it, TypeScript compiles to it. You can’t run Node.js backends without it. The shift to TypeScript doesn’t reduce JavaScript’s relevance; it refines it. The JavaScript/TypeScript ecosystem combined still vastly outscales every other language by total GitHub activity.
The frontend commoditization threat is real: AI tools can scaffold basic React interfaces faster than most junior developers. But the JavaScript developer who understands the runtime, the event loop, and the performance model remains difficult to replace. The language isn’t the moat, the depth of understanding is.
SO #1 by usage · 66%13 consecutive yearsReactNode.jsVue
04
RustThe Highest-Paying Language — and the Most Honest Career Bet
Rust has been the most admired programming language in the Stack Overflow Developer Survey for ten consecutive years, 72% admiration rate in 2025. Job postings grew 35% year-over-year. US senior salaries run $130,000 to $235,000+, with top employers including Cloudflare, 1Password, and Figma. The salary premium exists precisely because the talent pool is small, only an estimated 709,000 developers use Rust as their primary language globally.
The admiration-to-adoption gap is real and requires honest framing: TIOBE still doesn’t rank Rust in its top 15 by search volume. The ownership model and borrow checker genuinely filter out casual learners, not as a flaw, but as the mechanism that produces the quality guarantees that make Rust worth paying for. For mid-career developers targeting systems programming, infrastructure, or AI inference infrastructure (where Rust is already displacing Python at companies like Cloudflare), this is the strongest long-term career bet in the entire landscape, if you can commit to the learning curve.
Most admired · 10 years straight$130k–$235k++35% job posts YoYSystemsSecurity
05
Go (Golang)The Pragmatist’s Systems Language
Go sits at #8 on TIOBE with strong and growing adoption in cloud-native backends and DevOps tooling. It’s the gentler on-ramp to systems programming compared to Rust, no borrow checker, a straightforward concurrency model, and a famously simple toolchain. Go developers working on AI inference roles command salaries roughly 37% higher than TypeScript counterparts in equivalent roles, per 2026 benchmark data. Best choice for developers targeting scalable backends, Kubernetes tooling, or infrastructure work, high ROI without a year-long learning cliff.
TIOBE #8Cloud-nativeKubernetesDevOpsConcurrency
06
JavaEnterprise Bedrock That Refuses to Die
Java’s trajectory is the cautionary tale about dominance not being permanence, and then the plot twist where it refuses to confirm the lesson. TIOBE had Java at 26.49% share in 2001; today it sits near 8%. That sounds like collapse. But Java remains TIOBE’s #3 language overall, powers the majority of enterprise backends globally, runs Android development, and is embedded in financial services and healthcare systems that aren’t being rewritten anytime soon. Java in 2026 is a stability bet, not a growth bet.
TIOBE #3EnterpriseAndroidSpringFintech
07
C#TIOBE’s Language of the Year — With a Defined Niche
TIOBE named C# its “Programming Language of the Year 2025” for achieving the largest year-over-year gain among tracked languages. This measures growth rate, not absolute dominance, Python still holds the top spot overall. But C#’s resurgence reflects Microsoft’s ongoing .NET investment and Unity’s continued reign as the dominant game development engine. If game development or Microsoft-stack enterprise is your specific target, C# is non-negotiable.
TIOBE Language of the Year 2025.NETUnityEnterprise
08
SQLThe Non-Negotiable Second Skill
SQL isn’t a language to learn in the way Python or TypeScript is. It’s table stakes. PostgreSQL has been the most admired and most desired database in Stack Overflow’s Developer Survey for three consecutive years. Every data role, every backend role, and every analytics role requires SQL competency. The fastest path to employability in 2026 — starting from zero, is Python + SQL, not Python alone. Don’t treat SQL as optional. It isn’t.
Non-negotiablePostgreSQL most admired DB, 3 yrs straightData engineeringAnalytics
The AI Disruption: What It Actually Means for Your Career
Here’s the uncomfortable arithmetic most career guides skip: the U.S. Bureau of Labor Statistics projects 15% growth in software developer jobs through 2034, roughly 129,200 new openings per year. Simultaneously, it projects a 6% decline in traditional “computer programmer” roles. Both numbers are accurate. They don’t contradict each other. The industry is growing at the top while contracting at the bottom.
Entry-level developer job postings dropped approximately 25% in 2024. Employment of developers aged 22–25 fell nearly 20% that same year. An IEEE Spectrum report from December 2025 found employers’ outlook for graduate hiring was at its most pessimistic since 2020, explicitly attributing the shift to AI handling tasks previously assigned to junior developers.
⚠ Critical Context
The “learn Python and get hired in six months” advice was accurate in 2021. It’s still true for the right Python skills, but the wrong version (basic scripting, vanilla CRUD apps) is the fastest way to build a portfolio that AI can already replace. The bar for “hireable” has shifted upward, not disappeared.
SWE-bench verification rates — measuring how well AI resolves real GitHub issues, jumped from 33% in early 2024 to over 70% by early 2026. That’s not linear progress. It’s an inflection point. GitHub Copilot reached 4.7 million paid subscribers by January 2026, up 75% year-over-year. Over 1.1 million public repositories now import an LLM SDK, up 178% year-over-year.
The pattern that emerges is consistent: AI is replacing tasks, not entire professions. It replaces the most routine, specification-driven implementation work fastest. What it can’t reliably replace, yet, is architectural judgment, security reasoning, performance-critical optimization, and deep domain expertise. The languages commanding the highest salaries in 2026 (Rust, Go senior roles, Python AI engineering) all map to exactly these categories.
AI is replacing the most specification-driven coding fastest. What remains is judgment, and the languages that reward judgment pay the most.
How to Choose: A Decision Matrix
The right language depends entirely on where you’re starting and where you’re going. Here’s the breakdown without the generic advice.
Starting from zero
Python, then SQL. Python’s syntax is the lowest-friction path to being productive. It’s the #1 language for AI and data roles, which are the fastest-growing job category. SQL pairs immediately for data work. JavaScript is the right alternative if you specifically want to build visible web applications from day one, seeing results in a browser sustains motivation. Avoid starting with Rust or C++, the time-to-productivity ratio is too low for beginners.
Mid-career switch or bootcamp student
Python + TypeScript is the highest-ROI six-to-twelve month combination in 2026. Python for AI-integrated backend work; TypeScript for full-stack product development. The portfolio you need isn’t a to-do app. It’s a project integrating an LLM API, handling real data, and demonstrating architectural thinking, even at modest scale. GitHub presence matters more than certifications in 2026’s hiring environment.
Mid-career developer (5–10 years in)
The language question is secondary to the domain question. Domain expertise in healthcare, fintech, infrastructure, or security is the moat AI doesn’t erode. That said: adding Rust or Go to a Python/TypeScript base positions you for systems and infrastructure roles commanding $140k–$200k+. The admiration-to-adoption gap in Rust (72% admired, small talent pool) signals a hiring premium that early adopters will capture.
Engineering manager or CTO
TypeScript should be your default assumption for new projects, 80% of new developers on GitHub use Copilot in their first week, and TypeScript produces fewer AI-generated compile errors. Audit your stack against the GitHub Octoverse 2025 finding: 80% of new library activity is concentrated in six languages (Python, JavaScript, TypeScript, Java, C++, C#). If your stack sits outside this cluster, your dependency maintenance burden is growing.
💡 Our Read
The safest combination for a career spanning the next decade: Python for AI/data work, TypeScript for product builds, SQL as the non-negotiable second skill, and a serious Rust study effort before 2027. That covers the three dominant trends without betting everything on a single paradigm.
FAQ: Best Programming Languages to Learn in 2026
The top questions Google surfaces on this topic, answered directly and completely.
Which programming language is most in demand in 2026?
Python leads job market demand in 2026, especially for AI, data science, and machine learning. The Stack Overflow 2025 Developer Survey recorded Python’s largest single-year adoption jump, +7 percentage points, of any major language. AI/ML job postings on Indeed are up 134% since 2020. For full-stack product roles, TypeScript is increasingly the default hiring assumption.
Is Python still worth learning in 2026?
Yes. Python holds TIOBE’s #1 spot at 21.81% market share and dominates the fastest-growing job category in software. Senior Python developers earn $98k–$188k annually. The caveat: entry-level Python scripting faces direct AI competition. Pairing Python with SQL and LLM framework experience (LangChain, FastAPI) is what the 2026 job market rewards.
Is JavaScript still relevant in 2026?
Absolutely. JavaScript is used by 66% of all professional developers, the most-used language for thirteen consecutive years per Stack Overflow. TypeScript, which overtook Python as GitHub’s #1 language by contributors in August 2025, compiles to JavaScript. The two are the same ecosystem. Combined, they outscale all other languages in total GitHub activity.
Should I learn Rust in 2026?
Rust is the most admired language (72%, Stack Overflow 2025) and offers the highest salary premium, $130k to $235k+ in the US, with job postings up 35% year-over-year. Best suited for developers targeting systems programming, infrastructure, or security. Not recommended as a first language: the learning curve is steep and the talent pool is deliberately small, which is exactly what drives the premium.
What programming language should a beginner learn in 2026?
Python is the consensus top recommendation: simple syntax, vast library ecosystem, #1 for AI and data roles, strong job market. Pair it with SQL immediately for maximum employability. JavaScript is the best alternative if you want to build visible web results from day one. Avoid starting with Rust or C++, the time-to-productivity ratio is too low for complete beginners.
Is Go (Golang) worth learning in 2026?
Yes, for a specific target. Go ranks #8 on TIOBE and dominates cloud-native backends and DevOps tooling. It’s the gentler path to systems programming compared to Rust, with Go developers in AI inference roles earning roughly 37% more than TypeScript counterparts in equivalent positions. Best for developers targeting scalable backend systems, Kubernetes, or infrastructure work.
What are the highest-paying programming languages in 2026?
In the US: Rust ($130k–$235k+), Go (strong AI inference premium), senior Python ($112k–$188k), and TypeScript in senior full-stack roles. Solidity (blockchain) reaches $120k–$200k but carries market volatility. Salary data from ZipRecruiter, Glassdoor, and RustJobs.dev as of April 2026.
Will AI replace programmers in 2026?
Not entirely, but specific roles are already disrupted. Entry-level developer postings fell ~25% in 2024, and AI generates over 25% of code at Google. The BLS still projects 15% growth in software developer roles through 2034, but simultaneously a 6% decline in traditional “computer programmer” roles. AI is replacing tasks, not entire professions, and raising the floor of what human developers must deliver.
Is TypeScript better than JavaScript in 2026?
For professional development at team scale, TypeScript is increasingly the default: it overtook Python as GitHub’s #1 language in August 2025, driven by the need to catch type errors in AI-generated code. For small projects or solo developers, plain JavaScript remains entirely valid. The debate is real for small teams, TypeScript’s strictness has a velocity cost that not every codebase justifies.
What programming languages are losing popularity in 2026?
Languages in confirmed decline include PHP (dropped from TIOBE #3 in 2010 to #18 by 2026), Objective-C (displaced by Swift), and Haskell. Java’s drop from 26% TIOBE share in 2001 to ~8% today is the canonical cautionary tale, but slow decline over decades isn’t obsolescence. Java remains essential in enterprise and Android. COBOL is maintained but not learned by new developers.
What Comes Next
The best programming languages to learn in 2026, Python, TypeScript, Rust, Go, are correct as answers. But the reason they’re correct has shifted. It’s no longer just about ecosystem size or job posting volume. It’s about where human judgment adds irreplaceable value when AI handles the routine implementation.
Python wins because building AI pipelines requires architectural and domain judgment, not just syntax. TypeScript wins because typed code is how teams safely ship AI-generated code into production. Rust wins because memory-safe, performance-critical systems are the last category AI handles poorly, and the salary premium reflects exactly that scarcity. Go wins because cloud-native infrastructure requires the kind of concurrency reasoning that AI still struggles to get right.
The developer who learns Python + TypeScript this year, pairs it with SQL, builds publicly on GitHub with AI-integrated projects, and starts a serious Rust track before 2027 isn’t playing defense. They’re building a profile genuinely difficult to displace, by an AI or a competitor.
Three things to watch in the next 6–18 months: First, whether SWE-bench rates cross 80%, the threshold at which Amodei’s forecast starts affecting senior roles, not just junior ones. Second, the October 2026 GitHub Octoverse, whether TypeScript’s 1.6% margin over Python holds, widens, or reverses. Third, Rust adoption in LLM inference infrastructure, if it normalizes at Cloudflare-scale companies, the salary premium will compound and early adopters will be positioned ahead of the hiring curve.
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Machine Learning Engineer Salary in 2026: Google, Meta, and OpenAI vs. Everyone Else
NeuralWired Research·May 2026·14 min read·Salary & Careers
A machine learning engineer at Meta’s E6 level cleared $786,000 in total compensation last year. An entry-level ML engineer at a mid-market company in Dallas earned $69,000. Both carry the same job title. This is the central problem with every ML engineer salary article you’ve read, they average those two people together, then tell you the result means something.
The machine learning engineer salary in 2026 isn’t a number. It’s a range so wide it makes the average nearly useless. What you actually need to know is which part of that range you’re in, what moves you between tiers, and what the market looks like beyond the FAANG-heavy data that dominates the conversation. That’s what this article delivers.
$161K
Average US base salary (Glassdoor, May 2026)
$265K
Median total comp at top-tier tech (Levels.fyi)
3.2:1
Open ML roles vs. qualified candidates
56%
Wage premium for AI skills globally (PwC 2025)
The Real Numbers | By Source, Not By Average
Every major salary database is measuring a different population. Before you benchmark against any figure, you need to know who that figure actually describes. Here’s what each source is actually telling you:
Source
Figure (US, 2026)
What It Actually Measures
Glassdoor
$161,030 avg base; up to $248,375 at 90th pct
Self-reported, delayed, skews toward large employers
Built In
$162,080 base; $212,022 total comp
Verified tech-industry responses; most common bracket $200K–$210K
ZipRecruiter
$128,769 average; $101.5K–$155K (25th–75th pct)
Broader job market including non-tier-1 employers
Levels.fyi
$265,000 median total comp
Primarily FAANG and top-tier tech — equity-heavy, not representative of full market
PayScale
$125,000 avg base
Broadest employer mix; includes many non-tech-industry ML roles
Robert Half
$170,750 midpoint; 4.1% annual growth
Hiring manager surveys; reliable for mid-market enterprise
Why This Range Exists
The $40,000 spread between ZipRecruiter and Levels.fyi isn’t a measurement error, it’s a structural reality. One database captures a Series B startup in Austin; the other captures a staff engineer at Google. They’re different jobs with the same title. Any article that gives you a single average number without this context is wasting your time.
Entry level is a separate market entirely. Entry-level ML engineers in the US average $69,362 as of May 2026, with the majority earning $51,500–$78,500. The headline $200K+ figures are for engineers with three to seven years of production deployment experience. Not bootcamp graduates. Not new master’s program completers.
Google, Meta, OpenAI: What the Data Actually Shows
If you want the ceiling, Levels.fyi’s verified compensation data from May 2026 is the place to look. But interpret these numbers as the top end of the market, not the market itself.
Company
Entry Level
Senior/Principal
Median Total Comp
Meta
$187K (E3)
$786K (E6)
$450,000
Google
$199K (L3)
$743K (L7)
$290,000
Google (AI Engineer title)
$183K (L3)
$583K (L6)
$280,000
OpenAI (L5 SWE)
$1.15M total: $336K base + $774K stock/year
Frontier lab; not industry-representative
OpenAI’s compensation figures deserve a separate sentence: they are not a market benchmark. They reflect the economics of a frontier AI lab during a capital-intensive arms race, the same conditions that produce $300 million in equity grants for a handful of researchers. Anthropic operates in the same tier. These numbers are real; they’re just not what a hiring manager at a healthtech company or a Series C startup is competing against.
“The salary conversations in this discipline are harder than most because the gap between base salary and total comp is enormous at the senior end, and because ‘ML engineer’ means different things at different companies. Someone building recommendation systems at a Series D startup and someone fine-tuning foundation models at Meta are both called ML engineers. They’re not doing the same job. They’re not paid the same either.”
— Robert, Co-Founder & Strategic Advisor, KORE1 (ML Engineer Salary Guide, May 2026)
Which Skills Move the Needle (With Dollar Figures)
The single most actionable finding from 2026 salary data: specialization has a larger salary impact than switching companies, changing cities, or earning an additional degree. Here’s the breakdown from Signify Technology’s 2025–2026 US Market Benchmarks:
Skill / Specialization
Premium Over Base
Dollar Range
Generative AI / LLM Fine-tuning
+40%–60%
+$56,000–$110,000
MLOps Expertise
+25%–40%
+$35,000–$74,000
NLP
+20%–35%
+$28,000–$64,000
PyTorch Proficiency
+8%–12%
+$10,000–$22,000
RAG architecture, retrieval-augmented generation, deserves specific mention because KORE1’s placement data shows it triggering negotiating power in a way that generic “AI experience” doesn’t. One placement example from their May 2026 guide: a healthcare AI engineer moving to fintech negotiated a $22K base increase specifically because she had built a production RAG system processing 400,000 clinical documents. That’s not a hypothetical. That’s a closed deal.
The premium compounds with seniority. Levels.fyi’s Q3 2025 analysis found that entry-level AI engineers earn 6.2% more than non-AI peers, but staff engineers earn 18.7% more. Investing in AI specialization early isn’t a one-time bump; it’s a multiplier that widens as you advance.
“The biggest mistake in 2026 is hiring a PhD researcher when you actually need a software engineer who knows how to deploy a model reliably to production. The highest ML Engineer salaries are no longer going to those who can theorize about AI. They are going to those who can ship AI products reliably.”
— Optiveum, specialist ML recruitment (April 2026)
The Credential Debate | What the Data Actually Shows
There’s a narrative circulating that portfolio beats degree, and it’s partially true. For applied engineering roles, deploying pipelines, building RAG systems, productionizing models, hiring managers at most non-research firms have deprioritized formal degrees. The PwC 2025 data found employer demand for formal degrees falling 9 percentage points for AI-exposed jobs between 2019 and 2024.
But the counterpoint matters: the percentage of job postings mentioning PhDs jumped over 6% year-over-year in 2026, while postings requiring master’s and bachelor’s degrees dropped. At the frontier research tier, the roles with the highest ceilings, academic credentials are becoming more important, not less. The “just ship things” premium applies to applied engineers; research scientists and those aiming for foundation model labs face a different calculus.
The Global Gap: US vs. UK, Canada, Australia
The US salary differential isn’t narrowing. For ML engineers outside the US, this is one of the most financially consequential career facts of the decade.
Market
Average ML Salary (USD equiv.)
Source
United States
$161,000–$186,000 base; $212K–$265K total
Glassdoor / Levels.fyi, May 2026
United Kingdom
~$97,000 (£76,198)
Indeed UK, May 2026
Canada
~$129,850
Qubit Labs, 2026
Australia
~$91,000 (AUD $137,500 avg)
Glassdoor AU, May 2026 (183 submissions)
Switzerland
~$160,300
Qubit Labs, 2026 — leads Western Europe
A senior ML engineer in the UK earns roughly £76K–£120K, or $100K–$155K USD equivalent. The same profile in the US commands $180K–$300K+ total comp. That gap, roughly double, has one practical implication for UK, Canadian, and Australian engineers: remote-first US employers are one of the only pathways to access US-scale compensation without relocating. It’s not a small opportunity; it’s a career-defining one for engineers who pursue it deliberately.
Why Salaries Are This High | And the Risks That Could Change That
The ML salary premium has a structural explanation, not just a hype explanation. Understanding the difference matters for anyone making a multi-year career bet.
The Supply Problem
There are approximately 1.6 million open AI/ML positions and only around 518,000 qualified candidates, a 3.2-to-1 demand-to-supply ratio. That’s not a hiring freeze number; that’s the ratio driving upward pressure on compensation. The ML market is projected to reach $503.4 billion by 2030, up from $113.1 billion in 2025. Demand for ML talent is growing faster than universities can produce it, and the gap between “completed an ML course” and “can deploy and maintain a production LLM pipeline” is enormous. That gap is where the compensation premium lives.
PwC’s 2025 Global AI Jobs Barometer, the largest study of its kind, based on analysis of close to one billion job ads across six continents, found that workers with AI skills command a 56% wage premium over equivalent roles that don’t require AI skills, across every industry analyzed. That premium was 25% the year prior.
“In contrast to worries that AI could cause sharp reductions in the number of jobs available, this year’s findings show jobs are growing in virtually every type of AI-exposed occupation, including highly automatable ones. Even if they can pay the premium required to attract talent with AI skills, those skills can quickly become out of date without investment in the systems to help the workforce learn.”
— Joe Atkinson, Global Chief AI Officer, PwC (PwC Press Release, June 2025)
Meanwhile, ML engineering is growing while general software engineering contracts. AI/ML job postings were up 59% from the pre-pandemic baseline in July 2025 (Indeed Hiring Lab), while general software engineering positions were down 49%. The “tech layoffs” and “ML demand” headlines are describing different talent pools. They are not contradictory.
The Risks | Two Worth Taking Seriously
Contrarian Signal
Glassdoor’s 2026 data shows ML engineers as the only category with a year-over-year salary decrease, down approximately $10,000 from early 2025. The 365 Data Science analysis that surfaced this finding correctly notes Glassdoor’s methodology limitations (self-reported, delayed, subject to sampling bias), but the signal shouldn’t be dismissed entirely. Our read: this likely reflects early normalization in generalist ML roles while LLM and GenAI specialists continue to see premiums. It’s not evidence of a crash, but it’s a reason not to assume unlimited upward trajectory.
The second risk is structural: the 2021 SaaS hiring bubble inflated headcount on speculative valuations, then deflated hard. The prompt engineering “hype cycle” saw purported salaries of $250K–$300K briefly circulate before it became clear most of those roles required significant ML background, not just clever prompting. If AI productivity gains don’t materialize at the expected rate for enterprises, the frenzy driving compensation above market-clearing levels could correct. It’s a real scenario. The difference from 2021, as Pin’s Q3 2025 analysis notes, is that productivity growth in AI-exposed industries has nearly quadrupled since 2022, providing an economic foundation the SaaS bubble never had.
What This Means for Your Career Right Now
If You’re an Active ML Engineer
The most valuable move available to you in 2026 isn’t switching companies, though that’s worth $30K–$60K on average. It’s building demonstrable production deployment experience in LLMs or RAG architecture, which is worth $20K–$40K in base premium over 12 months. Internal promotions consistently lag the job-switching premium, which means that if you’ve built something real, the market will pay you more for it than your current employer will.
If You’re Making a Career Switch Into ML
The share of AI/ML engineering roles in overall tech hiring grew from 10% in 2023 to over 50% in 2025. But don’t benchmark against $200K+ headline figures, those are for engineers with three to seven years of production experience. Entry-level in this field averages $69,362. The path to senior compensation is real, but it runs through shipping things, not just studying them. Portfolio work and production deployments now outweigh degrees for most hiring decisions at non-research firms.
If You’re Hiring
AI/ML job postings increased 89% in the first half of 2025. Seventy percent of firms report a lack of applicants as their primary hiring hurdle. Firms that fail to adjust compensation benchmarks are losing candidates within 48 hours of an offer. One tactical lever that’s underused: contract-to-perm structures. Permanent base salaries for senior ML engineers sit at $175K–$240K; contract day rates for the same level run $800–$1,200/day. Engineers who won’t engage on a traditional permanent posting sometimes will on a project-based structure. That’s not a salary hack, it’s a pipeline access strategy.
Frequently Asked Questions
What is the average machine learning engineer salary in 2026?
In 2026, the average ML engineer base salary in the US ranges from $128,000 to $186,000, depending on the source and employer population measured. Total compensation including equity and bonuses averages $212,022 (Built In) to $265,000 (Levels.fyi). Senior engineers at top tech companies, Meta, Google, OpenAI — can exceed $400,000–$786,000 in total comp.
How much do machine learning engineers make at Google and Meta?
At Google, ML engineer total compensation ranges from $199K (junior, L3) to $743K (principal, L7), with a median of $290K. At Meta, the range is $187K (E3) to $786K (E6), with a median of $450K. Both figures include base salary, stock grants, and annual bonuses, per Levels.fyi updated May 2026.
Do machine learning engineers make more than software engineers?
Yes, by a significant margin. The BLS median for software developers is $133,080. ML engineers average $161K–$186K base in the same market. At the staff/principal level, the AI premium reaches 18.7% over non-AI peers. Specialists in LLM fine-tuning earn 40–60% above baseline ML salaries.
What machine learning skills pay the most in 2026?
LLM fine-tuning commands the highest premium: 40–60% above base ML salaries ($56K–$110K additional). MLOps expertise adds 25–40% ($35K–$74K). NLP adds 20–35%. Generative AI and RAG architecture are the fastest-rising skills. ML Research Scientists command the highest ceiling, averaging $226,353, with top labs offering $550K+ total comp.
What is the machine learning engineer salary in the UK vs. USA?
The gap is stark. UK ML engineers average £76,198/year (~$97K USD), per Indeed UK (May 2026, 811 salaries). In the US, the average is $161K–$186K base, roughly double the UK figure. Senior US roles at FAANG clear $300K–$700K+ total comp. Switzerland leads Europe at ~$160K USD. Canada averages ~$130K USD.
Is machine learning engineering a good career in 2026?
By most metrics, yes. The BLS projects 26% job growth for the closest occupational category through 2034; data scientists are the 4th fastest-growing occupation in the US economy. AI/ML postings were up 163% year-over-year in 2025. Demand outstrips supply 3.2:1. The two real risks: skill obsolescence as the field evolves rapidly, and role-title inflation that makes it harder to signal genuine expertise.
What You Now Know That Most People Don’t
The ML engineer salary story in 2026 isn’t “AI pays well.” That’s a headline. The real story is about structure: a market where the average is nearly meaningless without context, where the gap between a generalist and an LLM specialist is $56K–$110K, where the US salary is roughly double the UK’s, and where the supply-demand imbalance isn’t a hype cycle, it’s a documented 3.2:1 ratio that’s been consistent for multiple years.
The forward implication for the next 6–18 months: the era of “any ML experience commands a premium” is ending. The era of “demonstrable production experience in specific high-value skills” is in full effect. Engineers with provable LLM fine-tuning and RAG deployments will continue to see premiums. Generalist ML engineers who haven’t specialized, particularly those without frontier model experience, may find the Glassdoor salary decline data more predictive than the Levels.fyi headline numbers.
Three things to watch:
Credential inflation at research labs. PhD demand in ML job postings jumped 6% in 2026. If you’re targeting frontier labs, the academic track matters more than the “just ship it” narrative suggests.
Remote-first US employer expansion. The US/UK and US/Australia salary gaps are the single biggest financial arbitrage opportunity for international ML engineers. Watch for US companies formalizing remote hiring for senior roles.
The productivity ROI test. Enterprise AI spending is enormous. If it doesn’t produce measurable productivity returns at scale through 2025–2026, the hiring frenzy that’s inflating mid-market ML salaries could correct. The signal to watch: Fortune 500 renewal rates on AI contracts.
Stay ahead of the market.
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How Agentic AI Works: The Architecture Behind Autonomous AI in 2026 | NeuralWired
Agentic AI · 2026
How Agentic AI Actually Works | And Why Most Companies Are Getting It Wrong
Agentic AI is no longer a research topic, it’s running in production at Capital One, Fountain, and dozens of enterprises you’ve heard of. Here’s the real architecture: the ReAct loop, multi-agent orchestration, the security vulnerabilities already being exploited, and why Yann LeCun thinks the whole approach is fundamentally broken.
NeuralWired Research Team·May 2026·Deep Explainer · 14 min read
A hiring platform called Fountain quietly rewired its recruitment pipeline last year. No fanfare. No press release about “AI transformation.” Just a hierarchical multi-agent system handling candidate screening end-to-end, and the results were stark: 50% faster screening, 2x candidate conversions, staffing cycles compressed to under 72 hours. Humans stayed in the loop for final decisions. Agents did everything else.
That’s agentic AI in its most useful form. Not a chatbot. Not autocomplete at scale. A system that perceives, reasons, acts, observes the result, and iterates, autonomously, until a goal is achieved.
The market is pricing this in fast. The AI Agents market was valued at $7.84 billion in 2025 and is projected to reach $52.62 billion by 2030, a 46.3% CAGR. Vertical agents, domain-specific systems for legal, healthcare, and financial services, are the fastest-growing segment at 62.7% CAGR. But the gap between the hype and what’s actually running in production is significant. Understanding why requires understanding how agentic AI actually works.
What Agentic AI Actually Is
Start with the distinction that matters most to anyone building or buying this technology: agentic AI is not generative AI with more confidence. It’s a categorically different architecture.
Generative AI, the ChatGPT most people know, operates in a single pass. Prompt in, response out. It’s reactive by design. Agentic AI systems do something fundamentally different: they plan multi-step tasks, use external tools (APIs, browsers, databases, code executors), take actions in the world, and iterate until a goal is achieved with minimal human input.
Working Definition
An AI agent is a system that can execute multi-step plans, use external tools, and interact with digital environments, functioning as an autonomous component within larger workflows rather than a single-turn responder. The key distinction from a chatbot is autonomy and action.
“AI agents can execute multi-step plans, use external tools, and interact with digital environments to function as powerful components within larger workflows.”
— Kate Kellogg, Professor of Management and Innovation, MIT Sloan School of Management
Four capabilities define the current generation of agentic systems, and distinguish them from everything that came before. Autonomy: operating without continuous human intervention. Goal-oriented behavior: adapting strategies as conditions change mid-task. Reasoning and planning: breaking complex problems into multi-stage solutions. Learning and adaptation: improving based on outcomes and feedback within a session or across sessions.
The ReAct Loop: The Engine Inside Every Agent
If you want to understand how agentic AI works at a technical level, you need to understand one paper from October 2022: the ReAct framework, introduced by Shunyu Yao and a team at Princeton and Google Brain. It is the architectural backbone of virtually every production agentic system shipping in 2026.
ReAct stands for Reasoning + Acting. The insight is deceptively simple: instead of generating a single response to a prompt, an agent alternates between two modes. It reasons about what to do. Then it acts, calling a tool, querying a database, executing code. Then it observes the result of that action. Then it reasons again, informed by what it just saw. Then it acts again. This loop continues until the task is done.
Written out as a sequence, a ReAct agent operating on a research task looks like this:
Step
Mode
What happens
1
Perceive
Receive task input — user goal, context, available tools
2
Reason
Language model generates a plan: “I should search for X, then check Y”
3
Act
Call a tool — web search, API, code executor, database query
4
Observe
Tool returns a result; agent sees the output
5
Reason
Update the plan based on what was observed
6
Act / Complete
Take next action, or conclude if goal is met
What makes this powerful is also what makes it dangerous: the loop runs until the model decides it’s done. A poorly constrained agent will keep acting. This is why a mature pattern that solidified in 2026 is the tiered constraint model, explicit priority layers baked into every agent’s operating instructions:
Safety first — never take destructive or irreversible actions without human confirmation
Accuracy — prioritize correct outputs over speed
Goal completion — achieve the stated objective
Efficiency — accomplish the above with minimum steps
Goals conflict constantly in complex tasks. Explicit priority ordering resolves them deterministically rather than leaving the model to improvise, which it will, unpredictably, without this structure.
Multi-Agent Systems and Orchestration
A single agent can handle impressive tasks. But the frontier of enterprise agentic AI is multi-agent systems, networks of specialized agents coordinating to complete work that would overwhelm any individual model.
Gartner reported a 1,445% increase in multi-agent system inquiries from Q1 2024 to Q2 2025. That’s not gradual adoption, that’s a category inflection point.
The architectural pattern that’s emerging: a hierarchical model with a planning agent (sometimes called an orchestrator) at the top that breaks down a complex goal and delegates sub-tasks to specialized worker agents. Each worker has access to specific tools. Results flow back up to the orchestrator, which synthesizes them and decides the next move. Human oversight can be plugged in at any tier.
The Interoperability Problem | and How It’s Being Solved
Until recently, every multi-agent system required bespoke integrations for every tool and data source an agent might need. That’s changing fast. Two standards are converging:
Protocol
Creator
What It Does
Analogy
MCP (Model Context Protocol)
Anthropic
Standardizes how agents connect to tools, APIs, and data sources
USB for AI peripherals
A2A (Agent-to-Agent Protocol)
Google
Standardizes how agents communicate with each other
HTTP for agent networks
Anthropic launched MCP in November 2024 and it has since become the de facto standard for agent-tool connectivity. Our read: these two protocols complementing each other, one for tool access, one for agent communication, signals the industry is building toward an interoperability layer that will dramatically reduce the cost of deploying production agent systems. That’s a structural accelerant for adoption.
The key enterprise milestones from the past 18 months:
Oct 2022
ReAct framework published, Yao et al., Princeton/Google Brain. Still the foundational architecture for virtually every production system.
Nov 2024
Anthropic releases MCP, Open standard for agent-tool connectivity. Becomes the de facto infrastructure layer.
Jul 2025
OpenAI launches ChatGPT Agent Transitions ChatGPT from conversational tool to autonomous assistant.
Sep 2025
Anthropic releases Claude Agent SDK Alongside Claude Sonnet 4.5. Developers can now build fully autonomous AI systems on top of Claude.
Jan 2026
Claude 4.5 hits 60%+ on OSWorld Computer-use benchmark. Up from single-digit performance in the pre-agentic era. A meaningful reliability milestone.
Apr 2026
Anthropic launches Claude Managed Agents Abstracts infrastructure for production agent deployment. Reduces the engineering overhead of scaling.
The Production Reality: Numbers That Matter
Here’s the adoption picture, stripped of the optimism that characterizes most analyst reports:
88%
of organizations use AI in at least one function (McKinsey, 2025)
6%
qualify as high performers generating 5%+ EBIT impact
11%
actively use agentic AI in production (Deloitte, 2025)
40%+
of agentic AI projects predicted scrapped by 2027 (Gartner)
The gap between “using AI” and “generating measurable business impact from AI” is enormous. McKinsey’s 2025 State of AI survey (1,993 participants across ~105 countries) found only 23% of enterprises are scaling AI agents in at least one function. Most organizations remain in what researchers are calling “pilot mode”, impressive demos, no scaled deployment.
“We have agents deployed at scale in the economy to perform all kinds of tasks.”
— Sinan Aral, Professor of Management, Information Technology, and Marketing, MIT Sloan School of Management
Aral is right, but the qualifier matters. Agents are deployed at scale in the economy. They are not deployed at scale in most individual enterprises. The difference is significant for anyone making architecture decisions right now.
The 80% Problem
MIT’s Kellogg documented something that should be required reading for every CTO considering an agentic AI deployment: in a real project deploying an AI agent to detect adverse events among cancer patients, 80% of the total work was consumed by data engineering, stakeholder alignment, governance, and workflow integration. Not the AI itself. Not the model. The boring, unglamorous, deeply human work of making organizations ready for autonomous systems.
The demos are compelling. The production path is brutal. Expect it.
Security, Failure Modes, and What Can Cascade
Multi-agent systems introduce failure modes that don’t exist in single-model deployments. The most dangerous: cascading errors. One agent’s hallucination becomes another agent’s input. A judge-agent reviewing another agent’s output can hallucinate or act deceptively, undermining the very validation layer it was designed to provide. The safeguard inherits the failure mode it was meant to catch.
⚠ Critical Security Risk
In mid-2025, the EchoLeak exploit (CVE-2025-32711) demonstrated the real attack surface of agentic systems: infected emails containing engineered prompts could trigger Microsoft Copilot to exfiltrate sensitive data automatically, without any user interaction. This is prompt injection at scale. It requires no user error. It exploits the agent’s autonomy directly.
Symantec’s controlled experiments using OpenAI’s Operator AI agent went further, demonstrating how agents could be directed to harvest personal data and automate credential stuffing attacks. These are not theoretical threat models. They’ve been demonstrated against production systems.
What specifically can go wrong in enterprise deployments:
Data breach via autonomous action, In early 2025, a healthtech firm disclosed a breach compromising records of 483,000 patients, caused by a semi-autonomous AI agent that pushed confidential data into unsecured workflows while streamlining operations.
Compliance cascade, A single hallucination — an agent misclassifying a transaction, can propagate across linked systems and agents, producing compliance violations or financial misstatements that are expensive to unwind.
Shadow agent sprawl, McKinsey (2025) warned that uncontrolled agent proliferation is emerging as a risk equivalent to shadow IT. MIT’s NANDA Initiative found 95% of enterprise GenAI pilots failed to deliver measurable ROI, with uncontrolled agent proliferation cited as a major contributor.
Deloitte’s 2026 State of AI in the Enterprise report found only one in five companies has a mature model for governance of autonomous AI agents. That’s not a nice-to-have gap. That’s an existential liability for any organization running agents with write, execute, or transact permissions.
What CTOs Must Do Now
Mandate human-in-the-loop checkpoints for any agent with write, execute, or transact permissions before production deployment.
Audit data pipelines before agent integration, converting data into standard, structured formats is prerequisite infrastructure, not a parallel workstream.
Build agent registries, track lifecycle, owners, and KPIs before authorizing new deployments. “Shadow agent sprawl” is a real and growing risk.
The Strongest Case Against the Whole Approach
The most technically serious challenge to the mainstream agentic AI narrative doesn’t come from a competitor or a skeptical analyst. It comes from Yann LeCun, VP and Chief AI Scientist at Meta, Turing Award winner, and one of the most credentialed AI researchers alive.
LeCun’s argument is architectural, not operational. It goes to the foundation of how current LLM-based agents work.
“An agentic system that is supposed to take actions in the world cannot work reliably unless it has a world model to predict the consequences of its actions. Without it, the system will inevitably make mistakes. This is the key to unlocking everything from truly useful domestic robots to Level 5 autonomous driving.”
