DEV Community: Cedric Sebastian

AlphaEvolve: Evaluator Harnesses, JAX, and the Boundaries of Autonomous Algorithm Discovery

Cedric Sebastian — Tue, 01 Sep 2026 02:23:36 +0000

AlphaEvolve shows that LLM-guided evolutionary search can produce improved algorithms when candidate quality can be evaluated automatically. Its limits reveal an equally important lesson: an autonomous search system can only optimize for what its evaluator can recognize.

In 2025, Google DeepMind reported that AlphaEvolve found a procedure for multiplying two 4 × 4 complex-valued matrices with 48 scalar multiplications, one fewer than two recursive levels of Strassen’s method. The one-multiplication reduction made the headline. The search loop that found it explains why the result matters beyond matrix multiplication.

Now, we will explore AlphaEvolve to better understand it.

What is AlphaEvolve?

AlphaEvolve is an autonomous evolutionary AI coding agent developed by Google DeepMind that combines language-model proposals with evolutionary search. It uses two of Google’s frontier Gemini models in complementary roles:

Gemini Flash generates high-throughput proposals, expanding the breadth of ideas explored.
Gemini Pro adds greater depth through occasional suggestions from the more capable model.

The paper does not disclose the exact sampling mixture.

AlphaEvolve illustrates how AI has evolved from producing generic, repetitive output to building advanced systems that pair large language models (LLMs) with evolutionary-search harnesses.

The word “autonomous” needs a clear boundary. Humans define the problem, provide the initial program and evaluation code, supply optional context, and mark the code regions that AlphaEvolve may modify. The system then conducts a sustained search within those constraints. This goes well beyond asking a model for a single answer, but it does not make AlphaEvolve an independent scientist who chooses questions or standards of evidence.

AlphaEvolve extends FunSearch, DeepMind’s earlier system for evolving a selected function within a human-written program. AlphaEvolve can modify much larger regions of code, optimize multiple scores simultaneously, and run evaluations for hours in parallel. The expansion is substantial, but the underlying pattern is inherited: language-model variation followed by executable selection.

How does it work?

AlphaEvolve advanced outline. The diagram illustrates how its process works as explained below.

The loop could be demonstrated like this:

User input A user supplies an existing algorithm or program, evaluation code, and optional context about the problem.
Prompt sampler A prompt sampler then selects previous candidates from a program database and gives them to the Gemini models for revision.
Gemini models Create new candidates by proposing code changes, usually returning patches instead of rewriting the whole program.
Evaluator pool Multiple candidates can be evaluated in parallel. Inexpensive checks can filter out invalid or poor candidates before more costly evaluations are run. The evaluation code and the environment in which it runs together form the evaluator harness. This harness determines how each candidate is executed, tested, timed, and scored.
Program database Candidates that receive strong evaluation scores are returned to the database, where they can be chosen again in further generations. The model provides variation by changing the code, while the evaluator provides the selection pressure that determines which changes remain.

AlphaEvolve generates and modifies programs, runs them through an automated evaluator, retains promising candidates, and uses their performance to guide the next generation. This process lets the system evaluate far more candidate programs than we could explore manually on a scale humans can’t examine without sacrificing accuracy, although the reliability of that search still depends on what the evaluator measures.

AlphaEvolve revolutionizes how we work by letting us formulate problems, design evaluators to test them, and constrain the search space. The evolutionary system decides which candidates survive, and human researchers decide whether the resulting score reflects meaningful progress. The evaluator is only one part of the system . In other words, the evaluator matters, but it’s only one part of a much larger human-guided system. AlphaEvolve’s limited ablation studies suggest that its performance depends on several factors, including model capability, evolutionary selection, prompt adaptation, available context, and how much of the code the system is allowed to modify. While the overall loop may appear straightforward, it is supported by several interacting components that surprisingly make the system more complex in practice (Novikov et al., 2025). For the results to be reliable, these mechanisms also need to be paired with a well-designed evaluator.

Results with evidence level distinction

AlphaEvolve’s public record contains three kinds of evidence: artifacts outsiders can verify, production measurements reported by Google, and later work that extends an artifact without reproducing the original search.

Keeping these categories separate prevents a verified mathematical construction from giving proprietary deployment claims more certainty than they warrant.

Demonstrated and checkable. DeepMind released an exact rank-48 decomposition and verification code for multiplying 4 × 4 matrices over complex coefficients. Here, rank counts the bilinear scalar multiplications in the construction. It does not measure wall-clock speed. Additions, memory traffic, numerical stability, coefficient arithmetic, and target hardware can outweigh the benefit of saving one multiplication.

DeepMind initially described the result as the first improvement in 56 years over the complex rank-49 baseline associated with Strassen’s 1969 method. The publication record complicates that priority claim. In 2024, Kaporin reported a numerical complex rank-48 scheme. AlphaEvolve’s contribution is better described as an exact, publicly checkable construction found by the system. Dumas et al. (2025) then transformed that artifact into a 48-multiplication algorithm with rational coefficients. Their work corroborates and extends the construction, but it does not reproduce DeepMind’s search system, which remains closed.

Developer-reported. Google reports that an AlphaEvolve scheduling heuristic, first validated in simulation and then deployed, recovers an average of 0.7% of its worldwide compute resources. The white paper also reports an average 23% speedup across evaluated Pallas matrix-multiplication kernel shapes and about a 1% reduction in Gemini training time. For the kernel study, realistic input shapes were divided between optimization and evaluation sets and measured on TPUs. This provides a useful generalization check, but the workloads, raw timing records, and production environment remain proprietary. These figures are therefore first-party measurements rather than independently audited results.

The same qualification applies to DeepMind’s report that AlphaEvolve matched the best-known construction on roughly 75% of more than 50 mathematical problems and improved on it in roughly 20%. The public report does not fully document the problem-selection protocol or provide a precise denominator. In a May 2026 update, DeepMind reported that AlphaEvolve had become a regular tool for designing next-generation TPUs and had produced a Spanner heuristic that cut write amplification by 20%. These claims came after the 2025 white paper and, like the earlier figures, have not been independently audited.

System Constraints & Bottlenecks

AlphaEvolve’s main boundary is automated evaluability. Tasks that require manual experiments, subjective judgment, or evidence unavailable to the harness do not fit the current loop. Even machine-gradeable tasks can fail in quieter ways. A proxy may reward the wrong behavior, public tests may invite overfitting, omitted edge cases may hide invalid programs, and noisy timing may favor a candidate that will not survive a different device or compiler version. Multi-objective scores add another judgment call because their weights determine what the search may sacrifice.

The concern is empirical, not merely philosophical. In a small 2026 circle-packing preprint (Ishibashi et al., 2026) found generated programs that exploited weaknesses in the evaluator and reported more such hacks from the more capable model they tested. The study examined another harness on one benchmark, so it is evidence of a general failure mode, not an audit of AlphaEvolve’s published results.

A stronger harness would combine exact validity checks where possible with hidden holdouts, adversarial cases, repeated measurements, cross-device tests, versioned environments, independent reimplementation, and human inspection of finalists. These controls raise costs. That cost limits how many candidates can be evaluated and which problems are economically searchable. Model quality, context, representation, compute budget, and editable scope remain bottlenecks even when the evaluator is sound.

Human Adaptation, Decision, and Judgement

Many people are afraid of AI because its capabilities have advanced far faster than expected, but fear will never help us. We should adapt, learn, and use AI as a collaborative brainstorming partner that helps us think better and strengthen our arguments. One-sided perspectives can create new mistakes even as we try to solve existing ones.

AlphaEvolve makes this distinction especially clear. Human judgment enters both before and after the search. Researchers choose the objective, decide what code may change, construct the evaluation environment, and set acceptable constraints and tradeoffs. Once the search is complete, people still have to determine whether the winning program is valid, robust, deployable, and scientifically meaningful, while considering the consequences of putting it into practice. AlphaEvolve itself relies on automated evaluators to score proposed programs, making human choices about objectives and evaluation central to what the system ultimately discovers.

A benchmark score cannot make those decisions. The evidence therefore supports treating AlphaEvolve as a research instrument that operates under human scientific judgment, not as a replacement for it.

Its value lies in expanding the range and persistence of algorithmic search, while responsibility for defining the problem, interpreting the results, and deciding what counts as a meaningful discovery remains with people.

What JAX changes

JAX can reduce the cost of repeated numerical evaluation. It cannot determine whether the evaluator represents the right scientific objective.

That distinction separates AlphaEvolve’s documented JAX use from broader hypotheses about what the pairing might eventually enable.

JAX already appears in the published work. AlphaEvolve used JAX and Optax while evolving tensor-decomposition search code, and it optimized a tiling heuristic for a Pallas kernel used in Gemini training. Pallas is an experimental JAX kernel language that gives programmers fine-grained control over code for GPUs and TPUs, including data movement through fast on-chip memory.

Three JAX transformations are especially relevant:

jax.jit traces and compiles compatible numerical functions for a target device. Repeated calls can reuse the compiled executable when input types and relevant static arguments remain compatible.
jax.vmap maps one function across a batch axis. It can batch evaluations over inputs or parameter settings, but it does not merge structurally different candidate programs into one computation.
jax.grad differentiates scalar-output functions. It could support continuous optimization inside a candidate when the inner objective is differentiable, but it does not differentiate AlphaEvolve's discrete outer search.

During compilation, JAX traces a compatible Python function into an internal representation called a jaxpr, lowers it to StableHLO, and passes the lowered program to XLA to produce an executable for a CPU, GPU, or TPU. A compiler intermediate representation is simply a structured form between source code and machine instructions that allows compilers to analyze and transform it.

This stack makes a broader research program both feasible and practical. AlphaEvolve could propose new kernels, solver components, optimizers, or training-loop components, while JAX could compile and vectorize repeated evaluations for efficient execution on accelerators. For promising candidates, automatic differentiation could help tune internal parameters by supplying gradients to an optimizer, provided the inner objective is differentiable and scalar-valued. This remains a hypothesis about how the search space could be expanded, not a demonstrated path toward general intelligence.

The economics depend on how similar the candidates are. Repeated evaluations of the same jitted candidate can amortize compilation costs, while changes to the code, shapes, dtypes, or static parameters may require new tracing and compilation. Structurally heterogeneous programs may therefore erase the throughput advantage. A credible experiment would also evaluate correctness, speed, memory use, numerical stability, and behavior across unseen data, shapes, devices, and compiler versions.

AlphaEvolve combined with JAX could broaden the practical search space for numerical components when compilation costs can be amortized. That same acceleration makes evaluator quality even more consequential:

A flawed harness can reward the wrong behavior at a greater scale.

This strengthens the case for automated algorithm search, but it is not evidence of autonomous science without human control.

References

AlphaEvolve Team. (2025, May 14). AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms. Google DeepMind. https://deepmind.google/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

AlphaEvolve Team. (2026, May 7). AlphaEvolve: How our Gemini-powered coding agent is scaling impact across fields. Google DeepMind. https://deepmind.google/blog/alphaevolve-impact/

Dumas, J.-G., Pernet, C., & Sedoglavic, A. (2025). A non-commutative algorithm for multiplying 4x4 matrices using 48 non-complex multiplications (arXiv:2506.13242, Version 7) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2506.13242

Google DeepMind. (2025). Results of AlphaEvolve [Data set and computer software]. GitHub. https://github.com/google-deepmind/alphaevolve_results

Ishibashi, Y., Yano, T., & Oyamada, M. (2026). Effective harness engineering for algorithm discovery with coding agents (arXiv:2605.15221, Version 1) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2605.15221

JAX Authors. (n.d.-a). Ahead-of-time lowering and compilation. JAX documentation. Retrieved August 29, 2026, from https://docs.jax.dev/en/latest/aot.html

JAX Authors. (n.d.-b). Pallas: Custom kernels in JAX. JAX documentation. Retrieved August 29, 2026, from https://docs.jax.dev/en/latest/401/pallas.html

JAX Authors. (n.d.-c). Transformations: grad and vmap. JAX documentation. Retrieved August 29, 2026, from https://docs.jax.dev/en/latest/101/transformations.html

Kaporin, I. E. (2024). Finding complex-valued solutions of Brent equations using nonlinear least squares. Computational Mathematics and Mathematical Physics, 64(9), 1881–1891. https://doi.org/10.1134/S0965542524701021

Novikov, A., Vũ, N., Eisenberger, M., Dupont, E., Huang, P.-S., Wagner, A. Z., Shirobokov, S., Kozlovskii, B., Ruiz, F. J. R., Mehrabian, A., Kumar, M. P., See, A., Chaudhuri, S., Holland, G., Davies, A., Nowozin, S., Kohli, P., & Balog, M. (2025). AlphaEvolve: A coding agent for scientific and algorithmic discovery [White paper]. arXiv. https://doi.org/10.48550/arXiv.2506.13131

Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., Kohli, P., & Fawzi, A. (2024). Mathematical discoveries from program search with large language models. Nature, 625(7995), 468–475. https://doi.org/10.1038/s41586-023-06924-6

The Most Underrated Infrastructure Shift of the Year: Google Cloud’s New Serverless

Cedric Sebastian — Thu, 30 Apr 2026 04:28:52 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

Topic: Cloud Run’s new serverless primitives—Cloud Run Instances, Cloud Run Sandboxes, and Ephemeral Disk Storage—announced at Google Cloud Next ’26, plus native integration with the Gemini Enterprise Agent Platform via the Model Context Protocol (MCP).

Format: Opinion Piece

Introduction: The Announcement Nobody Is Talking About

Google Cloud Next ’26 produced 260 announcements [1]. Predictably, the headlines went to the Gemini Enterprise Agent Platform—the rebranded, expanded successor to Vertex AI—alongside eighth-generation TPUs, the Agentic Data Cloud, and the Wiz-powered Agentic Defense story [2]. Thomas Kurian’s keynote framed the entire conference around a singular, aggressive thesis: the agentic enterprise is here, and Google Cloud is its operating system [6].

Buried beneath that flashy narrative, however, was a set of infrastructure announcements that will easily outlast the keynote buzz. Google quietly introduced three new Cloud Run primitives that fundamentally redefine what serverless means on GCP. Alongside these, the platform revealed a tight, native integration with the Gemini Enterprise Agent Platform via the Model Context Protocol (MCP) [6]. Together, these updates transform Cloud Run from a passive HTTP host into a stateful, secure, agent-first compute fabric.

The developer community has largely ignored these primitives in favor of the Gemini spectacle. That is a massive mistake. This evolution addresses the single greatest barrier to production-grade AI agents—trustworthy, isolated, and persistent execution—in a way no hyperscaler has delivered before. If developers overlook these primitives now, they will spend the next two years retrofitting architectures that could have been built correctly from day one.

The Problem: Why “Traditional” Serverless Failed the Agentic Era

Before examining the new primitives, we have to understand the architectural gap they fill. Cloud Run established itself as the gold standard for stateless, request-driven compute, abstracting away the Kubernetes control plane while preserving Knative’s operational simplicity. But this model was designed for a world of synchronous HTTP requests, not for AI agents that:

Run continuously for hours.
Maintain complex conversational state.
Execute dynamically generated, untrusted code.
Demand persistent, local scratch space.

The industry workarounds had become folklore. Developers pinged their own endpoints to keep containers warm. They massively over-provisioned RAM because the local filesystem was an in-memory tmpfs—meaning if you staged a 2 GB dataset, you consumed 2 GB of your container’s memory, often triggering an OOM (Out of Memory) kill.

For anything more complex, teams begrudgingly fell back to GKE clusters or VMs. These weren’t edge cases; they were the default, broken patterns for agentic workloads. One Chinese developer perfectly summarized the prevailing anxiety in the community:

“每次赋予代理真实工具权限（不是玩具演示），都会感受到一种‘无法摆脱的低级恐惧’”
— “Every time you give an agent real tool permissions (not a toy demo), you feel an inescapable, primal fear.” [8]

Giving an LLM the keys to a shell and hoping it behaves isn’t security; it’s a prayer. This is exactly the crisis Google’s new Cloud Run primitives solve.

The Four Primitives That Change Serverless

Google announced four interconnected capabilities at Next ’26. Here is a breakdown of what they are and why they matter.

The Architectural Shift at a Glance

Capability	Traditional Serverless Model	Next '26 Cloud Run Primitives
Compute	Scale-to-zero, request-driven	Instances: Guaranteed, long-lived background processing
Security	Basic container-level isolation	Sandboxes: Zero-trust, gVisor-backed micro-VMs
Storage	In-memory `tmpfs` (The "RAM Tax")	Ephemeral Disk: Dedicated NVMe block storage
Orchestration	Custom API middleware	MCP Integration: Native, IAM-authenticated agent scaling

1. Cloud Run Instances (Preview): Guaranteed Baseline Compute

Historically, Cloud Run’s scale-to-zero model left a frustrating gap for architectures requiring continuous background processing or persistent daemons. Cloud Run Instances bridge that gap by provisioning long-lived, dedicated containers that do not scale to zero [3].

From a systems-engineering perspective, this is the most critical primitive. It provides a native serverless abstraction for continuous event backbones without relying on HTTP concurrency hacks. The official blog demonstrated a single command to provision such an instance with a Cloud Storage volume mount:

gcloud run instances create \
  --image alpine/openclaw:latest \
  --port 18789 \
  --memory 4Gi \
  --default-url \
  --add-volume mount-path=/home/node/.openclaw,type=cloud-storage,bucket=$BUCKET_NAME

Developers no longer need to choose between the operational simplicity of serverless and the continuous persistence requirements of stateful workloads.

2. Cloud Run Sandboxes (Preview): Zero-Trust Execution

When building systems that dynamically generate and execute code, you are dealing with fundamentally untrusted workloads. Cloud Run Sandboxes expose gVisor-backed, micro-VM isolation as a configurable developer primitive, allowing you to spin up execution environments with granular egress and syscall restrictions [4].

This is the exact same isolation technology Google uses to protect Gemini itself. The performance characteristics are staggering: 300 sandboxes launched per second per cluster, sub-second time-to-first-instruction, and stateful session persistence [5]. Prior approaches forced a trade-off: sacrifice isolation (shared storage) or sacrifice continuity (reconstructing the environment for every call). Cloud Run Sandboxes maintain strict boundaries while preserving session state—acting as a "memory-equipped prison" for autonomous code execution.

3. Ephemeral Disk Storage (Preview): Breaking the RAM Tax

This primitive will immediately improve the quality of life for developers doing data-intensive work. Previously, staging large datasets meant paying a massive "RAM tax" due to the tmpfs filesystem.

Ephemeral Disk Storage attaches dedicated, local, high-speed block storage (NVMe-backed) directly to your instances [3]. This storage dies with the instance, but crucially, it does not consume your memory allocation. A Kafka consumer buffering gigabytes of streaming data can now write to local NVMe rather than holding it in memory, avoiding OOM kills entirely [4].

4. Gemini Enterprise Agent Platform Integration via MCP (Preview)

Google tightly integrated Cloud Run with the Gemini Enterprise Agent Platform using the Model Context Protocol (MCP). This allows multi-agent systems to securely interface with internal infrastructure without building custom authentication middleware; the transport layer is natively authenticated via Google Cloud IAM [4].

This elevates Cloud Run from a mere hosting target to a cognitive capability. An agent equipped with an MCP-mapped skill can dynamically act as its own platform engineer—provisioning an Instance for a background daemon or a Sandbox for safe code execution on the fly.

Production Patterns: What This Actually Enables

Architectural features are only as valuable as the systems they enable. Here are four production patterns these primitives unlock [4]:

Continuous Stateful Ingestion: Combine Instances and Ephemeral Disk to deploy a horizontally scalable Kafka consumer group entirely on serverless. Instances maintain uninterrupted connections; NVMe buffers the payloads.
Autonomous Zero-Trust SOC Analyst: Security agents often need to execute dynamic scripts to analyze suspicious binaries. Cloud Run Sandboxes ensure this execution occurs within an isolated gVisor micro-VM with restricted network egress, ensuring a zero-blast radius.
Agentic Firewall Configuration Loops: An Instance can run continuously to tail telemetry logs. Upon detecting an anomaly, it triggers a Gemini Agent via MCP, which securely navigates the firewall architecture via IAM and updates blocklists in real time.
The Agentic Platform Engineer: A local agent equipped with an MCP-mapped Skill can dynamically orchestrate its own Instances, Sandboxes, and Ephemeral Disks to solve complex problems, provisioning its own compute resources in real time.

The Critique: What’s Missing and What to Watch

An honest evaluation requires acknowledging the gaps:

Preview Gating: The most valuable primitives are preview-only and limited to select customers. Developers must weigh the risk of betting on preview APIs against immediate architectural benefits.
Vendor Coupling Risk: The MCP integration is deeply tied to Google Cloud IAM. While the A2A (Agent-to-Agent) protocol and ADK (Agent Development Kit) are open-source, the managed hosting layer and Agent Registry are heavily coupled to GCP [5].
Observability Maturity: Debugging what happened inside an ephemeral gVisor container requires a mature observability pipeline. While ADK 1.0’s OpenTelemetry integration is encouraging, the sandbox-specific tracing story needs clearer documentation.
The Competitive Landscape: AWS Lambda and Azure Container Apps haven't responded with equivalent primitives yet—but they will. Google’s gVisor-backed serverless sandboxing is a massive first-mover advantage, but it is a window of opportunity, not an impenetrable moat.

Conclusion: The Foundation, Not the Fireworks

The Gemini Enterprise Agent Platform is the user-facing story of Next ’26. But platforms need foundations, and Cloud Run’s new primitives are precisely that: the infrastructure layer that makes agentic workloads production-grade rather than a flimsy proof-of-concept.

My recommendation to the developer community is straightforward: look past the keynote pyrotechnics. Experiment with Cloud Run Instances for your stateful workloads. Evaluate Cloud Run Sandboxes as the execution boundary for any agent running untrusted code. Plan your capacity models around Ephemeral Disk rather than RAM over-provisioning.

The agentic era demands infrastructure that is stateful, secure, and highly composable. Google shipped exactly that at Next ’26. The rest of us just haven’t noticed yet.

References

[1] Google Cloud, “260 things we announced at Google Cloud Next ’26 – a recap,” Google Cloud Blog, Apr. 25, 2026. [Online]. Available: https://cloud.google.com/blog/topics/google-cloud-next/google-cloud-next-2026-wrap-up

[2] Google Cloud, “Welcome to Google Cloud Next ‘26,” Google Cloud Blog, Apr. 23, 2026. [Online]. Available: https://cloud.google.com/blog/topics/google-cloud-next/welcome-to-google-cloud-next26

[3] S. Giannini and B. Runkle, “What’s new in Cloud Run at Next ‘26,” Google Cloud Blog, Apr. 23, 2026. [Online]. Available:

What’s new for Cloud Run at Next ‘26 | Google Cloud Blog

Cloud Run updates at Next include integrations with Google AI Studio and Gemini Enterprise Agent Platform plus support for NVIDIA Blackwell GPUs.

cloud.google.com

[4] Google Cloud Developer Advocates, “Stateful Serverless: The Architectural Evolution of Cloud Run for the Agentic Era,” Medium, Apr. 28, 2026. [Online]. Available: https://medium.com/google-cloud/architectural-evolution-of-cloud-run-for-the-agentic-era-28af995cecb0

[5] R. Bobade, “I Looked Past the Keynote Hype: Why A2A + ADK Is the Real Story of Google Cloud NEXT ’26,” dev.to, Apr. 23, 2026. [Online]. Available:

Google Cloud NEXT '26 Challenge Submission

Rushikesh Bobade

Apr 23

I Looked Past the Keynote Hype: Why A2A + ADK Is the Real Story of Google Cloud NEXT '26

#devchallenge #cloudnextchallenge #googlecloud #ai

10 min read

[6] M. Gerstenhaber and M. Bachman, “Introducing Gemini Enterprise Agent Platform, powering the next wave of agents,” Google Cloud Blog, Apr. 23, 2026. [Online]. Available: https://cloud.google.com/blog/products/ai-machine-learning/introducing-gemini-enterprise-agent-platform

[7] Google Cloud, “Day 1 at Google Cloud Next ‘26 recap,” Google Cloud Blog, Apr. 23, 2026. [Online]. Available: https://cloud.google.com/blog/topics/google-cloud-next/next26-day-1-recap

[8] “每秒300个沙盒：Google把AI代理的‘定时炸弹’拆了,” 163.com, Apr. 30, 2026. [Online]. Available: https://www.163.com/dy/article/KRNLIEBV05561FZW.html

Decision Is All You Need: Lifelong Learning or Lifelong Guilty

Cedric Sebastian — Mon, 22 Dec 2025 20:44:32 +0000

print("Decide and Build your future, not accept.")

It’s been long time since the previous story uploaded, thanks.

This time we will learn about Lifelong Learning, empowering Google Skill for Lifelong Learning, and we become free from Lifelong Guilty.

Disclaimer: Reading this blog make you become future-ready being. This blog consists of motivational, philosophy, and psychology.

“It’s what you do right now that makes a difference.”

— Black Hawk Down

Introduction: The Reality

Have you ever read or seen a post of people who talk about Lifelong learning like this?

Post by Eva Kozma on LinkedIn

That's correct. L ifelong learning is the act of learning throughout one's entire life. These align with Albert Einstein's quotes.

“Education is what remains after one has forgotten what one has learned in school.”

― Albert Einstein

Imagine if the picture below became reality:

The introvert with AI can reach further than those who do not use AI.

The leader with AI capable of reaching another country.

While the boss with AI is only able to reach less than the introvert with AI, and the absolute trouble because the boss only control & instruct not lead even teach.

That's all just different human characters with AI, what if different human characters with AGI (Artificial General Intelligence) or even ASI (Artificial Super Intelligence)? Answer it with your empathy and intelligence.

Real threat of AI_:_ Recently, many people said, “ AI will replace humans ”.

My thought_:_ Humans aren’t replaced by AI, but humans will be replaced by those who use AI and those willing to learn future-ready skills.

AI only replaced those who are still unwilling to adapt, and learn. It replaces the stagnant. It replaces the arrogant.

Question:

Even machine learn, why do humans still doomscroll?

Decide now, lifelong learning or lifelong guilt?

The Logic: Psychology of F.O.B.O (Fear Of Becoming Obsolete)

Historically, and even now, humans have inner anxiety from within. Humans have constantly compared themselves to their nearest neighbors and asked, "Can I be better than them?" (Example: "Looks, our neighbors Morty became a High-Paid Cloud Consultant at IBM"). That is social comparison. Morty's Fortune or even Luck, or he studied smarter, which let him become a High-Paid Cloud Consultant at IBM.

This time, our goals have shifted. We are no longer comparing ourselves with others, but we are truly competing with technological advancements. This has evolved jealousy into a real danger*: F.O.B.O. (Fear Of Becoming Obsolete)*. Now humans need competence, future-ready skills, and always updated.

Dario Amodei , co-founder and chief executive officer of Anthropic, and Elon Musk are optimistic that AI will be smarter than the most intelligent humans by 2026. And OpenAI CEO Sam Altman published an essay saying that we might reach AGI in "a few thousand days*(!)*," suggesting sometime in the next decade.

SIMA (Scalable Intructable Multiworld Agent) 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds has proven that human almost reach even achieve AGI.

My thought : We might reach AGI between 2026–2035, possibly around 2027.

~ backed by data such as SIMA 2 capabilities , as we can see that SIMA 2 capabilities able to reach AGI soon.

Look at the benchmark below, which shows a 10% gap in "Task completion success rates" between Human and SIMA 2. It looks insane, but that's real.

Image by Google Deepmind

The Philosophy: Just in Case vs. Just in Time

Have you ever heard, seen or remember these phrase?

“It’s Now or Never”

This phrase is the title of a famous 1960 song by Elvis Presley. As we can see and understand from this phrase, we should take any decision or action immediately, because another opportunity may not come.

Nowadays, many people focus only on themselves and lack motivation to learn, whereas many people in the past were motivated to invent something new. That means, recent people are learn just in case.

People buy the book, just in case.
People learn language, just in case.
People learn the programming language, just in case.

In the past, all of this made sense due to a lack of resources. Information was very limited. Today, we need to change our mindset. We need to learn " just in time". Currently, a growth mindset is more valuable and necessary, regardless of how rich you are or who you are.

Humans are living and Natural Intelligence, which means they are the most advanced above Artificial Super Intelligence. Those who are still stuck in the past, with a fixed, mindless mindset and unwilling to learn new things, such as future-ready skills, are ready to be replaced by future technology, perhaps Artificial Super Intelligence.

True freedom is not about knowing everything. True freedom is about knowing that whatever happens, you have the Growth Mindset to learn the solution Just in Time.

The Solution: Empowering Google Skills

“ Facta sunt potentiora verbis ” (Deeds are more powerful than words).

As we can see, deeds or practices are more powerful than just a theory or words. Nowadays, there are too many learning platforms that only teach us the " words" (I mean Theory), even Albert Einstein said:

In theory, theory and practice are the same.

In practice, they are not. ― Albert Einstein

The antidote of F.O.B.O. isn't to stay & learn "Just in case", it is " Just in time".

There is a new way to learn, empowering Google Skills (which formerly was Google Cloud Skills Boost and before that, Qwiklabs). It is a comprehensive ecosystem containing nearly 3,000 courses, labs, and credentials in one platform — including much content from across Google Cloud , Google DeepMind , Grow with Google, and Google for Education into one essential platform.

Google Skills is a new home for those who want to become future-ready beings, always updated, and build and learning skills in AI, Cloud, etc. Build real-world skills through actual practice (Hands-on learning) that is more powerful than a lot of Theory, and then prove what you've learned with credentials like skill badges, completion, and entry-level certificates and certifications.

Ninety-six percent of decision makers plan to invest more time in hands-on labs. It offers help and extensive no-cost options for learners at every level. Learn now, you are eligible to get 35 credits free every month to enroll in hands-on labs on Google Skills at no cost.

The Result: New Freedom

What if you truly adopt the “Just in Time” philosophy?

You are ready to adapt to the era of Intelligence.

When you master the skill of retrieval and hands-on practice or learning, you realize something profound:

You don't need to own the information to use it.
You can close the 40 browser tabs you’ve been "saving for later." Close them.
You stop buying courses "just in case." Save your money. Save your life.

This isn’t about being lazy. It is about being ready. When you are free from the anxiety of what you should be learning, you actually create space for what you want to learn. You move from a state of reactive panic ("I must learn this to survive in the Era of Intelligence") to proactive curiosity ("I want to learn this because it fascinates me").

In the 21st century, the smartest person in the room is no longer those who are clever and have memorized all the answers. It is the one who really understands the question deeply enough to find the answer.

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Thank you so much for reading this article. I hope this article can give you a better understanding and vision of how to become a future-ready being, a lot of quotes, new knowledge about the reality, F.O.B.O. (Fear Of Being Obsolete), just in case vs. just in time, and empowering Google Skills as the solution.

References

Altman, S. (2024, September 23). The Intelligence Age. SamAltman.com. https://ia.samaltman.com/
DeepMind. (2025, November 13). SIMA 2: An Agent that Plays, Reasons, and Learns With You in Virtual 3D Worlds. Google DeepMind. https://deepmind.google/blog/sima-2-an-agent-that-plays-reasons-and-learns-with-you-in-virtual-3d-worlds/
Google Cloud. (2025). The Value of Cloud Skills: Why Hands-on Labs Matter. Google Cloud Skills Boost. https://www.skills.google/
McGinnis, P. J. (2004). Social Theory at HBS: McGinnis' Two FOs. The Harbus. https://patrickmcginnis.com/about-me/
Presley, E. (1960). It's Now or Never [Song]. On Elvis Is Back!. RCA Victor.